ML Research Engineer Jobs

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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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Build end-to-end training pipelines including data preparation, training, evaluation, and inference; design new model architectures or adapt existing open-source frontier models; fine-tune models using state-of-the-art methods such as LoRA/QLoRA, SFT, DPO, and distillation; architect scalable inference systems using technologies like vLLM, TensorRT-LLM, and DeepSpeed; build data systems for high-quality synthetic and real-world training data; develop alignment, safety, and guardrail strategies; design evaluation frameworks covering performance, robustness, safety, and bias; own deployment tasks focusing on GPU optimization, latency reduction, and scaling policies; shape early product direction, experiment with new use cases, and build AI-powered experiences from zero; explore frontier techniques including retrieval-augmented training, mixture-of-experts, distillation, multi-agent orchestration, and multimodal models.

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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.

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The Tech Lead will ship code and lead from the front by architecting the system and mentoring the team while spending significant time hands-on in the codebase using Python and PyTorch. They will own the core chat loop by optimizing context windows, memory/retrieval-augmented generation (RAG) retrieval, and inference latency to ensure a seamless, real-time experience. They will own the model lifecycle by driving strategy for supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF/DPO), deciding when to prompt, fine-tune, and architect new RAG pipelines. They will manage the sourcing, labeling, and cleaning of diverse datasets to improve model steerability and multicultural performance. Additionally, they will architect high-precision moderation by designing and training custom classifiers to detect and filter non-consensual or illegal content in an explicit environment and create nuanced, context-aware moderation systems beyond binary safe/unsafe flags.

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The Tech Lead will ship production code and lead the LLM team of 3 engineers by acting as both architect and mentor. Responsibilities include owning the core chat loop by optimizing context windows, memory/RAG retrieval, and inference latency for a real-time experience. The role involves driving strategy for supervised fine-tuning (SFT), RLHF/DPO preference optimization, managing data sourcing, labeling and cleaning to improve model steerability and multicultural performance. Additionally, they will architect high-precision moderation systems by designing and training custom classifiers to detect and filter non-consensual or illegal content in an explicit environment, moving beyond binary safe/unsafe flags to nuanced, context-aware moderation systems.