Top Machine Learning Engineer Jobs

Design, build, and maintain ML infrastructure for model lifecycle: training, optimization, validation, deployment, inference, and observability. Own ML inference and compiler platforms, develop scalable ML pipelines, continuous testing/monitoring, and support deployment to diverse hardware for autonomous vehicles.

Build and maintain core ML infrastructure: design and implement ML training, optimization, validation, and deployment pipelines; operate an in-house ML inference and compiler platform; implement testing, monitoring, and observability for model lifecycles; collaborate with cross-functional teams to deploy optimized models to vehicle hardware.

Design, build, and own production-grade LLM and agentic AI systems, including evaluation pipelines, failure diagnosis, retrieval and orchestration, data-driven improvement loops, deployment, and observability while collaborating with clinicians and product teams.

Lead design, development, deployment, and maintenance of large-scale ML systems and pipelines. Provide senior technical consulting, resolve complex ML and governance issues, plan end-to-end model integrations, and document implementations for performance optimization.

Lead design and implementation of agentic AI systems in the Unity engine, developing scalable RL and planning frameworks, integrating ML models under real-time constraints, establishing evaluation and safety practices, and mentoring engineers to deliver reusable, performant agent behavior for games and simulations.

Design, optimize, and scale training and inference workloads for personalization and continual learning. Productionize research prototypes, improve latency/reliability/efficiency, optimize per-user serving and memory retrieval, and work on distributed training across GPUs and nodes. Collaborate with researchers, platform engineers, and customers; establish engineering practices, testing, on-call, and security posture.

Act as 'Customer Zero' for the internal ML research platform: benchmark and stress-test distributed training and inference stacks, enable rapid prototyping and agentic/auto-research workflows, identify system bottlenecks across hardware and software, document empirical findings, and advise engineering and research teams on platform improvements.

Design and optimize ultra-low-latency ML inference pipelines across CPUs, GPUs, and FPGAs. Benchmark heterogeneous platforms, develop high-performance GPU kernels, resolve memory and interconnect bottlenecks, and apply model reduction (quantization, pruning, distillation) to meet microsecond-level latency for production trading systems while collaborating with ML researchers, HPC, FPGA, and datacenter teams.

Build and scale end-to-end ML platforms and generative modeling tools for molecular design. Own data ingestion, training, evaluation, serving, and reproducibility; optimize accelerator utilization; automate ML-to-wet-lab workflows; prototype and deploy models to accelerate drug discovery.

Design, build, and operate ML and data infrastructure for R&D Quality: productionize models, develop reliable data pipelines and orchestration, implement CI/CD and monitoring, support AI/agent workflows, ensure security and GxP compliance, and partner with data scientists and Quality stakeholders to deliver scalable analytics.

As a Senior ML/AI Engineer, you'll design and deploy intelligent systems for forecasting, sentiment analysis, and autonomous decision-making. Responsibilities include building ML models, developing agentic architectures, and maintaining production systems at scale.

As a Machine Learning Engineer, you will develop models for NLP and code-generation systems, analyze data, and improve AI performance using advanced algorithms. You'll manage the entire ML lifecycle and ensure effective communication of model benefits across the organization.

The Senior Machine Learning Engineer will develop, deploy, and optimize large language models on healthcare data, collaborate with teams, and ensure compliance.

Lead design and operation of a next-generation ML platform on GCP: build IaC, centralize projects into a compliant landing zone, administer GKE clusters for distributed ML, automate identity and guardrails with Terraform, optimize GPU/TPU compute and costs, and create developer onboarding and paved paths for scientists.

Build and maintain ML platform tooling and pipelines for perception, prediction, and planning. Enable dataset curation, distributed training, evaluation, and deployment (cloud and edge). Optimize tooling, collaborate with ML teams, review code, and drive long-term platform strategy while working in a hybrid Palo Alto environment.

Build and productionize large-scale ML models and pipelines for query understanding and personalization. Work on autocomplete, query tagging/NER, query expansion, intent modeling, and LLM-powered natural-language search. Collaborate cross-functionally to design, deploy, and monitor batch and real-time systems, leveraging modern ML infrastructure and tooling to improve search relevance across Airbnb products.

Architect and develop a high-performance ML runtime for autonomous vehicles, optimizing for both onboard and offboard systems while collaborating with Waymo ML practitioners and enhancing tooling for performance analysis.

The Senior Machine Learning Engineer will utilize deep experience and insights to enhance communication and digital interactions through advanced machine learning tools and algorithms, focusing on unstructured data.

Build and maintain ML and generative AI tooling, training and inference platform services, model evaluation and testing frameworks, and APIs to expose ML products. Deploy and manage applications across the AI/ML SDLC, mentor teammates, and write production-quality code daily.

Design, build, and operate large-scale, low-latency recommendation systems (retrieval, ranking, multi-objective optimization). Drive offline training, online inference, A/B testing, and metric analysis. Apply LLMs and foundation models for content understanding, user intent, and feature generation. Rapidly prototype AI/LLM ideas and own projects from MVP to production, ensuring data quality and system performance.

Develop and train computer vision and visual representation pipelines for robotic manipulation (RGB, RGB-D, depth, segmentation, pose, keypoints). Build perception models to support RL and imitation policies, improve synthetic/real data pipelines, evaluate models on real robots, calibrate sensors, and collaborate with robotics and simulation engineers to iterate on models and data for robust deployment.

Develop and implement reinforcement and supervised learning algorithms for robotic manipulation, build simulations and domain randomization, optimize model architectures and data pipelines for sample efficiency, and deploy, evaluate, and debug policies on physical robots.

Design, implement, and maintain large-scale ML/RL training infrastructure: job orchestration, scheduling, checkpointing, experiment tracking, developer tooling, distributed training, and resource management for cloud compute.

As a Senior Machine Learning Engineer (Tech Lead), you'll lead the ML technical direction, design architectures for loco-manipulation, mentor team members, and manage cross-functional partnerships while writing production-quality code.

The ML Systems Engineer will design and manage efficient training and inference systems, optimize hardware utilization, and collaborate with researchers on RL loop integration, enhancing scientific discovery.