Top Senior Machine Learning Jobs

Design and deploy agentic AI systems, build ML pipelines, optimize production models, and lead enterprise data standardization and governance initiatives. The role collaborates across business and technical teams to identify AI use cases, establish data quality and metadata standards, promote data literacy, advise on AI best practices, and ensure scalable, reliable, compliant AI solutions.

Lead AI/ML-driven automation for ASIC and SoC design implementation. Explore and prototype generative AI, reinforcement learning, graph neural networks, and convolutional network solutions for VLSI optimization, focusing on power, performance, area, and quality. Collaborate with academic researchers and cross-functional architecture, design, verification, and implementation teams to evaluate methodologies, develop CAD tools, and pilot automation across advanced automotive processor and microcontroller designs.

Develop and lead evaluation systems, benchmarks, and large-scale data pipelines for Waymo foundation models and embodied AI agents. Evaluate models across pretraining, supervised fine-tuning, and reinforcement learning for quality, safety, and realism. Collaborate across teams to bring innovative machine learning technologies into production. The role requires strong ML engineering, deep learning, distributed systems, programming, analytical, and debugging skills.

Own machine learning systems end to end, from data exploration and modeling through production deployment, monitoring, and iteration. Build scalable NLP, LLM, retrieval, ranking, recommendation, prediction, and agentic systems with explainable outputs. Partner with Product and Go-to-Market to deliver customer-facing AI capabilities, improve ML platform reliability, communicate technical work clearly, and mentor engineers.

The Senior ML Engineer will develop and deploy full-stack AI solutions, manage LLM integration, and collaborate closely with defense clients to enhance real-world applications in secure environments.

The Head of ML will define the technical vision, mentor a team of ML engineers, and lead the production of automation products using survey data.

Lead data-centric work for embodied AI: mine and curate fleet driving data, build large-scale enrichment and labeling pipelines, train and fine-tune foundation models, and design rigorous offline and closed-loop evaluation to improve safety and generalization across cities, vehicles, and sensor rigs.

Design, train, and optimize high-performance ML/AI models and inference pipelines for remote sensor (OPIR) data. Integrate models into production Linux-based systems, collaborate with C++ and systems engineers, apply rigorous software engineering and Agile practices, and support missile warning, defense, and sensor-fusion applications.

Lead design, build, and productionize scalable ML systems on cloud platforms. Provide technical leadership, mentor teams, translate business needs into data-driven solutions, work with large datasets, implement recommender/NLP/vision models, leverage LLMs and agentic architectures, and support business development and client engagements.

Design, develop, and productionize scalable machine learning and data science solutions for clients. Analyze very large datasets across cloud platforms, implement recommender/NLP/computer vision and LLM-based systems, optimize for performance and scalability, leverage containerization and data warehousing, and lead/mentor junior practitioners while supporting business development and client strategy.

Lead design and delivery of end-to-end, production-scale ML systems on cloud platforms. Provide technical leadership, mentor teams, analyze and transform large datasets, implement recommender/NLP/risk models, productionize scalable ML, and grow practice through client-facing activities and business development.

Lead and execute ML/AI strategy, design and deploy production-grade ML and GenAI solutions, mentor MLEs, collaborate with cross-functional teams, and build scalable, secure ML/LLM platforms and inference pipelines to drive business impact.

Design and optimize on-vehicle perception inference: allocate CPU/GPU resources, compress and quantize large multimodal models, build TensorRT model conversion pipelines, and deliver production-quality low-latency C++/CUDA inference for power- and thermal-constrained vehicle SoCs.

Develop and lead research and product applications for LLMs, multimodal models, generative AI, and computer vision across cloud, devices, sensors, and robotics. Responsibilities include designing scalable and efficient models, optimizing them for resource-constrained hardware, evaluating real-world performance, mentoring scientists, collaborating with engineering teams, and contributing to patents, publications, and technical documentation.

Build and scale machine learning infrastructure, training systems, inference workflows, and agentic AI tools for protein design research. Improve distributed training performance, GPU utilization, reliability, and scalability while partnering with AI scientists and protein engineers. Translate research prototypes into reusable production systems, contribute to technical direction, evaluate emerging ML tools, and support scientific communication.

As a Senior Scientist, you will develop machine learning methods for biologics design, optimize large-molecule therapeutics, and work collaboratively with experimental teams to enhance drug development processes.

Develop high-performance software for silicon test pattern generation, silicon application, failure analysis, and yield learning. Create parallel graph traversal and analysis methods, collaborate across technical teams, and apply LLMs, RAG, graph-based machine learning, and reinforcement learning to electronic design automation solutions. The role also involves large-scale model training, data processing, agentic applications, and advanced multi-agent and vector database systems.

Design, implement, and evaluate alignment and quality metrics for member-facing LLM and recommendation systems. Build tools and pipelines for RLHF, reward modeling, fine-tuning, and human-in-the-loop evaluation. Collaborate cross-functionally, write production-quality code, and improve model robustness and metrics using member feedback and large-scale distributed systems.

Design, build, and operate low-latency, high-QPS ML model serving and feature systems for real-time ad decisioning. Scale inference, ensure online/offline feature consistency, productionize ranking and scoring models, implement monitoring and simulation infrastructure, and partner with Data Science to validate and rollout models safely.

Build and productionize ML models for shipping cost estimation, retailer credit underwriting, SEO-driven landing pages, personalization, and lifetime value prediction. Collaborate with data scientists, engineers, and product managers to design features, deploy explainable and performant models, and extract value from two-sided marketplace data to grow and optimize Faire's retailer business.

Deploy and maintain state-of-the-art ML models (LLMs and vision) in production for government customers. Improve models via retraining, tuning, and architecture updates; build scalable ML infrastructure; prototype ML-driven product enhancements; work with massive datasets and multimodal fine-tuning pipelines; collaborate across research, product, and engineering. Requires active security clearance and light travel (~10%).

Build, optimize, and deploy production-grade machine learning systems for sports analytics. Improve data pipelines, feature engineering, model training, evaluation, and low-latency prediction services. Collaborate with DevOps and Data Engineering to scale workloads on Kubernetes, maintain cloud-native EDW/ETL solutions, and promote software development best practices.

The role involves developing scalable ML algorithms, optimizing LLM inference performance, managing model serving pipelines, and collaborating with data engineers for efficient knowledge graph generation.

The Machine Learning Engineer will build tools for interpretable AI, optimize infrastructure, and integrate workflows into products, ensuring reliability and performance.

Lead design and implementation of production ML systems for low-latency ad use cases (bidding, ranking, pacing, fraud). Build training and feature pipelines, real-time inference services, and ML team. Partner with engineering and product, own model experimentation, evaluation, monitoring, and deployment at scale.