Software Engineer, Platform at Thinking Machines | San Francisco

Thinking Machines Lab aims to advance collaborative general intelligence, making AI accessible and adaptable for individuals and organizations. The team brings together scientists, engineers, and innovators behind well-known AI solutions, including ChatGPT, Character.ai, Mistral, and open-source projects like PyTorch, OpenAI Gym, Fairseq, and Segment Anythin…

About the Role and TeamAs a Research Engineer on the Post-Training team at Character.AI, you will immerse yourself in the dynamic field of fine-tuning AI models. Your primary focus will be on enhancing their performance and ensuring adherence to the highest standards of quality and efficiency. Your contributions will play a crucial role in our innovative advancements in AI technology, fostering an era where technology transcends mere tools and becomes a reliable companion in our everyday lives. Your creativity and expertise will serve as a driving force for transformative change in an AI-driven future.The Post-Training team specializes in evolving our robust pretrained language models into intelligent and engaging products.In your role as a Post-Training Researcher, you will collaborate with cross-functional teams and utilize our technical stack to enhance model performance and refine training methodologies, including data, compute, and algorithms. You will have the opportunity to shape the conversational experiences of millions of users daily.

About Applied IntuitionApplied Intuition, Inc. is at the forefront of advancing physical AI technology. Established in 2017 and now valued at a remarkable $15 billion, our Silicon Valley-based company is dedicated to developing the digital infrastructure necessary to infuse intelligence into every moving machine across the globe. We cater to various sectors, including automotive, defense, trucking, construction, mining, and agriculture, focusing on tools and infrastructure, operating systems, and autonomy. Our solutions are trusted by 18 of the top 20 global automakers and the United States military along with its allies. Our headquarters is in Sunnyvale, California, with additional offices in Washington, D.C.; San Diego; Ft. Walton Beach, Florida; Ann Arbor, Michigan; London; Stuttgart; Munich; Stockholm; Bangalore; Seoul; and Tokyo. Discover more at applied.co.As an in-office company, we expect our employees to primarily work from their Applied Intuition office five days a week. However, we value flexibility and trust our employees to manage their schedules responsibly, which may include occasional remote work, starting the day with morning meetings from home, or leaving early for family commitments.About the Role and TeamWe are seeking multiple enthusiastic Research Engineers to join our Research Group at Applied Intuition. The group's mission is to pioneer innovative technology that supports the next generation of physical AI, focusing particularly on the transformative applications of end-to-end autonomous driving and robotic generalist solutions. Our team comprises leading experts from top institutions and organizations, recognized for their outstanding academic and industry contributions, including eight Best Paper awards at prestigious conferences and journals such as CVPR and ICRA. To learn more, visit appliedintuition.com/research.With access to industry-leading tools and infrastructure, our researchers can utilize millions of miles of data from extensive fleets and implement their developed methods into various autonomous and robotic systems, including self-driving cars and trucks.

Reinforcement Learning EngineerJoin our innovative team at mlabs, a leading software development firm at the forefront of decentralized finance. We are dedicated to building the infrastructure for the world's largest crypto social networks and digital asset launchpads. Our mission-driven group of builders values speed, technical excellence, and exceptional talent.We are currently looking for a Reinforcement Learning (RL) Engineer who will be responsible for the end-to-end development of an RL-driven trading agent. This role involves managing real capital to enhance trading volume and user engagement within a dynamic memecoin ecosystem. The ideal candidate is an expert capable of integrating advanced modeling techniques with live financial operations. You will play a pivotal role in transitioning existing heuristic-based systems to learning-based methodologies while adhering to strict risk management protocols in a global 24/7 market.Key Responsibilities:Autonomous Agent Development: Lead the design, delivery, and refinement of an RL-driven trading agent to boost ecosystem engagement.Objective Function Design: Create reward functions and policies closely aligned with product objectives while implementing strict downside risk management.Validation Frameworks: Develop comprehensive evaluation and validation frameworks, including simulations and offline analyses, to minimize dependence on live testing.System Transition: Oversee the transition of existing heuristic-based systems to cutting-edge learning-based approaches safely.Technical Leadership: Act as the sole RL expert within a compact, high-performing team, responsible for the entire lifecycle from data modeling and deployment to monitoring and risk management.Interview Process:Recruiter / HR Call: Initial screening to discuss your professional background, risk management philosophy, and cultural fit.Technical Interview: In-depth assessment of RL architecture, simulation frameworks, and experience in live production.Final Interview: Strategic discussion with leadership focusing on alignment with our mission, role expectations, and long-term goals.

