AnthropicSan Francisco, CA | New York City, NY | Seattle, WA
On-site Full-time
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Qualifications
Ideal candidates will have over 5 years of experience in building and managing large-scale backend infrastructure. You should possess a deep understanding of distributed systems design and implementation, alongside strong operational skills for debugging complex production challenges. Proficiency with cloud platforms like GCP/GCS is essential, and experience with AWS or Azure is a plus. Familiarity with containerization technologies such as Docker and Kubernetes, along with a solid grasp of their security implications, is required. You should be adept at using infrastructure as code practices and have a collaborative mindset to work effectively with research teams and security partners.
About the job
Join Anthropic as an Infrastructure Engineer on our Sandboxing team, where you'll play a pivotal role in building and scaling secure execution environments for AI research. Your expertise will ensure that researchers can safely experiment with AI-generated code in isolated settings. As our models advance, the infrastructure that supports these environments becomes increasingly vital. Your contributions will help maintain security and reliability at scale, directly aligning with our mission to develop trustworthy and beneficial AI systems.
About Anthropic
At Anthropic, we are dedicated to creating AI systems that are reliable, interpretable, and steerable. Our mission is to ensure that AI is not only safe but also beneficial for users and society at large. As a rapidly growing team of researchers, engineers, policy experts, and business leaders, we are committed to developing AI solutions that prioritize safety and trustworthiness. Join us in shaping the future of AI.
OpenAI's research infrastructure group creates and maintains the backbone systems for advanced machine learning model training. This team often goes beyond conventional training methods, developing new infrastructure to support novel research at scale. Their work closely connects systems engineering with research progress, making it possible to run experimen…
Full-time|$350K/yr - $475K/yr|On-site|San Francisco
At Thinking Machines Lab, our mission is to empower humanity by advancing collaborative general intelligence. We envision a future where everyone has access to the knowledge and tools necessary to harness AI for their unique needs and goals.Our team comprises scientists, engineers, and builders who have developed some of the most widely utilized AI products, such as ChatGPT and Character.ai, alongside open-weight models like Mistral, and popular open-source initiatives like PyTorch, OpenAI Gym, Fairseq, and Segment Anything.About the PositionWe are seeking an Infrastructure Research Engineer to design and construct the foundational systems that facilitate the scalable and efficient training of large models for both deployment and research purposes. Your primary objective will be to streamline experimentation and training at Thinking Machines, enabling our research teams to concentrate on scientific advancements rather than system limitations.This role is a perfect match for an individual who possesses a strong blend of deep systems expertise and a keen interest in machine learning at scale. You will take full ownership of the training stack, ensuring that every GPU cycle contributes to scientific progress.Note: This is an evergreen role that we keep open continuously to express interest. We receive numerous applications, and there may not always be an immediate role that aligns perfectly with your experience and skills. However, we encourage you to apply. We regularly review applications and reach out to candidates as new opportunities arise. Feel free to reapply as you gain more experience, but please avoid applying more than once every six months. We may also post specific roles for individual projects or team needs, in which case you are welcome to apply directly alongside this evergreen role.Key ResponsibilitiesDesign, implement, and optimize distributed training systems that scale across thousands of GPUs and nodes for extensive training workloads.Develop high-performance optimizations to maximize throughput and efficiency.Create reusable frameworks and libraries that enhance training reproducibility, reliability, and scalability for new model architectures.Establish standards for reliability, maintainability, and security, ensuring systems remain robust under rapid iterations.Collaborate with researchers and engineers to construct scalable infrastructure.Publish and disseminate findings through internal documentation, open-source libraries, or technical reports that contribute to the advancement of scalable AI infrastructure.
