Machine Learning Research Engineer - ML Systems

Scale AI, Inc.San Francisco, CA; Seattle, WA; New York, NY

On-site Full-time $218.4K/yr - $273K/yr

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Key Responsibilities:Design, profile, and enhance our training and inference framework. Work collaboratively with ML teams to expedite their research and development processes, empowering them to create next-generation models and data curation strategies. Investigate and incorporate cutting-edge technologies to refine our ML system. Preferred Qualifications:A strong enthusiasm for system optimization. Hands-on experience with multi-node LLM training and inference. Proven experience in developing large-scale distributed ML systems. Robust software engineering capabilities, with proficiency in frameworks and tools like CUDA, PyTorch, Transformers, Flash Attention, etc. Excellent written and verbal communication skills with the ability to thrive in a cross-functional team environment. Desirable Skills:Demonstrated expertise in post-training methodologies and/or innovative use cases for large language models, including instruction tuning, RLHF (Reinforcement Learning from Human Feedback), tool usage, reasoning, agents, and multimodal applications.

About the job

At Scale, we occupy a unique position in the AI landscape, serving as an essential provider of training and evaluation data along with comprehensive solutions for the entire ML lifecycle. You will collaborate closely with Scale's ML teams and researchers to enhance the foundational platform that underpins our ML research and development initiatives. Your contributions will be crucial in optimizing the platform to support the next generation of LLM training, inference, and data curation.

If you are passionate about driving the future of AI through groundbreaking innovations, we want to hear from you!

About Scale AI, Inc.

Scale AI is a leader in the AI sector, providing indispensable training and evaluation data as well as comprehensive end-to-end solutions for the machine learning lifecycle. Our platform empowers researchers and engineers to push the boundaries of AI technology.

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ML/AI Research Engineer - Founding Team at Agentic AI Lab

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ML/AI Research Engineer - Founding Team at Agentic AI LabLocation: San Francisco Bay AreaType: Full-TimeCompensation: Competitive salary + meaningful equity (founding tier)At fabrion, backed by 8VC, we are assembling a top-tier team dedicated to addressing one of the most pressing infrastructure challenges in the industry.About the RoleJoin us in shaping the future of enterprise AI infrastructure, focusing on agents, retrieval-augmented generation (RAG), knowledge graphs, and multi-tenant governance.As an ML/AI Research Engineer, you will spearhead the design, training, evaluation, and optimization of agent-native AI models. Your work will integrate LLMs, vector search, graph reasoning, and reinforcement learning, establishing the intelligence layer for our enterprise data fabric.This role goes beyond prompt engineering; it encompasses the entire ML lifecycle—from data curation and fine-tuning to thorough evaluation, interpretability, and deployment, all while considering cost-effectiveness, alignment, and agent coordination.Core ResponsibilitiesFine-tune and assess open-source LLMs (e.g., LLaMA 3, Mistral, Falcon, Mixtral) for enterprise applications, leveraging both structured and unstructured data.Construct and enhance RAG pipelines utilizing LangChain, LangGraph, LlamaIndex, or Dust, integrating with our vector databases and internal knowledge graphs.Train agent architectures (ReAct, AutoGPT, BabyAGI, OpenAgents) using enterprise task datasets.Develop embedding-based memory and retrieval chains employing token-efficient chunking strategies.Create reinforcement learning pipelines to enhance agent behaviors (e.g., RLHF, DPO, PPO).Establish scalable evaluation harnesses for LLM and agent performance, including synthetic evaluations, trace capture, and explainability tools.Contribute to model observability, drift detection, error classification, and alignment efforts.Optimize inference latency and GPU resource utilization across both cloud and on-premises environments.Desired ExperienceModel Training:Deep understanding of machine learning principles and hands-on experience with model training.