Anthropic is hiring a Research Engineer focused on Reinforcement Learning Infrastructure and Reliability. This role is based in San Francisco, CA. Role overview This position centers on building and maintaining systems essential to AI research. The work supports Anthropic’s reinforcement learning efforts, with an emphasis on infrastructure stability and performance. What you will do Collaborate with a team of specialists to develop and support key systems for AI research. Improve the reliability and efficiency of infrastructure supporting reinforcement learning projects. Apply technical expertise to advance Anthropic’s AI capabilities. Team environment Work alongside engineers and researchers dedicated to advancing AI reliability and performance. The team values collaboration and aims to enable new research while maintaining the stability of Anthropic’s core systems.

This is a hybrid position based in our Bay Area (SF or Palo Alto) or Chicago offices, requiring in-office attendance on Tuesdays and Thursdays.Why Join Us?At Grindr, we believe AI has the potential to transform the dating landscape. As a Staff MLOps Engineer, you will be pivotal in developing and managing the infrastructure, tools, and scalable systems that empower impactful AI initiatives. You will architect and maintain the platforms facilitating data ingestion, feature computation, model training, automated evaluation, deployment, and continuous monitoring for our machine learning teams that craft recommendations, LLM-powered experiences, advertisements, visual searches, as well as growth, trust, and safety mechanisms. Your role will involve designing foundational systems that enable our ML engineers to innovate swiftly, deploy models reliably, and manage them confidently in production.What You Will Do:We are seeking an exceptional MLOps engineer passionate about enabling ML at scale, with experience handling over 6 million daily active users and hundreds of millions of daily interactions. You will thrive in building robust, automated pipelines, developing reliable production training and inference systems, and establishing the infrastructure and processes that enhance ML product development across our organization.In this role, you will define and implement the strategy for Grindr’s ML platform and oversee the end-to-end model lifecycle.Key Responsibilities:Construct and oversee end-to-end ML pipelines for data ingestion, feature computation, model training, validation, deployment, and inference, all while managing substantial data scales.Establish and oversee a feature store, ensuring feature consistency, lineage, and reuse across teams.Utilize top-tier tools for managing deployment, scheduling, and environments within the specialized realm of ML Infrastructure.Develop automated model deployment workflows that incorporate CI/CD, secure rollout strategies, and reproducibility guarantees.Implement monitoring and observability solutions for ML systems, covering data quality checks, drift detection, performance metrics, and alerting mechanisms.Support and build training environments featuring experiment tracking, distributed training, hyperparameter tuning, and artifact and environment management.Collaborate with ML engineers and data engineers to streamline workflows, enhance model iteration speed, and enforce MLOps best practices.

Position OverviewJoin c-the-signs as a Senior MLOps Engineer, where your expertise in machine learning engineering will enable you to create and manage a cutting-edge production platform that drives ML and LLM-based healthcare workflows. You will be instrumental in designing systems that are reliable, secure, and compliant, facilitating model development, evaluation, deployment, monitoring, and ongoing enhancement. Collaborating closely with teams across ML, data science, security, and product development, you will ensure robust solutions are delivered.This opportunity is perfect for those who have successfully deployed ML systems in production environments and are passionate about LLM orchestration, RAG, evaluations, guardrails, and observability, particularly within regulated sectors.Key ResponsibilitiesMLOps & ML PlatformDesign and manage end-to-end ML platforms covering data ingestion, feature engineering, training, evaluation, deployment, and monitoring.Develop and uphold CI/CD processes for ML, including testing, packaging, versioning, reproducibility, automated rollbacks, and approvals.Adopt MLOps best practices, including model registry, experiment tracking, lineage, governance, and reproducible training environments.Create scalable training infrastructures with distributed training, GPU scheduling, cost controls, and auto-scaling functionalities.Establish and maintain feature pipelines and feature stores, ensuring alignment between training and inference to prevent training-serving skew.Implement comprehensive model monitoring and observability, covering performance metrics, drift detection, bias/fairness signals, latency, throughput, and data quality.Develop and manage LLM delivery pipelines, encompassing prompt/versioning, retrieval, orchestration, evaluation, deployment, monitoring, and iterative improvements.Create robust LLM evaluation frameworks that include offline and online components, automated regression testing, human-in-the-loop review workflows, and risk assessment.Establish cost management protocols focusing on token/cost budgeting, caching strategies, autoscaling, and performance optimization.Deployment, Reliability, and OperationsDeploy ML models on GCP utilizing containers and orchestration tools (e.g., GKE, Cloud Run) while building CI/CD for ML/LLM systems featuring automated tests and safe rollouts.Implement observability practices which include tracing, metrics, logs, dashboards, and alerts for model/system health metrics like latency, token usage, error rates, and drift indicators.Data, Governance, and Compliance (Healthcare)Design systems with a focus on security and privacy from the outset, incorporating IAM, least privilege, secrets management, audit logs, encryption, and PHI/PII handling protocols.Establish governance frameworks that cover model/prompt lineage, dataset provenance, evaluation traceability, and approval workflows in line with healthcare compliance standards.