Full-time|$350K/yr - $475K/yr|On-site|San Francisco
At Thinking Machines Lab, our mission is to empower humanity by advancing collaborative general intelligence. We're dedicated to crafting a future where everyone can harness the power of AI to meet their unique needs and aspirations.Our team comprises scientists, engineers, and innovators who have developed some of the most widely utilized AI products, including ChatGPT and Character.ai, as well as open-weight models like Mistral, in addition to renowned open-source projects such as PyTorch, OpenAI Gym, Fairseq, and Segment Anything.About the RoleWe are seeking a talented Infrastructure Research Engineer to architect and develop the foundational systems that facilitate the scalable and efficient training of large models using reinforcement learning.This position exists at the crossroads of research and large-scale systems engineering, requiring a professional who not only comprehends the algorithms behind reinforcement learning but also appreciates the practicalities of distributed training and inference at scale. You will have a diverse set of responsibilities, from optimizing rollout and reward pipelines to enhancing the reliability, observability, and orchestration of systems. Collaboration with researchers and infrastructure teams will be essential to ensure reinforcement learning is stable, rapid, and production-ready.Note: This is an evergreen role that we maintain on an ongoing basis to express interest. Due to the high volume of applications we receive, there may not always be an immediate position that aligns perfectly with your skills and experience. We encourage you to apply, as we continuously review applications and reach out to candidates when new opportunities arise. You may reapply after gaining more experience, but please refrain from applying more than once every six months. Additionally, you may notice postings for specific roles that cater to unique project or team needs; in those circumstances, you are welcome to apply directly alongside this evergreen role.What You’ll DoDesign, implement, and optimize the infrastructure that supports large-scale reinforcement learning and post-training workloads.Enhance the reliability and scalability of the RL training pipeline, including distributed RL workloads and training throughput.Create shared monitoring and observability tools to ensure high uptime, debuggability, and reproducibility of RL systems.Work closely with researchers to translate algorithmic concepts into production-quality training pipelines.Develop evaluation and benchmarking infrastructure to assess model performance based on helpfulness, safety, and factual accuracy.Publish and disseminate insights through internal documentation, open-source libraries, or technical reports that contribute to the advancement of scalable AI infrastructure.
Join our dynamic team at Cognition as a Research Engineer specializing in Infrastructure. In this role, you will be at the forefront of cutting-edge research, contributing to innovative solutions that shape the future of our infrastructure projects.Your responsibilities will include conducting thorough research, analyzing data, and collaborating with cross-functional teams to implement effective strategies. We are looking for an individual who is passionate about technology and infrastructure, eager to solve complex problems, and ready to drive impactful results.
About Our Innovative TeamJoin the Workload team at OpenAI, where we are at the forefront of designing and managing the cutting-edge infrastructure that drives the training and inference of large language models (LLMs) at an unprecedented scale. Our systems are engineered to harmonize the complex processes of model training and serving, abstracting performance, parallelism, and execution across extensive GPU and accelerator networks. This robust foundation allows researchers to concentrate on elevating model capabilities, while we take care of the scalability, efficiency, and reliability needed to bring these advanced models to life.Your Role and ResponsibilitiesWe are seeking a talented engineer to design and implement the dataset infrastructure that will fuel OpenAI’s next-generation training stack. Your primary focus will be on creating standardized dataset interfaces, scaling pipelines across thousands of GPUs, and proactively identifying and addressing performance bottlenecks. Collaboration with multimodal researchers and infrastructure teams will be key to ensuring that our datasets are unified, efficient, and user-friendly.Key Responsibilities Include:Design and maintain standardized dataset APIs, including those for multimodal (MM) data that exceeds memory capacity.Develop proactive testing and validation pipelines for dataset loading at GPU scale.Work collaboratively to integrate datasets into training and inference pipelines, ensuring seamless user experiences.Document and maintain dataset interfaces to ensure they are discoverable, consistent, and easily adoptable by other teams.Establish validation systems to assure datasets remain reproducible and unchanged once standardized.Identify and troubleshoot performance bottlenecks in distributed dataset loading, such as stragglers impacting global training speed.Create visualization and inspection tools to highlight errors, bugs, or bottlenecks in datasets.Ideal Candidate ProfilePossess strong engineering fundamentals and experience in distributed systems, data pipelines, or infrastructure.Have a proven track record in building APIs, modular code, and scalable abstractions, with a user-centric approach to design.Be adept at debugging performance issues across large-scale machine fleets.Demonstrate a passion for advancing data infrastructure to enhance research capabilities.