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About Sygaldry Technologies Sygaldry Technologies develops quantum-accelerated AI servers in San Francisco, focusing on faster AI training and inference. By combining quantum technology with artificial intelligence, the team addresses challenges in computing costs and energy efficiency. Their AI servers integrate multiple qubit types within a fault-tolerant system, aiming for a balance of cost, scalability, and speed. The company values optimism, rigor, and a drive to solve complex problems in physics, engineering, and AI. Role Overview: ML Infrastructure Engineer The ML Infrastructure Engineer joins the AI & Algorithms team, which includes research scientists, applied mathematicians, and quantum algorithm specialists. This role centers on building and maintaining the compute infrastructure that powers advanced research. The systems you build will support reliable GPU access, reproducible experiments, and scalable workloads, so researchers can focus on their core work without needing deep cloud expertise. Expect to design and manage compute platforms for a range of tasks, including quantum circuit simulation, large-scale numerical optimization, model training, tensor network contractions, and high-throughput data generation. These workloads span multiple cloud providers and on-premises GPU servers. Key Responsibilities Develop compute abstractions for diverse workloads, such as GPU-accelerated simulations, distributed training, high-throughput CPU jobs, and interactive analyses using frameworks like PyTorch and JAX. Set up infrastructure to support experiment tracking and reproducibility. Create developer tools that make cloud computing feel local, streamlining environment setup, job submission, monitoring, and artifact management. Scale experiments from single-GPU prototypes to large, multi-node production runs. Multi-Cloud GPU Orchestration Design orchestration strategies for workloads across multiple cloud providers, optimizing job routing for cost, availability, and capability. Monitor and improve cloud spending, keeping track of credit balances, burn rates, and expiration dates.

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Machine Learning Research Engineer - ML Systems

Scale AI, Inc.

Full-time|$218.4K/yr - $273K/yr|On-site|San Francisco, CA; Seattle, WA; New York, NY

Join Scale AI's ML platform team (RLXF) as a Machine Learning Research Engineer, where you will play a pivotal role in developing our advanced distributed framework for training and inference of large language models. This platform is vital for enabling machine learning engineers, researchers, data scientists, and operators to conduct rapid and automated training, as well as evaluation of LLMs and data quality.At Scale, we occupy a unique position in the AI landscape, serving as an essential provider of training and evaluation data along with comprehensive solutions for the entire ML lifecycle. You will collaborate closely with Scale's ML teams and researchers to enhance the foundational platform that underpins our ML research and development initiatives. Your contributions will be crucial in optimizing the platform to support the next generation of LLM training, inference, and data curation.If you are passionate about driving the future of AI through groundbreaking innovations, we want to hear from you!

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Member of Technical Staff - ML Research Engineer, Multi-Modal - Audio

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Full-time|On-site|San Francisco

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Oct 31, 2025

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ML Research Engineer - Hardware Codesign

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Full-time|Hybrid|San Francisco

About Our TeamAt OpenAI, our Hardware organization is pioneering the development of cutting-edge silicon and system-level solutions tailored to meet the distinctive needs of advanced AI workloads. We are dedicated to building the next generation of AI silicon, collaborating closely with software engineers and research partners to co-design hardware that integrates seamlessly with our AI models. Our mission includes not only delivering high-quality, production-grade silicon for OpenAI's supercomputing infrastructure but also creating custom design tools and methodologies that foster innovation and enable hardware optimized specifically for AI applications.About the RoleWe are on the lookout for a talented Research Hardware Co-Design Engineer to operate at the intersection of model research and silicon/system architecture. In this role, you will play a critical part in shaping the numerics, architecture, and technological strategies for the future of OpenAI's silicon in collaboration with both Research and Hardware teams.Your responsibilities will include diagnosing discrepancies between theoretical performance and real-world measurements, writing quantization kernels, assessing the risks associated with numerics through model evaluations, quantifying system architecture trade-offs, and implementing innovative numeric RTL. This is a hands-on position for individuals who are passionate about tackling challenging problems, seeking practical solutions, and driving them to production. Strong prioritization and transparent communication skills are vital for success in this role.Location: San Francisco, CA (Hybrid: 3 days/week onsite)Relocation assistance available.Key Responsibilities:Enhance our roofline simulator to monitor evolving workloads and deliver analyses that quantify the impact of architectural decisions, supporting technology exploration.Identify and resolve discrepancies between performance simulations and actual measurements; effectively communicate root causes, bottlenecks, and incorrect assumptions.Develop emulation kernels for low-precision numerics and lossy compression techniques, equipping Research with the insights needed to balance efficiency with model quality.Prototype numeric modules by advancing RTL through synthesis; either hand off innovative numeric solutions cleanly or occasionally take ownership of an RTL module from start to finish.Proactively engage with new ML workloads, prototype them using rooflines and/or functional simulations, and initiate evaluations of new opportunities or risks.Gain a holistic understanding of the transition from ML science to hardware optimization, breaking down this comprehensive objective into actionable short-term deliverables.Foster collaborative relationships across diverse teams with varying goals and expertise, ensuring that progress remains unimpeded.Clearly articulate design trade-offs with explicit assumptions and rationale.