Join fal in our pursuit to maintain a leading edge in model performance for generative media models. You'll be instrumental in designing and implementing innovative solutions for model serving architecture, built on our proprietary inference engine. Your focus will be on maximizing throughput while minimizing latency and resource consumption. In addition, you will create performance monitoring and profiling tools to identify bottlenecks and optimization opportunities. Collaborate closely with our Applied ML team and clients in the media sector to ensure their workloads leverage our accelerator effectively.

About NewsBreakEstablished in 2015, NewsBreak is a pioneering Content Intelligence platform that is redefining the content economy. With over 40 million monthly active users, our flagship platform provides highly personalized local news and information, driven by advanced AI technologies, recommendation systems, and advertising technology.As recognized by Fast Company as #32 on the Top Workplaces for Innovators, we take pride in being Great Place to Work® certified. Our dynamic team of technologists, product innovators, and business leaders is dedicated to addressing significant challenges at scale.Achieving unicorn status in 2021, we are committed to maintaining our high-growth trajectory and building the foundational infrastructure for content intelligence.If you’re driven to dream big, innovate quickly, and make a meaningful impact, we want to hear from you! For more details, visit www.newsbreak.com/aboutAbout the RoleWe are looking for an AI Development Engineer to join our AI and machine learning team. This role emphasizes multimodal machine learning modeling (audio, video, image, text) and the creation of AIGC systems and AI agents.Your responsibilities will involve translating cutting-edge research into scalable, real-world AI systems including virtual humans, text-to-image, and text-to-video generation. You will work closely with research, engineering, and product teams to propel next-generation multimodal and generative AI technologies forward.ResponsibilitiesDesign and develop multimodal ML models for understanding and generating audio, video, image, and text.Create AIGC systems, including virtual humans and text-to-image and text-to-video models.Develop AI agent systems encompassing reasoning, planning, tool usage, and multi-agent collaboration.Transform leading-edge research into scalable, production-ready AI systems.

Join the Center for AI Safety (CAIS), a premier research and advocacy institution dedicated to minimizing large-scale societal risks associated with artificial intelligence. We tackle the most pressing challenges in AI through innovative technical research, community-building initiatives, and active policy engagement, alongside our sister organization, the Center for AI Safety Action Fund.As a Research Engineer, you will operate at the forefront of advanced machine learning research and dependable engineering practices. Your role will involve designing and executing experiments on large language models, developing the necessary tools for extensive model training and evaluation, and translating findings into publishable research. You will work collaboratively with CAIS researchers and external academic and commercial partners, utilizing our compute cluster to conduct large-scale training and evaluations. Your work will focus on critical areas such as AI honesty, robustness, transparency, and the identification of trojan/backdoor behaviors, all aimed at mitigating real-world risks posed by sophisticated AI systems.

