Top Machine Learning Engineer Jobs

Lead the design, development, optimization, and production deployment of advanced machine learning models and algorithms. Build scalable ML pipelines, preprocess and analyze large datasets, ensure data quality, monitor model performance, and collaborate with data scientists, software engineers, and product teams to integrate AI/ML solutions into products and services.

Lead the architecture and production operation of Grubhub’s ranking, recommendation, retrieval, and query-understanding systems. Drive improvements in long-term diner value through deep learning, embeddings, LLMs, experimentation, and real-time inference. Establish model evaluation, deployment, observability, and reliability standards across teams. Partner with Product, Search, Ads, and Data Platform, mentor engineers, influence technical strategy, and communicate trade-offs to executive stakeholders.

Own the architecture and technical direction of large-scale ranking, recommendation, retrieval, and query-understanding systems. Lead production machine learning across deep learning, embeddings, LLMs, experimentation, model deployment, and observability. Partner across Product, Search, Ads, and Data Platform, mentor engineers, drive architecture reviews, improve operational standards, and translate technical trade-offs for executive stakeholders.

Develop, train, evaluate, debug, and improve machine learning models for real-world applications. Own the ML lifecycle from dataset construction and experimentation through deployment, while applying deep learning, reinforcement learning, and LLM techniques. Collaborate with engineering and product teams, contribute to scalable ML infrastructure and tooling, define evaluation metrics, analyze errors, and translate research concepts into practical solutions.

Develop ML-powered motion planning systems for Level 4 autonomous vehicles. Responsibilities include building learned planners and end-to-end models, applying imitation and reinforcement learning, integrating ML with rule-based and optimization-based planning, training and evaluating models using real-world data and simulation, and deploying production systems under safety, latency, and onboard compute constraints. The role also involves scenario mining, evaluation infrastructure, closed-loop simulation, and collaboration across autonomous driving teams.

Lead camera ISP tuning and image quality evaluation for autonomous vehicle cameras. Collaborate with cross-functional teams and vendors to define specs, characterize sensors, develop ISP configurations, run objective/subjective IQ tests (Imatest), and optimize ISP for perception performance in production vehicles.

Build and operate MLOps infrastructure for production AI agents, including evaluation frameworks, CI/CD quality gates, model access across cloud providers, experiment tracking, observability, versioned deployments, and cost optimization. Develop and fine-tune LLMs and machine learning models, monitor production quality and drift, and collaborate with AI engineers and clinical experts to deliver reliable healthcare claims automation.

Own the system that enables AI characters to remember users across hundreds of conversations, including memory schema design, information extraction, and compression. Develop and evaluate improvements based on user retention and recall accuracy. Work with a small, fast-moving engineering team in person at the Palo Alto office, taking ownership of ambiguous problems and defining effective solutions.

Design, build, and deploy scalable AI/ML models and pipelines for consumer healthcare. Develop production code (Python/Java/Scala), automate infrastructure (Terraform, Kubernetes), validate model performance, integrate cloud services (Azure/AWS), and collaborate cross-functionally to monitor and document enterprise-scale AI solutions.

Develop, train, evaluate, and deploy machine learning models using Python and PyTorch for defense applications. Build backend software, data pipelines, APIs, and system integrations supporting ML workflows. Prepare datasets, implement published ML methods, work in Linux-based containerized environments, and collaborate across project teams. Document and communicate technical results while contributing to development processes and operationally relevant prototypes.

Design, build, and deploy scalable ML pipelines for multimodal neuroscience data. Research, train, and deploy advanced deep learning and reinforcement learning models to predict brain states, detect pathological patterns, and generate personalized neuromodulation parameters. Select AI frameworks and cloud infrastructure, collaborate with computational neuroscientists, and contribute to IP and publications.

Design, build, and deploy autonomous AI agents using LLMs and agent frameworks; integrate agents into e-commerce and supply chain systems; implement multi-agent orchestration, memory and tool integrations; fine-tune models and apply RL concepts; and build safety, monitoring, and MLOps pipelines.

Lead the design, productionization, and operation of machine learning and agentic AI capabilities. Partner with product engineering, shape AI/ML architecture, build evaluation systems and data pipelines, and develop reliable MLOps tooling. Champion responsible AI, safety, latency, cost, and error handling. Serve as a technical resource for engineers, influence direction, mentor others, and occasionally explain AI functionality to customers or customer-facing teams.