OverviewPluralis Research is at the forefront of innovation in Protocol Learning, specializing in the collaborative training of foundational models. Our approach ensures that no single participant ever has or can obtain a complete version of the model. This initiative aims to create community-driven, collectively owned frontier models that operate on self-sustaining economic principles.We are seeking experienced Senior or Staff Machine Learning Engineers with over 5 years of expertise in distributed systems and large-scale machine learning training. In this role, you will design and implement a groundbreaking substrate for training distributed ML models that function effectively over consumer-grade internet connections.
OpenAI is hiring a Software Engineer for Post-Training Research in San Francisco. This position centers on improving the performance and capabilities of advanced machine learning models after their initial training phase. Role overview Work closely with a skilled team to explore new ways of strengthening AI systems. The focus is on researching and developing methods that push the boundaries of what these models can achieve once training is complete. Collaboration Expect to contribute to ongoing research efforts and share insights with colleagues who are passionate about advancing AI. Teamwork and knowledge exchange are key parts of this role. Location This position is based in San Francisco.
Full-time|$350K/yr - $475K/yr|On-site|San Francisco
At Thinking Machines Lab, we are on a mission to empower humanity by advancing collaborative general intelligence. Our vision is to create a future where everyone has access to the knowledge and tools necessary to harness AI for their unique needs and objectives.We are a diverse team of scientists, engineers, and builders responsible for developing some of the most influential AI products on the market, such as ChatGPT and Character.ai. Our contributions extend to open-weight models like Mistral and popular open-source projects including PyTorch, OpenAI Gym, Fairseq, and Segment Anything.About the RoleWe are seeking talented engineers to join our team and develop the libraries and tools that will accelerate research efforts at Thinking Machines. You will take charge of our internal infrastructure—creating evaluation libraries, reinforcement learning training libraries, and experiment tracking platforms—while building systems that enhance research velocity over time.This position emphasizes collaboration. You will work closely with researchers to identify bottlenecks and pain points, ensuring that they trust your systems to function seamlessly and find them enjoyable to use.What You'll DoDesign, build, and manage research infrastructure, including evaluation frameworks, RL training systems, experiment tracking platforms, visualization tools, and shared utilities.Develop high-throughput, scalable pipelines for distributed evaluation, reward modeling, and multimodal assessment.Establish systems for reproducibility, traceability, and robust quality control across research experiments and model training runs, implementing effective monitoring and observability.Collaborate directly with researchers to identify bottlenecks and unlock new capabilities, managing research tools like a product manager by proactively seeking feedback and tracking adoption.Work alongside infrastructure, data, and product teams to integrate tools across the technical stack.
OverviewPluralis Research is at the forefront of Protocol Learning, innovating a decentralized approach to train and deploy AI models that democratizes access beyond just well-funded corporations. By aggregating computational resources from diverse participants, we incentivize collaboration while safeguarding against centralized control of model weights, paving the way for a truly open and cooperative environment for advanced AI.We are seeking a talented Machine Learning Training Platform Engineer to design, develop, and scale the core infrastructure that powers our decentralized ML training platform. In this role, you will have ownership over essential systems including infrastructure orchestration, distributed computing, and service integration, facilitating ongoing experimentation and large-scale model training.ResponsibilitiesMulti-Cloud Infrastructure: Create resource management systems that provision and orchestrate computing resources across AWS, GCP, and Azure using infrastructure-as-code tools like Pulumi or Terraform. Manage dynamic scaling, state synchronization, and concurrent operations across hundreds of diverse nodes.Distributed Training Systems: Design fault-tolerant infrastructure for distributed machine learning, including GPU clusters, NVIDIA runtime, S3 checkpointing, large dataset management and streaming, health monitoring, and resilient retry strategies.Real-World Networking: Develop systems that simulate and manage real-world network conditions—such as bandwidth shaping, latency injection, and packet loss—while accommodating dynamic node churn and ensuring efficient data flow across workers with varying connectivity, as our training occurs on consumer nodes and non-co-located infrastructure.