Jan 13, 2026

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AI/ML Research Internship Opportunity

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Internship|On-site|San Francisco

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Senior AI/ML Specialist Solutions Architect (AI Infrastructure & Cloud)

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Full-time|$225K/yr - $315K/yr|Remote|San Francisco

About the CompanyLavendo is a pioneering publicly traded company leading the charge in the AI revolution. With an AI-centric cloud platform, we are transforming the artificial intelligence landscape. Our state-of-the-art infrastructure, including extensive GPU clusters and advanced cloud services, supports developers in harnessing the explosive growth of the global AI industry, catering to Fortune 1000 firms, innovative startups, and AI researchers alike.Company type: Publicly tradedIndustry: AI/ML, Cloud Computing, Infrastructure-as-CodeCandidate Location: Remote U.S.Our mission is to democratize AI infrastructure access and empower organizations to innovate, optimize, and deploy AI solutions seamlessly at any scale. By simplifying the complexities of AI development, we provide a comprehensive full-stack AI platform that marries robust hardware with easy-to-use tools and services.The OpportunityWe are on the lookout for a Senior AI/ML Specialist Solutions Architect to become a crucial part of our client's dynamic team. This role presents an exciting opportunity to design and implement scalable AI solutions tailored for AI-centric clients, leveraging cutting-edge technologies and contributing to one of the most powerful commercially available supercomputers.What You'll DoArchitect and enhance distributed training and inference systems for large-scale AI models.Design and deliver customer-centric solutions that optimize performance and drive business value.Lead the migration of ML pipelines from Proof of Concept to scalable production environments.Foster long-term relationships with clients, ensuring satisfaction and alignment with their strategic objectives.Produce whitepapers, conduct technical presentations, and facilitate webinars to disseminate insights and best practices.Provide technical guidance and mentorship to teams regarding AI infrastructure and deployment strategies.Collaborate with engineering and product teams to prioritize customer feedback and shape product roadmaps.

Feb 23, 2026

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Director of Product Management - AI/ML at Abridge | San Francisco

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Full-time|On-site|SF Office

About AbridgeAbridge, founded in 2018, is dedicated to enhancing understanding within healthcare. Our innovative AI-driven platform is designed specifically for medical conversations, streamlining clinical documentation while allowing clinicians to concentrate on their primary focus—their patients.Our robust technology converts patient-clinician dialogues into structured clinical notes in real-time, integrating seamlessly with EMR systems. Thanks to Linked Evidence and our specially designed, auditable AI, we uniquely align AI-generated summaries with ground truth, enabling providers to trust and verify outputs swiftly. As trailblazers in generative AI for healthcare, we are establishing industry standards for the ethical application of AI across health systems.Our diverse team comprises practicing MDs, AI scientists, PhDs, creatives, technologists, and engineers collaborating to empower individuals and enhance the clarity of care. Our offices are located in the vibrant Mission District of San Francisco, the bustling SoHo area of New York, and East Liberty in Pittsburgh.The RoleThe success of Abridge hinges on delivering the most accurate, personalized, and clinically relevant notes, forming the cornerstone of our product strategy. The quality of our notes is vital for clinician trust and our ability to expand into additional workflows. This role is pivotal in ensuring the quality and personalization of all notes.The Note Generation team is central to this mission, overseeing the models that drive documentation, quality measurement systems at scale, and specialty and workflow engines that guarantee our outputs align with diverse clinical practices.We seek a visionary product leader to direct the product vision, strategy, and execution for Note Generation. You will lead a team of Product Managers and collaborate closely with engineering, machine learning, design, clinical leaders, and our specialty councils. You will establish the standard for note quality, shape our model and evaluation roadmap, and ensure we deliver both essential and distinctive capabilities required to dominate the market.What You’ll DoLead AI product strategy for Note Generation. Define the long-term strategy for Abridge’s core note models and evaluation stack.Oversee the evaluation platform and measurement systems. Create a world-class note quality evaluation system utilizing human annotation, LLM judges, automated backtesting, and production analytics. Collaborate with ML and engineering teams to ensure reliable benchmarks, continuous validation, and rapid iteration cycles.Drive innovation and best practices. Continuously enhance our product offerings and lead cross-functional initiatives to ensure our solutions remain at the forefront of AI in healthcare.

Dec 10, 2025

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Deployment Strategist for AI/ML Product at Pallet

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Full-time|Remote|San Francisco or New York City

Pallet is seeking a dynamic and innovative Deployment Strategist to join our team, focusing on Artificial Intelligence and Machine Learning products. In this role, you will collaborate with cross-functional teams to deliver cutting-edge solutions that drive impactful outcomes for our clients. Your expertise in deploying AI/ML technologies will be critical in shaping the future of our product offerings.

Mar 6, 2026

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