Top Machine Learning Jobs

Lead modeling of weather patterns and their impact on the Waymo Driver. Build pipelines and spatio-temporal models integrating multiple weather data sources. Measure performance in adverse weather, develop scalable analysis frameworks, optimize operational mitigation processes, and collaborate with product and engineering. Act as a technical contributor and lead, scope ambiguous problems, and champion measurement rigor across teams.

Build and deploy statistical and machine learning models, design experiments, analyze product and operational metrics, drive data collection, forecast performance, and communicate insights to product, engineering, and business teams.

Build LLM- and agent-based prototypes and production solutions, develop ML models where appropriate, prioritize reuse and scalability, collaborate with product, platform, and front-end partners, and support business development through technical delivery and client-facing engagement.

Build and own production applied-AI systems focused on information retrieval and LLM pipelines. Design experiment frameworks and evaluation pipelines, optimize for metrics (precision, recall, F1), run benchmarks, perform failure analysis, and iterate to improve real-world system performance at scale.

Design, build, and deploy ML systems that link extracted document features to repayment and fraud outcomes. Run backtests, build evaluation and QA frameworks, develop adaptive/online learning for fraud and credit risk, and collaborate with customers to integrate models into underwriting workflows.

The AI Enterprise Engineer will work with enterprise customers to define model specifications, analyze agent behavior, and benchmark models using Bespoke Labs tools, requiring a strong foundation in AI/ML and production deployment.

Design and build the data and analytics layer: schemas, pipelines, APIs, dashboards, forecasting and ML models. Deliver usage-based metering, ensure data correctness and observability, and ship customer-facing analytics while raising engineering standards.

This role involves designing, deploying, and maintaining machine learning and AI models in production, managing ML pipelines, and ensuring system reliability and performance.

Develop and implement advanced reinforcement learning methods and distributed systems for AI agents. Collaborate with customers to understand needs and create tailored solutions.

As a Senior Machine Learning Scientist, you will develop methodologies for advertising-related challenges, utilizing deep learning and statistical techniques for optimization and experimentation.

Architect and lead the development of a state-of-the-art inference platform focusing on low latency and scalability for advertising. Oversee performance optimization and collaborate with cross-functional teams.

Lead design and implementation of a scalable data lakehouse and analytical layer in Google Cloud, develop and deploy machine learning models and pipelines, mentor data scientists, set technical strategy, and partner with stakeholders to drive predictive formulation and product innovation.

Develop spatio-temporal machine learning models for automated driving, collaborate with experts on algorithm design, and ensure production-level implementation of perception systems.

Build, deploy, and maintain production machine learning models to predict business outcomes; support experiments and forecasting of product/features; analyze large transactional data to identify marketplace optimizations; collaborate with product, growth, and engineering to drive experiments into production and communicate results to stakeholders.

The Senior AI ML Engineer will design and implement AI/ML systems for healthcare, manage projects from ideation to release, and mentor engineers. Responsibilities include delivering quality code, driving architectural decisions, and collaborating with cross-functional teams.

Lead the delivery of ML initiatives, define roadmaps with cross-functional teams, and develop ML models for conversational agents and smart tools, enhancing customer support.

As a Senior Machine Learning Engineer in Model Risk Management, you will evaluate and validate models that assess risk in lending and fraud, ensuring their robustness and reliability. You will collaborate with model owners, tackle model challenges, and build evaluation tools critical for understanding model performance.

Design and deploy machine learning models for financial document processing, optimize large language models, collaborate with cross-functional teams, and guide junior peers in a fast-paced environment.

Develop and maintain AI/ML applications, focusing on agent-based workflows, API integration, and enterprise deployment, requiring senior-level experience.

As a Lead Machine Learning Engineer, you'll manage AI/ML projects, optimize models, enhance ad-serving platforms, and mentor junior engineers.

Responsible for managing the AI/ML project lifecycle, developing low-latency ad-serving platforms, and enhancing search solutions through advanced modeling techniques.

This role involves developing AI/ML architectures, resolving complex issues, and partnering with stakeholders to enhance business outcomes through technology and data.

Design and guide enterprise-scale, cloud-native solutions leveraging software engineering and AI. Provide technical leadership across SDLC, design mobile/cloud apps, integrate via APIs and data services, implement CI/CD and release/configuration practices, and architect end-to-end ML pipelines for cloud and edge deployment while aligning solutions with business objectives.

As a Machine Learning Engineer, you will own the execution layer for ML projects, including data preprocessing, training, benchmarking, and collaboration with researchers to deliver actionable results.

Develop and deploy state-of-the-art physical AI for outdoor robotics, focusing on perception, planning, control, simulation-driven training, multi-sensor integration, data pipelines, and GPU-optimized training and inference.