At Afresh, we are at the forefront of revolutionizing the fresh food industry through advanced AI solutions. Our partnerships with prominent grocers such as Albertsons, Wakefern, Meijer, and Stater Bros enable us to facilitate the ordering of billions of dollars in fresh food across more than 12,000 grocery departments nationwide.After witnessing a remarkable 70% growth in 2025, we have broadened our platform to encompass all fresh departments, launched our full store suite, and introduced DC Fresh Buying.Our mission is to eradicate food waste and ensure that fresh food is accessible to everyone. In 2025, our innovative software contributed to saving 200 million pounds of food waste. If you're searching for a role where your contributions lead to substantial impact and social good, now is the perfect time to join our team and help shape the future of fresh.The ML Platform Engineering team at Afresh is dedicated to constructing and maintaining the essential infrastructure and tools that drive our machine learning and applied science solutions. We provide the shared components and services that empower our teams to develop, deploy, and scale robust ML models. Our responsibilities include developing a high-performance data API, configurable featurization, dependable forecasting systems, highly parallel optimization engines, scalable training pipelines, and advanced experimentation capabilities. As our product offerings and customer base expand, the scale and complexity of our platform will also increase, seamlessly accommodating predictions and simulations across various timeframes (hours, days, weeks), intricate data hierarchies (pallets on a truck, shelves of mangos in a store, chunks of fruit in a bowl), and limitless configuration options (average shelf fullness, backroom loads, truck capacities).About the RoleAs a Senior ML Platform Engineer on the ML Platform Engineering team, you will play a crucial role in enhancing our core ML platform, elevating its performance, reliability, and scalability. You will be responsible for the critical infrastructure that allows all of Afresh's Machine Learning and Applied Science teams to innovate more rapidly and deliver meaningful impacts. Your contributions will support our product suite, including our flagship Prediction Engine, which influences replenishment decisions for over 15% of all produce sold in the United States.What You Will Do:In your first 3 months, you might deliver a project that helps generalize model configuration, enabling...

At GPTZero, we are committed to fostering trust and transparency across the internet. As a premier AI detection platform, we equip educators, students, journalists, marketers, and writers with the tools they need to navigate the rapidly changing world of AI-generated content. With a user base numbering in the millions, we are pioneering a category-defining company at the confluence of AI and information integrity.Our team is comprised of individuals from high-performance engineering backgrounds, including industry leaders like Meta, Perplexity, AWS, Affirm, and renowned AI research institutions such as Princeton, Caltech, and the Vector Institute.About this role:In this pivotal role, you will be instrumental in developing the next generation of AI tools designed to enhance and preserve critical thinking for humanity. The ideal candidate will be skilled in creating dependable AI agents and prompt engineering, possess a strong product sense, and demonstrate exceptional software engineering abilities. You will collaborate with a dynamic team of passionate innovators to create software that sets industry standards and has garnered millions of global users.

Waymo is at the forefront of autonomous driving technology, dedicated to becoming the world's most trusted driver. Originating from the Google Self-Driving Car Project in 2009, Waymo has tirelessly worked on developing the Waymo Driver—The World's Most Experienced Driver™—to enhance mobility access and save lives lost to traffic accidents. The Waymo Driver fuels our fully autonomous ride-hailing service and can adapt to various vehicle platforms and applications. With over ten million rider-only trips completed, Waymo has amassed more than 100 million miles driven autonomously on public roads, supplemented by tens of billions of simulated miles across 15+ U.S. states.The Perception team at Waymo is instrumental in crafting the technology that empowers the Waymo Driver. Our advanced models enable the Waymo Driver to interpret its surroundings, make informed decisions, and ensure safe passage for passengers. Engaging in research to tackle real-world challenges, we collaborate closely with Alphabet's research teams. Our access to extensive driving data from diverse sensors allows machine learning practitioners to develop scalable multi-modal models and techniques.You will report directly to our Director of Engineering within the Perception Organization.

About Liquid LabsAt Liquid AI, research has always been at the forefront of our mission. Liquid Labs serves as a dedicated internal research accelerator, facilitating groundbreaking advancements in the development of intelligent, personalized, and adaptive machines.Our roots extend back to MIT CSAIL, where pioneering work on Liquid Neural Networks established a new category of efficient sequence-processing architectures. This research laid the groundwork for our Liquid Foundation Models (LFMs), which are scalable, multimodal models designed for real-world applications in resource-constrained settings.In Liquid Labs, we continue this legacy by advancing the realm of efficient, adaptive intelligence through both fundamental research and practical engineering efforts.We collaborate closely with Liquid’s core foundation model and systems teams to turn theoretical concepts into deployable capabilities, setting the stage for a new era of powerful and efficient intelligent systems.About The Role:As a Research Engineer at Liquid Labs, you will be part of a dynamic, high-impact team pushing the boundaries of adaptive intelligence. You will be responsible for designing and implementing innovative architectures, training methodologies, and inference strategies to expand the potential of efficient AI.Your work will blend research and engineering, as you translate scientific concepts into functional systems, publish findings that advance the field, and deploy solutions that redefine what is achievable.While we prefer candidates from San Francisco and Boston, we welcome applications from other locations within the United States.

