Top Machine Learning Jobs

Lead the design and delivery of production machine learning systems for infrastructure observability, troubleshooting, and optimization. Develop anomaly detection, time-series analysis, event correlation, recommendations, ranking, and root-cause inference capabilities. Build evaluation frameworks, data pipelines, feature workflows, model-serving services, monitoring, and feedback loops. Integrate ML with telemetry platforms and customer-facing products, establish engineering standards, mentor engineers, and influence technical roadmaps across teams.

AI/ML Engineers will build and deploy AI-native products, autonomous agents, LLM applications, RAG systems, and production ML pipelines. Responsibilities may include integrating AI into crypto infrastructure and consumer products, fine-tuning and evaluating models, optimizing inference, and developing agentic workflows and machine-to-machine payment systems. Engineers will work end-to-end with high-ownership teams in fast-moving environments.

Develop and evaluate machine learning models for object classification, anomaly detection, and data-driven space mission tasks. Support data ingestion, preprocessing, feature engineering, experimentation, and model evaluation. Write clean, tested Python code while collaborating with senior engineers. The role is fully onsite in Denver or Long Beach and requires compliance with U.S. ITAR technology export regulations.

Develop, train, and evaluate machine learning models for space-domain applications including object classification and anomaly detection. Build data ingestion, preprocessing, feature engineering, and model evaluation pipelines; conduct experiments; track results; and write clean, testable Python code. Collaborate with senior engineers while contributing to AI capabilities supporting space security and autonomous systems.

Own machine learning models end to end, from data preparation and training through evaluation and production deployment. Build pipelines for large multimodal datasets including video, sensor streams, and language. Collaborate with engineering and computer vision teams, rapidly prototype solutions, apply relevant research, and continuously improve production models and tooling.

Lead the technical direction and hands-on development of ML and operations research systems powering delivery dispatch. Build pricing, quote selection, forecasting, routing, batching, fleet recommendation, LLM, optimization, evaluation, and MLOps capabilities. Translate operational constraints into reliable models and solver formulations, establish production ML standards, and mentor engineers. The role partners with Product and leadership to deliver measurable improvements in revenue, margin, utilization, conversion, and delivery reliability.

Leads the design, development, deployment, and operation of production AI/ML solutions, including generative AI, RAG, agentic workflows, conversational AI, predictive models, and automation. The role establishes responsible AI practices, integrates enterprise data and APIs, improves evaluation and observability, partners with cross-functional teams, mentors engineers, and drives initiatives from proof of concept through production scale.

Take end-to-end ownership of ML problems from framing and experimentation through deployment and support in customer environments. Build, deploy, and maintain production ML models that inform engineering decisions, and communicate technical tradeoffs to cross-functional and non-technical stakeholders.

The Machine Learning Engineer will develop and deploy AI systems for robotics, focusing on advanced manipulation techniques and control algorithms, collaborating across teams.

Develop and operate production AI/ML systems, including distributed model infrastructure, automated evaluation and testing, GPU optimization, secure CI/CD, and performance analytics. The role owns features end to end from architecture through production, performs model benchmarking and error analysis, and manages reliable deployment across multi-node clusters.

Develop machine learning and personalization systems for real-time advertising at planetary scale. Conduct architectural research on attention-based and post-attention models, evaluate hypotheses offline and online, improve recommender and targeting systems, and transition successful research into production. Build model implementations and data pipelines, guide infrastructure investments, collaborate with engineering and product teams, and contribute to the research community through publications, talks, and conferences.

Train and fine-tune diffusion models, adapters, and pipelines for character-based sticker generation. Build provenance-aware training data pipelines and evaluation systems measuring character fidelity and output quality. Partner with brand teams to secure character approvals, ensure reliable outputs from spoken requests, and collaborate with serving and trust-and-safety engineers to launch safe products for children.

