Top Machine Learning Jobs

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.

Lead research and development of end-to-end ML models for autonomous driving, experiment with large-scale datasets and architectures, collaborate with infra and autonomy teams, optimize models for deployment, and deploy solutions to on-road vehicles.

Lead end-to-end ML systems for identity verification, including document authenticity, face match, liveness and deepfake detection. Build GNN-based identity graphs, behavioral/device-intelligence models, vendor benchmarking/evaluation, and production enforcement. Mentor engineers and align ML architecture with Product, Compliance, Risk, and Security stakeholders.

Develop and evaluate ML and sensor-fusion algorithms (vision and radar) for detection, tracking, and classification of aircraft and hazards. Integrate ground and airborne sensors via IoT, run experiments at NASA centers, conduct literature reviews, support ML challenges, and publish/present research.

As an Applied Scientist II, you will work with cross-functional teams on projects that optimize software systems and improve ML components, while analyzing inefficiencies and proposing solutions.

Conduct transportation safety research and analysis to inform Vision Zero and Bus Action Plan; maintain and mine datasets; build predictive machine learning models and data products; perform ad-hoc and geospatial analyses; prepare visualizations and present findings; supervise collaborators and support agency data-driven decision-making.