Full-time|$350K/yr - $475K/yr|On-site|San Francisco
At Thinking Machines Lab, we are dedicated to empowering humanity by advancing collaborative general intelligence. Our vision is to create a future where everyone can leverage AI to meet their unique needs and aspirations.Our talented team comprises scientists, engineers, and innovators who have developed some of the most widely recognized AI products, including ChatGPT and Character.ai, alongside open-weight models like Mistral and popular open-source projects such as PyTorch, OpenAI Gym, Fairseq, and Segment Anything.About the PositionWe are seeking a motivated Infrastructure Research Engineer to design, enhance, and scale the systems that underpin large AI models. Your contributions will significantly improve inference speed, cost-effectiveness, reliability, and reproducibility, allowing our teams to concentrate on enhancing model capabilities rather than dealing with bottlenecks.Our mission centers on delivering high-performance and efficient model inference to support real-world applications and accelerate research efforts. In this role, you will be responsible for the infrastructure that guarantees smooth operation for every experiment, evaluation, and deployment at scale.Note: This is an evergreen role, kept open continuously to express interest. We receive numerous applications and may not always have an immediate opening that aligns perfectly with your skills and experience. However, we encourage you to apply. We regularly review applications and reach out to candidates as new opportunities arise. Feel free to reapply as you gain more experience, but we kindly ask that you avoid applying more than once every six months. You may also notice postings for specific roles related to particular projects or teams, in which case you are welcome to apply directly in addition to this evergreen role.What You Will DoCollaborate with researchers and engineers to transition cutting-edge AI models into production.Partner with research teams to ensure high-performance inference for innovative architectures.Design and implement new techniques, tools, and architectures that enhance performance, latency, throughput, and efficiency.Optimize our codebase and computing resources (e.g., GPUs) to maximize hardware FLOPs, bandwidth, and memory usage.Extend orchestration frameworks (e.g., Kubernetes, Ray, SLURM) for distributed inference, evaluation, and large-batch serving.Establish standards for reliability, observability, and reproducibility throughout the inference stack.Publish and share insights through internal documentation, open-source libraries, or technical reports that further the field of scalable AI infrastructure.
Join Baseten as a Post-Training Research Engineer and contribute to groundbreaking advancements in machine learning and AI. In this role, you will leverage your engineering skills to analyze and enhance models post-training, ensuring optimal performance and efficiency.
Join Our Innovative TeamAt OpenAI, our Training team is at the forefront of developing advanced language models that drive our research and products, getting us closer to achieving Artificial General Intelligence (AGI). This mission demands a blend of cutting-edge research to enhance our architecture, datasets, and optimization methods, alongside strategic long-term initiatives that boost the efficiency and capabilities of future models. We ensure that our models, including recent breakthroughs like GPT-4-Turbo and GPT-4o, adhere to the highest standards of excellence.Your RoleAs an integral member of our architecture team, you will spearhead architectural advancements for OpenAI’s leading models, enhancing their intelligence and efficiency while introducing novel capabilities. Your expertise in large language model (LLM) architectures and model inference will be crucial as you adopt a hands-on, empirical approach to problem-solving. Whether brainstorming creative breakthroughs, refining foundational systems, designing evaluations, or diagnosing performance issues, your diverse skill set will be invaluable.This position is located in San Francisco, where we embrace a hybrid work environment of three days in the office each week, and we provide relocation support for new hires.Your Key Responsibilities:Innovate, prototype, and upscale new architectures to elevate model intelligence.Conduct and evaluate experiments both independently and collaboratively.Analyze, debug, and enhance both model performance and computational efficiency.Contribute to the development of training and inference infrastructure.Who You Are:You possess experience with significant contributions to major LLM training projects.You excel at independently evaluating and enhancing deep learning architectures.You are driven to responsibly implement LLMs in real-world applications.You are knowledgeable about state-of-the-art transformer modifications aimed at improving efficiency.About OpenAIOpenAI is a pioneering AI research and deployment organization committed to ensuring that artificial general intelligence benefits humanity. We focus on developing safe and effective AI technologies that empower individuals and organizations across the globe.
