Top Machine Learning Engineer Jobs

Lead the Inference ML team in developing tools and APIs for large-scale ML applications, enhancing performance and usability, while collaborating across engineering teams.

The role involves managing the ML lifecycle, developing scalable ML systems, and collaborating on AI-first product solutions focusing on Large Quantitative Models.

Design and implement end-to-end AI agent systems, collaborate with cross-functional teams, and oversee deployment and performance evaluation in production environments.

As a Senior Machine Learning Engineer, develop LLM evaluation frameworks and agentic AI workflows to extract insights from conversations and optimize customer experiences.

The Physical Design Engineer will lead efforts in solving physical design challenges, improve RTL-to-GDS methodologies, and integrate AI/ML solutions into CAD flows, focusing on performance and runtime improvements.

As a Senior Machine Learning Engineer, you will drive AI vision, develop models, and collaborate cross-functionally to enhance user experiences.

Build and expand in-database ML model catalog: implement new model types, ensure framework parity with scikit-learn/Spark ML, optimize training and inference at petabyte scale, extend SQL-level linear algebra, and collaborate across teams to deliver correct, performant ML features and documentation.

Build and maintain ML training and inference infrastructure, model APIs, and design pipelines. Partner with ML and protein design scientists to convert research into reproducible tooling and production services. Optimize compute and storage, implement CI/CD, monitoring, and follow engineering best practices to scale and accelerate protein design workflows.

Lead ML efforts for behavior and planning: design, train, and deploy state-of-the-art models (prediction, decision-making, RL/imitation, generative, transformers), own full model lifecycle from data to onboard inference, generalize behavior across geographies and platforms, collaborate cross-functionally, and mentor engineers.

Lead hands-on research and engineering across agentic ML: train and fine-tune models, design evaluation and observability, build production improvement loops and tooling, prototype agent architectures, partner cross-functionally to productionize research, and set technical direction while mentoring senior engineers and scientists.

Design, build, and deploy production ML systems that combine structured clinical/EHR data with computer vision outputs. Own model calibration, interpretability, monitoring, drift detection, retraining workflows, and regulatory documentation. Partner cross-functionally, mentor engineers, and translate clinical questions into reliable, production-grade ML solutions.

Design, build, deploy, and operate production-grade ML systems and pipelines for a massive consumer audience. Own full lifecycle from data processing and feature engineering through training, validation, deployment, and monitoring. Drive ML infrastructure and architecture decisions, partner with product and data teams, optimize models against business metrics, and mentor engineers to ensure security, reliability, and compliance.

The Research Engineer, Infrastructure will build distributed training systems, optimize performance, manage data pipelines, and enhance research workflows, ensuring the infrastructure scales with AI advancements.

Design, build, and scale ML infrastructure: ingest vehicle sensor data, create batch pipelines for dataset curation, run distributed GPU training, and ensure performance, observability, efficiency, and security across the ML pipeline while partnering with ML teams.

Lead design, development, and production deployment of ML models and GenAI/LLM solutions to optimize clinical trial workflows. Build scalable pipelines, monitoring, and CI/CD for robust production use. Collaborate cross-functionally, mentor junior engineers, and communicate technical results to technical and non-technical stakeholders.

The role involves developing end-to-end machine learning solutions, focusing on NLP and computer vision, collaborating with teams, and preparing technical briefings for stakeholders.

As an Applied ML Software Engineer, you will design and implement ML models, manage cloud infrastructure, and optimize backend systems for AI-driven content discovery and recommendations.

The ML Platform Engineer will build scalable architecture for ML training workloads, optimize system performance, and collaborate with teams for enhanced efficiency on Kubernetes.

Develop machine learning and computer vision algorithms for automation and autonomy. Collaborate with experts, optimize solutions, and apply research insights.

Build and improve ML infrastructure and model pipelines for autonomy: optimize models (quantization, pruning, low precision), maintain and profile GPU ML compilers and runtimes, develop a model compiler framework (FTL), and collaborate with autonomy teams to validate and deploy optimized models to fleet vehicles.

Build and evolve ML infrastructure platform for resource provisioning, workload scheduling, petabyte-scale ETL, feature management, and platform abstractions to support large-scale model development and distributed training.

Build and scale ML infrastructure: automated IaC for GPU/CPU fleets, high-performance workload scheduling, petabyte-scale ETL, feature caching/storage, and platform abstractions to accelerate model development from experiment to production.

Design, build, and deploy ML infrastructure to optimize autonomy models using quantization, pruning, distillation, and model compilers. Maintain and profile GPU ML compilers/runtimes, collaborate with autonomy teams to validate and deploy optimized models to production vehicles.

As a Senior Staff Data Engineer, you'll set the technical vision for data architecture, lead initiatives across teams, mentor engineers, and ensure platform reliability, while integrating data systems with ML pipelines effectively.

The Training ML Framework Engineer will enhance training throughput in a distributed ML framework, collaborate with researchers, and optimize systems.