Build and adapt AI models for customer-specific scientific workflows. Lead post-training using SFT and reinforcement learning, design evaluation systems, analyze model failures, and drive iterative quality improvements. Partner with AI researchers and software engineers to integrate reliable model capabilities into production workflows. The role requires strong Python and PyTorch skills, deep familiarity with language, multimodal, or agentic models, and the ability to translate customer needs into technical solutions.

Lead applied AI model post-training, evaluation, and production ML system development. Adapt models to customer-specific scientific workflows using SFT and reinforcement learning, build evaluation and monitoring systems, debug complex model behavior, collaborate with research and software teams, and mentor engineers. The role requires deep expertise in Python, PyTorch, LLMs, multimodal or agentic systems, and shipping reliable machine learning capabilities.

Build high-fidelity digital twins and scalable physics-based simulations of data center power, cooling, and compute systems. Integrate asset models into simulation platforms, validate models against operational data, create APIs and infrastructure for reinforcement learning agent training, and optimize simulation performance. The role requires programming in Python, Go, or C++, first-principles modeling, and familiarity with machine learning environments.

The Founding ML Engineer will build and scale machine learning systems, focusing on ML pipelines, model implementation, and collaboration with teams to drive measurable impact.

The Founding ML Research Engineer will design and evaluate ML models, develop experimentation pipelines, and contribute to AI research while optimizing training processes.

Build and scale production AI systems for Prophetic’s real estate intelligence platform. Responsibilities include developing agentic systems, data pipelines, inference workflows, evaluation benchmarks, geospatial processing, and machine learning applications across hundreds of millions of records. The role requires shipping reliable Python services, optimizing throughput and cost, integrating research into products, and collaborating with engineering and product teams.

Machine Learning Engineer Interns will develop, deploy, and operate production-scale ML models and pipelines. They will lead an end-to-end research-to-production project, apply modern ML techniques to blockchain and crypto use cases, collaborate with senior engineers and product teams, and present findings to stakeholders. The role requires doctoral-level machine learning research, model development experience with PyTorch or TensorFlow, production-quality Python skills, and software engineering fundamentals.

Develop and optimize large language models for clinical documentation, reasoning, coding, and structured clinical outputs. Responsibilities include fine-tuning and post-training open-weight models, building evaluation frameworks and datasets, reducing inference cost and latency, serving models at scale, and moving research into production. The role collaborates with clinicians and engineering teams to deliver reliable AI in regulated healthcare environments.

Lead Zoox’s ML performance optimization team and roadmap for scalable training and inference. Oversee ML platform design, distributed training, GPU utilization, memory and communication optimization, model compression, profiling, and low-latency vehicle inference. Hire and mentor engineers while collaborating with ML research, software, data, product, and hardware teams to deliver measurable performance improvements across autonomous driving systems.

Leads scientific and technical direction for AI across structural heart applications. Designs, trains, validates, and deploys advanced machine learning models, including foundation, generative, multimodal, clinical language, imaging, and waveform models. Evaluates emerging technologies, establishes scientific standards, mentors technical staff, and partners with engineering, clinical, quality, and regulatory teams on safe, fair, robust, and compliant AI products. Builds data pipelines and MLOps capabilities while supporting model validation, risk management, monitoring, and regulatory documentation.

Conduct an open-ended machine learning research project tied to Lyft products. Responsibilities include developing and evaluating reinforcement learning, personalization, and sequential decision-making models; building production-quality ML pipelines; analyzing experiments; collaborating with product and technical teams; communicating findings; and preparing research for publication.

Lead development and deployment of predictive and ML models for credit risk, collections, and operations. Mentor data scientists, promote MLOps and modern tools, present insights to stakeholders, and drive experimentation with techniques such as causal inference and GenAI.

Build reinforcement learning environments from proprietary datasets, develop and deploy production AI agents, and help define technical strategy. Serve as the first engineering hire, collaborate with leadership, and eventually lead the growing engineering team. The role requires strong reinforcement learning expertise, production AI agent experience, AI-assisted coding proficiency, and willingness to work full-time on-site in the Bay Area.