Join Reddit’s innovative Machine Learning Platform team as a Senior Engineering Manager for our Unified Embeddings Platform. You will lead a talented team that is responsible for developing and maintaining the foundational machine learning embeddings that drive our recommendations, content discovery, and user engagement across the platform. Your role will involve architecting and implementing cutting-edge solutions utilizing large scale graph neural networks and transformer-based models, empowering various product teams to enhance user experience and foster community engagement. Collaborate with cross-functional teams to optimize the machine learning feature software development lifecycle, ensuring that our technology remains at the forefront of the industry.

About Our TeamThe Frontier Evaluations team is dedicated to developing pioneering model assessments that propel advancements toward safe Artificial General Intelligence (AGI) and Artificial Superintelligence (ASI). This innovative group crafts ambitious evaluations to quantitatively assess and guide our models while establishing self-improvement cycles that influence our training, safety, and deployment strategies. Among our open-source evaluations are SWE-bench Verified, MLE-bench, PaperBench, and SWE-Lancer. The team has also executed frontier evaluations for significant models such as GPT4o, o1, o3, GPT 4.5, ChatGPT Agent, and GPT5. If you are passionate about being at the forefront of AI advancements and guiding their ethical development, this is the ideal team for you.About YouWe are on the lookout for exceptional research engineers who are eager to challenge the boundaries of frontier models in the finance sector. We seek individuals who will contribute to shaping AI evaluations focused on financial reasoning and associated competencies while managing distinct threads of this initiative from conception to execution.In This Role, You Will:Identify vital model capabilities, skills, and behaviors essential to financial operations, and develop methods to accurately measure performance in these areas.Take ownership of a research agenda aimed at uncovering significant model capabilities, particularly related to financial reasoning, and design evaluations to quantify them.Continuously enhance evaluations of frontier AI models to gauge the extent of cutting-edge capabilities.We Expect You To:Demonstrate a strong background in research engineering, particularly in AI and finance.Exhibit a collaborative spirit, working effectively within a cross-functional team environment.Showcase exceptional analytical and problem-solving skills.

About BlockitAt Blockit, we believe that time is the most precious resource. Yet, the process of coordinating it often feels antiquated. Our mission is to revolutionize scheduling with cutting-edge AI technology—creating an autonomous scheduling agent that expertly navigates the complexities of time zones, group coordination, and logistics, much like a tireless executive assistant.Unlike traditional LLM applications that focus on one-on-one interactions, Blockit is pioneering the development of stateful, multiplayer AI agents that can seamlessly communicate among multiple users, retain context across discussions, and take meaningful actions. As more users integrate their calendars, the effectiveness of our network grows exponentially.With backing from Sequoia, we are a dynamic and agile team that prioritizes innovation and quality. If you are looking to contribute to something truly transformative, we would be excited to connect with you.Explore our team page to discover more about our culture and values!The RoleIn this position, you will play a pivotal role in enhancing the intelligence of Blockit's scheduling agents, which autonomously manage meetings across various participants, time zones, and constraints.Your responsibilities will include designing and refining agent architectures, crafting and optimizing prompts, developing evaluation frameworks, and implementing new capabilities as models evolve. You will collaborate with our orchestrator agents to manage conversation flow and enhance specialized sub-agents, all aimed at making Blockit smarter, faster, and more adept at complex coordination tasks.This is an opportunity to architect and build real-world AI agents that will be deployed in production environments.What You’ll DoDevelop and optimize prompts across our agent ecosystem, including orchestrators, sub-agents, and tools.Create and maintain evaluation processes to assess agent performance and identify regressions.Troubleshoot agent issues: analyze interactions to understand misinterpretations or erroneous decisions.Incorporate new agent functionalities in response to user needs.Experiment with innovative architectures and techniques as our models advance.Monitor and analyze agent behavior to identify patterns and potential failure points.What We’re Looking ForA minimum of 2 years of experience in developing and managing production software.Proficiency in AI and machine learning frameworks.Strong problem-solving skills and the ability to work collaboratively in a fast-paced environment.