WHO WE AREAt Applied Compute, we specialize in creating Specific Intelligence for enterprises—agents that continually learn from a company's processes, data, expertise, and goals. Our mission is to develop a continual learning layer and platform that captures context, memory, and decision traces across organizations, fostering an environment where specialized agents perform real work effectively.Why Join Us: We operate at a unique intersection of product development and advanced research. Our product team is building the platform for a new generation of digital coworkers, while our research team is pioneering advancements in post-training and reinforcement learning to enrich product experiences. Our applied research engineers collaborate closely with customers, deploying agents into production seamlessly. This blend of robust product focus, in-depth research, and real-world application is our approach to integrating AI into enterprises. We pride ourselves on being product-led, research-enabled, and forward-deployed.Our Team: We are a diverse group of engineers, researchers, and operators, many of whom are former founders with experience in RL infrastructure at OpenAI, data foundations at Scale AI, and various systems across renowned firms like Two Sigma and Watershed. We collaborate with Fortune 50 clients and are proudly backed by reputable investors including Kleiner Perkins, Benchmark, Sequoia, Lux, and Greenoaks.Who Thrives Here: We seek individuals passionate about applying innovative research and complex systems to solve real-world challenges. You should be adept at navigating new environments swiftly, whether it's a fresh codebase, a customer's data architecture, or an unfamiliar problem domain. Our team values collaboration with customers, emphasizing active listening and understanding their workflows. We find that former founders, individuals with extensive side projects, and those who demonstrate end-to-end ownership excel in our culture.THE ROLEIn the role of Research Systems Engineer, you will train frontier-scale models and devise methodologies to implement continual learning in enterprise settings. Your responsibilities will include designing and executing large-scale experiments, investigating cutting-edge reinforcement learning techniques, and developing tools to gain insights into training processes. This position lies at the crossroads of research and systems engineering, where you will innovate algorithms alongside researchers and collaborate with infrastructure engineers to implement them on GPUs.
Full-time|$252K/yr - $315K/yr|On-site|San Francisco, CA; Seattle, WA; New York, NY
At Scale AI, we collaborate with leading AI laboratories to supply high-quality data and foster advancements in Generative AI research. We seek innovative Research Scientists and Research Engineers with a strong focus on post-training techniques for Large Language Models (LLMs), including Supervised Fine-Tuning (SFT), Reinforcement Learning from Human Feedback (RLHF), and reward modeling. This position emphasizes optimizing data curation and evaluation processes to boost LLM performance across text and multimodal formats. In this pivotal role, you will pioneer new methods to enhance the alignment and generalization of extensive generative models. You will work closely with fellow researchers and engineers to establish best practices in data-driven AI development. Additionally, you will collaborate with top foundation model labs, providing critical technical and strategic insights for the evolution of next-generation generative AI models.
About UsAt Applied Compute, we are pioneering Specific Intelligence for enterprises through advanced AI agents that learn continuously from organizational processes, data, and objectives. We recognize the significant gap between what AI models can achieve in isolation and their performance within actual business contexts, often failing to adapt to feedback. Our mission is to build a continual learning layer that captures context, memory, and decision traces across enterprises, creating environments where specialized agents excel at real tasks.Why Join Us? We operate at a unique intersection of product development and research. Our product team is developing the platform that empowers a new generation of digital coworkers, while our research team is advancing post-training and reinforcement learning to enhance product experiences. As applied research engineers, we work closely with customers to deploy models into production effectively. This blend of robust product focus, deep research, and customer engagement is our strategy for successfully integrating AI into enterprise operations. We are product-led, research-enabled, and strategically deployed.Meet Our Team: Our team consists of engineers, researchers, and operators, many of whom are former founders. We have established RL infrastructure at OpenAI, developed data foundations at Scale AI, and built systems at Together, Two Sigma, and Watershed. We collaborate with Fortune 50 clients, including DoorDash, Mercor, and Cognition, and are backed by esteemed investors such as Benchmark, Sequoia, and Lux.Who Excels Here: We seek individuals passionate about applying innovative research and complex systems to overcome real-world challenges. Candidates should thrive in unfamiliar environments, whether it involves navigating new codebases, understanding new customer data architectures, or tackling unfamiliar problem domains. A genuine enjoyment of customer interactions—listening, empathizing, and comprehending how work is accomplished within organizations—is essential. Those with prior entrepreneurial experience, extensive side projects, or a proven ability to manage initiatives from start to finish will thrive in our culture.Your RoleAs a Research Systems Engineer, you will be responsible for training cutting-edge models and developing methodologies that facilitate continual learning within enterprise settings. You will design and execute large-scale experiments, delve into advanced reinforcement learning techniques, and create tools that enhance our understanding of the training process. This role uniquely positions you at the crossroads of research and systems engineering, where you will innovate new algorithms in collaboration with researchers and work alongside infrastructure engineers to deploy them on GPUs.