Lead the architecture and development of Vumedi’s AI-powered discovery platform. Build recommendation systems, semantic search, retrieval and ranking pipelines, knowledge graph RAG systems, and LLM-powered experiences for physicians. Design scalable inference infrastructure, evaluate emerging AI technologies, define long-term AI architecture, and collaborate with product, data science, and engineering teams while contributing to production code.

Lead the technical strategy and development of machine learning solutions for Uber’s homepage personalization, including sequential recommendation, intent-aware ranking, LLM-based systems, and agentic AI. Partner with product teams to improve engagement and business outcomes, influence technical direction across teams, and mentor machine learning engineers.

Build, deploy, and maintain production machine learning models and end-to-end ML pipelines. Develop MLOps frameworks with model versioning, CI/CD, registries, feature stores, monitoring, and automated retraining. Deploy solutions using cloud and containerized technologies, troubleshoot performance issues, address model drift, and collaborate with engineering and data teams on scalable mission-critical applications.

Architect and lead large-scale machine learning systems, including LLM infrastructure and production deployments. Define ML architecture, MLOps, governance, observability, security, performance, and cost standards. Partner with stakeholders to translate customer needs into scalable solutions, influence AI strategy, mentor engineers, and guide adoption of modern AI techniques such as RAG, vector search, prompt engineering, and agent workflows.

Own the architecture and engineering of production AI/ML infrastructure powering Relay. Build model-serving, inference, evaluation, orchestration, deployment, observability, and lifecycle systems. Optimize model performance, latency, throughput, scalability, reliability, and cost while establishing feedback loops for continuous quality improvement. Partner with engineering and product teams to advance agentic AI capabilities and make foundational architectural decisions in a rapidly evolving environment.

Build and deploy production machine learning models, scalable ML infrastructure, real-time training and inference pipelines, multimodal AI systems, and model APIs. Optimize model performance for cost, speed, and accuracy while applying MLOps practices. Collaborate with data science teams to productionize research models and independently architect highly available ML systems for a global media and entertainment platform.

Design, build, evaluate, and deploy LLM-powered agents and machine learning systems that improve Quora content quality and safety. The role involves developing agentic workflows, conducting offline and online evaluations, monitoring production performance, investigating failures, and improving accuracy, reliability, latency, and cost. New graduates will collaborate with experienced engineers and own projects from development through production.

Set technical direction for production AI agents, LLM and vision-language model post-training, evaluation, MLOps, and data improvement systems. Architect agent orchestration, retrieval, observability, and failure recovery; establish quality and release standards; and ship measurable model improvements. This hands-on individual contributor will co-own the ML roadmap, lead technical reviews, mentor engineers, support hiring, and implement privacy-conscious, HIPAA-aware practices.

Leads architecture, development, optimization, and production deployment of machine learning and deep learning models for edge AI systems. Develops computer vision, signal processing, sensor fusion, and distributed ML infrastructure capabilities. Optimizes models for resource-constrained AI chips and edge devices, using Python, C++, PyTorch, and TensorFlow. Provides technical leadership and collaborates with scientists, engineers, and product managers across public safety, enterprise, and defense applications.

Designs, builds, deploys, and optimizes generative AI and machine learning solutions on AWS, including Amazon Bedrock, foundation models, and RAG architectures. Develops document-processing automation, APIs, AI/ML models, monitoring, evaluation, and continuous-improvement processes. Integrates OCR, ICR, OMR, NLP, and data extraction capabilities while collaborating with cross-functional teams. The role also supports scalable cloud architecture, serverless applications, CI/CD, DevSecOps, and post-implementation troubleshooting.

Leads enterprise AI architecture for generative AI, agentic systems, traditional machine learning, and computer vision. Designs secure, scalable platforms; establishes MLOps and LLMOps standards; prototypes and writes production code; leads client solutioning and pre-sales; ensures regulatory compliance; mentors architects and engineers; and governs technical standards across strategic engagements.

Lead the architecture, development, and deployment of Cisco’s foundational AI platform across cloud, on-premises, edge, and air-gapped environments. Design scalable multi-tenant AI services, optimize models and inference for cost and performance, build production LLM, RAG, and agentic systems, establish automated evaluation frameworks, and drive technical governance. Mentor engineers, lead architecture reviews, and influence platform strategy through hands-on execution.