Join Trexquant Investment as an AI Engineer, where you will be at the forefront of developing, optimizing, and deploying state-of-the-art AI workflows and tools. This position provides a remarkable opportunity to engage with pioneering technologies that are instrumental in shaping our investment strategies.Key Responsibilities:Architect and implement systems that integrate large language models (LLMs), vector databases, and various AI tools tailored for quantitative research applications such as literature review, data retrieval, and code generation.Oversee the deployment, monitoring, and continuous enhancement of AI systems to ensure scalability and performance.Collaborate closely with quantitative researchers to design AI tools that effectively tackle specific market opportunities and challenges.Remain informed about the latest advancements in AI, contributing to our internal thought leadership and striving for innovation in our approaches.Facilitate the deployment of AI models in production environments, ensuring their smooth integration with existing infrastructure and real-time market data feeds.Assess potential risks associated with AI implementations and establish necessary safeguards, particularly concerning model bias and integrity.

At BeyondTrust, we empower you to realize your purpose through impactful work that contributes to a safer world via our cutting-edge cybersecurity SaaS solutions. Our commitment to flexibility, trust, and continuous learning ensures that your growth is acknowledged and your contributions to our success are celebrated. Join a community that challenges, supports, and inspires you to reach your full potential. The Role We are in search of a Senior Software Engineer with a focus on AI and Security to shape and construct the next wave of intelligent security systems. In this position, you will innovate platforms that leverage machine learning, behavioral analytics, and automation to identify threats, mitigate risks, and enhance the security framework of our organization. Your work will bridge the realms of security engineering, machine learning, and large-scale systems, transforming security data into actionable insights and automated defenses. The successful candidate will thrive in solving intricate challenges, manipulating extensive datasets, and developing production-grade systems to safeguard critical infrastructure. This position collaborates closely with teams across security engineering, data science, product engineering, and cloud platforms to deliver AI-enhanced capabilities that bolster detection, investigation, and response throughout the enterprise. You will leverage AI-assisted development as a fundamental aspect of software design, development, and delivery. What You’ll Do Build AI-Powered Security Systems Utilize tools such as GitHub Copilot and AI coding assistants to expedite the creation of secure applications and internal platforms. Develop effective prompts, workflows, and guardrails that empower engineers with security expertise to engage AI as an active collaborator in crafting functional, secure software. Implement AI-assisted development to streamline coding, testing, documentation, refactoring, and threat-aware design reviews. Deliver Applications to Production Transition applications from concept to production by applying robust engineering principles across architecture, deployment, observability, and reliability. Construct and manage CI/CD pipelines, containerized deployments, infrastructure as code, and secure release procedures. Ensure systems are production-ready through diligent monitoring, testing, operational readiness, and security-by-design practices. Uphold clear engineering and security principles, including least privilege, defense in depth, and scalable system architecture. Build Security Data Pipelines Develop and maintain data pipelines that support security analytics and reporting. Collaborate with data scientists to enhance data collection and processing methodologies. Ensure data integrity and accuracy through rigorous testing and validation processes.

About the RoleAt zero, we collaborate with innovative organizations within the Architecture, Engineering, and Construction (AEC) sector to revolutionize the design and delivery of the built environment. As a leading full-service management advisory firm dedicated solely to the built world, we merge specialized knowledge with advanced technology strategies to address significant challenges on a large scale.We are currently on the lookout for a Senior AI Engineer to spearhead the creation of automated building design systems and elevate the frontier of AI/ML applications in the built environment. In this role, you will design and execute generative design algorithms, computer vision systems, and optimization models that will transform the conception, design, and validation of buildings.This position merges deep learning, computational geometry, and architectural design, crafting AI systems that enhance human creativity while adhering to engineering constraints and building regulations.This is an extraordinary chance to leverage cutting-edge AI research to tackle one of humanity's core challenges: creating improved spaces for individuals to live, work, and flourish.