ML Research Engineer Jobs

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Advance inference efficiency end-to-end by designing and prototyping algorithms, architectures, and scheduling strategies for low-latency, high-throughput inference. Implement and maintain changes in high-performance inference engines including kernel backends, speculative decoding, quantization, and profiling and optimizing performance across GPU, networking, and memory layers to improve latency, throughput, and cost. Unify inference with RL and post-training by designing and operating RL and post-training pipelines, optimizing algorithms and systems jointly for inference-heavy workloads, and making RL workloads more efficient with inference-aware training loops. Use RL pipelines to train, evaluate, and iterate on models, co-design algorithms and infrastructure to tightly couple objectives, rollout collection, and evaluation with efficient inference, and quickly identify bottlenecks across all layers. Run experiments to understand trade-offs between model quality, latency, throughput, and cost, and feed insights back into design. Own critical systems at production scale by profiling, debugging, and optimizing inference and post-training services under real production workloads, driving roadmap items involving engine modifications, establishing metrics and experimentation frameworks to validate improvements. Provide technical leadership by setting technical direction for cross-team efforts intersecting inference, RL, and post-training, and mentoring engineers and researchers on full-stack ML systems and performance engineering.

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As a Machine Learning Research Engineer, you will be creating critical AI features for the core Archie product by working closely with AI research scientists, forward deployed engineers, software engineers, and subject matter experts to build AI capabilities for real engineering design tasks. Responsibilities include learning from experts in aerospace, electrical, mechanical, and automotive engineering to develop AI tools solving design engineering problems, collaborating with research scientists to train large language models and transition them into the core product through methods such as Mid-Training, SFT, RL, and Post-Training, and building new agentic features and integrations with major engineering design tools.

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As a Sovereign AI Intern, you will design, train and improve upon cutting-edge models to serve public interest, help develop new techniques to train and serve models safer, better, and faster, train extremely large-scale models on massive datasets, learn from experienced senior machine learning technical staff, and work closely with product teams to develop solutions.

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The Lead Machine Learning Engineer will own the development and improvement of the system predicting the next action salespeople should take to advance their relationships. Responsibilities include selecting the best model architecture and approach, involving a mixture of LLM steps and traditional ML models, picking evaluation metrics, designing systems to analyze models in production to identify areas for improvement, and identifying when to use the human data team for training or validation datasets. The engineer will read relevant research to find the best approach for their use case and, in partnership with the CTO, define how machine learning works with product engineering, model operations, and human data teams and how the team should develop moving forward.

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As an engineer in the Offline Driving Intelligence (ODIN) team at Zoox, the responsibilities include developing advanced multimodal large language models to enhance environmental understanding for robotaxis, designing model architectures and training techniques using sensor inputs and large scale data, driving end-to-end machine learning solutions from research to production using Zoox's data pipelines and infrastructure, collaborating with perception, planning, safety, and systems teams to integrate models into the vehicle's decision-making pipeline, and validating and optimizing solutions using real-world driving scenarios to contribute directly to the safety and reliability of Zoox's autonomous system.

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The Senior Machine Learning Engineer will research, evaluate, and implement state-of-the-art NLP methodologies and large language model approaches to drive product innovation and develop new functionalities. They will design, develop, and deploy LLM agents and multi-agent systems to automate complex legal workflows and enhance user experiences. The role involves collaborating on projects that leverage emerging technologies such as Retrieval-Augmented Generation (RAG) and Knowledge Graphs to enhance the core product and explore new use cases. The engineer will work closely with cross-functional teams to integrate advanced ML models and NLP solutions into the platform, ensuring alignment with business objectives and tangible value. Additionally, they will stay current with the latest trends and breakthroughs in NLP, machine learning, and multi-agent systems, contributing ideas to shape the strategic direction of AI initiatives.

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Design and ship advanced ML systems, especially LLM-driven agents and self-improving workflows. Build robust data and training pipelines, enable fast experimentation, and ensure models and agents continuously improve in production. Build LLM-based agents, tool-using workflows, and autonomous self-improvement loops. Design, train, and evaluate ML models across NLP/LLM, vision, and retrieval domains. Develop data pipelines, training code, experiment tooling, and automated deployment systems. Use PyTorch for model development and W&B (or similar) for tracking experiments and lineage. Implement monitoring for performance, drift, safety, and agent behavior. Optimize inference for latency, throughput, and cost. Work closely with engineering and product teams to turn prototypes into reliable production features. Establish ML engineering best practices and mentor teammates.

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As a Senior Machine Learning Researcher at AKASA, you will lead the design, training, and evaluation of large language models to address healthcare-specific challenges, focusing on advancing clinical Natural Language Understanding. You will collaborate with cross-functional teams including PhD researchers, ML engineers, and healthcare experts to integrate Human-in-the-Loop data for model improvements and explore optimization methods. Your role includes working end-to-end on model design, data creation, training, evaluation, and iteration to ensure research advances both models and real-world healthcare tasks. You will stay updated on machine learning advancements to maintain AKASA's leadership in healthcare AI, partner with healthcare experts to align models with real-world needs, contribute to high-impact publications, and support the integration of your research into AKASA's product offerings used across healthcare systems.

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The Tech Lead is responsible for architecting the system and mentoring a team of three engineers while spending significant time hands-on in the codebase using Python and PyTorch. They will own the core chat loop, optimizing context windows, memory/RAG retrieval, and inference latency to ensure a seamless real-time experience. They must drive the strategy for supervised fine-tuning (SFT), reinforcement learning with human feedback (RLHF/DPO), deciding when to prompt, fine-tune, or architect new retrieval augmented generation (RAG) pipelines. They manage the "Data Engine" overseeing sourcing, labeling, and cleaning datasets to improve model steerability and multicultural performance. Additionally, they design and train custom classifiers for high-precision moderation to detect and filter non-consensual or illegal content, moving beyond binary safe/unsafe flags to enable nuanced, context-aware moderation systems within an uncensored, NSFW environment.

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Lead the LLM team of 3 engineers by acting as both architect and hands-on coder, writing production code in Python/PyTorch, and mentoring the team. Own and optimize the core chat loop, including context windows, memory/RAG retrieval, and inference latency to ensure a real-time user experience. Drive the strategy for supervised fine-tuning (SFT) and RLHF/DPO (Preference Optimization), deciding when to prompt, fine-tune, or design a new RAG pipeline. Manage the data engine responsible for sourcing, labeling, and cleaning datasets to improve model steerability and multicultural performance. Architect and build sophisticated, context-aware moderation classifiers and alignment strategies to detect and filter non-consensual or illegal content in an explicit environment, moving beyond binary safe/unsafe flags.