Team and Platform Focus The Compute Infrastructure team at OpenAI designs, builds, and maintains the systems that support AI research at scale. This work brings together accelerators, CPUs, networking, storage, data centers, orchestration software, agent infrastructure, developer tools, and observability. The aim is to create a reliable, unified experience for researchers and product teams across the company. Projects span the full stack: capacity planning, cluster lifecycle management, bare-metal automation, and distributed systems. The team manages Kubernetes scheduling, system optimization, high-performance networking, storage, fleet health, reliability, workload profiling, benchmarking, and improvements to the developer experience. Even small improvements in communication, scheduling, hardware efficiency, or debugging can significantly accelerate research. OpenAI matches engineers to areas within Compute Infrastructure that align with their skills and interests. Role Overview This Software Engineer role centers on building and evolving the compute platform that supports OpenAI’s research and products. Candidates may bring expertise in low-level systems, high-performance computing, distributed infrastructure, reliability, CaaS, agent infrastructure, developer platforms, tooling, or infrastructure user experience. The most important qualities are strong analytical skills, the ability to write resilient code, and a collaborative approach that helps colleagues move faster and with more confidence. What You Will Work On Working close to hardware or at the user interaction layer Developing CaaS and agent infrastructure Managing control and data planes that connect the system Bringing new supercomputing capabilities online Optimizing training workloads through profiler traces and benchmarks Improving NCCL and collective communication Analyzing GPUs, NICs, topology, firmware, thermal dynamics, and failure modes Designing abstractions to unify diverse clusters into a single platform Areas of Expertise No one is expected to cover every area listed. Some engineers focus on system performance, kernel or runtime behavior, large-scale networking protocols, RDMA, NCCL, GPU hardware, benchmarking, scheduling, or hardware reliability. Others improve the platform’s usability through APIs, tools, workflows, and developer experience. The team values strong engineering judgment and a drive to advance the field.
On-site|On-site|San Francisco, CA | New York City, NY | Seattle, WA
Join Anthropic as an Infrastructure Engineer on our Sandboxing team, where you'll play a pivotal role in building and scaling secure execution environments for AI research. Your expertise will ensure that researchers can safely experiment with AI-generated code in isolated settings. As our models advance, the infrastructure that supports these environments becomes increasingly vital. Your contributions will help maintain security and reliability at scale, directly aligning with our mission to develop trustworthy and beneficial AI systems.
Full-time|$350K/yr - $475K/yr|On-site|San Francisco
At Thinking Machines Lab, our mission is to empower humanity by advancing collaborative general intelligence. We envision a future where everyone has access to the knowledge and tools necessary to make AI work for their individual needs and goals. Our team comprises scientists, engineers, and innovators who have developed some of the most widely adopted AI products, including ChatGPT and Character.ai, alongside open-weight models like Mistral, as well as popular open-source initiatives such as PyTorch, OpenAI Gym, Fairseq, and Segment Anything.About the RoleWe are seeking a highly skilled infrastructure research engineer to architect and develop core systems that facilitate efficient large-scale model training, with a strong emphasis on numerics. You will enhance the numerical foundations of our distributed training stack, focusing on precision formats, kernel optimizations, and communication frameworks to ensure that training trillion-parameter models is stable, scalable, and fast.This position is perfect for an individual who excels at the intersection of research and systems engineering—a creator who comprehends both the mathematics of optimization and the practicalities of distributed computing.Note: This is an "evergreen role" that remains open for ongoing expressions of interest. While we receive numerous applications and there may not always be an immediate opening that perfectly matches your skills and experience, we encourage you to apply. We continuously review applications and will contact applicants as new opportunities arise. You are welcome to reapply if you gain additional experience, but please refrain from applying more than once every six months. You may also notice postings for specific roles related to particular projects or teams; in those instances, you are welcome to apply for those positions in addition to the evergreen role.What You’ll DoDesign and optimize distributed training infrastructure for large-scale LLMs, ensuring performance, stability, and reproducibility in multi-GPU and multi-node environments.Implement and assess low-precision numerics (e.g., BF16, MXFP8, NVFP4) to enhance efficiency while maintaining model quality.Develop kernels and communication primitives that leverage hardware-level support for mixed and low-precision arithmetic.Collaborate with research teams to co-design model architectures and training methodologies that align with new numeric formats and stability requirements.Prototype and benchmark scaling strategies, including data, tensor, and pipeline parallelism that integrate precision-adaptive computation and quantized communication.Contribute to the design of our internal orchestration and monitoring frameworks.
About the TeamJoin the innovative Post-Training team at OpenAI, where we focus on refining and elevating pre-trained models for deployment in ChatGPT, our API, and future products. Collaborating closely with various research and product teams, we conduct crucial research that prepares our models for real-world deployment to millions of users, ensuring they are safe, efficient, and reliable.About the RoleAs a Research Engineer / Scientist, you will spearhead the research and development of enhancements to our models. Our work intersects reinforcement learning and product development, aiming to create cutting-edge solutions.We seek passionate individuals with robust machine learning engineering skills and research experience, particularly with innovative and powerful models. The ideal candidate will be driven by a commitment to product-oriented research.This position is located in San Francisco, CA, and follows a hybrid work model requiring three days in the office each week. Relocation assistance is available for new employees.In this role, you will:Lead and execute a research agenda aimed at enhancing model capabilities and performance.Work collaboratively with research and product teams to empower customers to optimize their models.Develop robust evaluation frameworks to monitor and assess modeling advancements.Design, implement, test, and debug code across our research stack.You may excel in this role if you:Possess a deep understanding of machine learning and its applications.Have experience with relevant models and methodologies for evaluating model improvements.Are adept at navigating large ML codebases for debugging purposes.Thrive in a fast-paced and technically intricate environment.About OpenAIOpenAI is a pioneering AI research and deployment organization dedicated to ensuring that general-purpose artificial intelligence benefits all of humanity. We are committed to pushing the boundaries of AI capabilities while prioritizing safety and human-centric values in our products. Our mission is to embrace diverse perspectives, voices, and experiences that represent the full spectrum of humanity, as we strive for a future where AI is a powerful ally for everyone.
About Our TeamThe Alignment Training team at OpenAI focuses on understanding how advanced models develop lasting behavioral patterns throughout the training process. We investigate which behaviors can be influenced during the pre-training, mid-training, and post-training phases; create the necessary data, objectives, and evaluations to guide these behaviors; and assess whether the resulting actions represent a general capability or a byproduct of the training environment.Our research encompasses synthetic data development, various training stages, model behavior analysis, and performance evaluation. We explore how models grasp user intentions, adhere to instructions, reason effectively, demonstrate honesty, and maintain reliability in novel situations. Our ultimate aim is to foster desirable behaviors early in training, reinforce them throughout, and ensure their consistency in real-world applications.About This PositionWe are seeking a seasoned researcher with profound expertise in large-scale model training, synthetic data creation, or evaluation processes, who is passionate about exploring how training decisions influence aligned behaviors in state-of-the-art models.In this role, you will define the research agenda for alignment training: outlining the behaviors we aspire for models to acquire, designing data and training strategies to cultivate them, and developing evaluation mechanisms to verify the breadth, strength, and durability of those behaviors. The ideal candidate will excel at translating vague behavioral inquiries into structured experimental plans: devising hypotheses, creating interventions, establishing pipelines, conducting experiments, and analyzing results for authenticity.This position is particularly suited for individuals eager to engage closely with the core model training framework, where decisions regarding data, objectives, and evaluations critically influence the alignment of deployed systems.Key Responsibilities:Innovate synthetic data methods that instill higher-level behavioral tendencies in models, such as comprehending user intent, consistently following instructions, clear reasoning, honesty, and alignment with defined goals and constraints.Analyze the impact of pre-training, mid-training, and post-training on subsequent model behavior, identifying the most effective interventions for each phase.Develop evaluation loops that link model behavior back to training data and objectives, enabling quicker iterations and clearer feedback.
