Top Senior Machine Learning Jobs

Design, build, and deploy transformer-based ML and LLM systems to automate risk investigations and decisioning. Own end-to-end pipelines (data ingestion, prompt tuning, orchestration, productionization, monitoring) and collaborate with product, engineering, and operations to deliver copilot/autopilot workflows that improve analyst productivity and eliminate manual reviews.

Lead research and development of emerging ML (embeddings, foundation models, transformers). Build and productionize machine learning models at scale, partner with product and engineering, analyze large-scale transactional and clickstream data, and run experiments to improve customer experiences across marketing, servicing, and fraud domains.

Lead the design and advancement of AI platform infrastructure, focusing on distributed training, efficiency, and optimization of ML models. Collaborate with teams and guide architectural decisions.

Develop and evaluate ML and perception models for future sensing and autonomous driving. Work on detection, tracking, classification, sensor fusion, training pipelines, validation, robustness metrics, and integrate models into onboard systems while collaborating with cross-functional teams.

Lead end-to-end ML and perception efforts for future sensing in autonomous vehicles: design sensor architectures, multi-modal fusion, model development, metrics, simulation-driven evaluation, system integration, and production readiness while mentoring engineers and building scalable data and training infrastructure.

The Senior ML/AI Engineer will enhance developer productivity for AV engineers at GM by designing, implementing scalable, reliable systems and mentoring team members.

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 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 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.

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.

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.

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.

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.

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.

Design, develop, and deploy end-to-end machine learning and large language model solutions. Perform data discovery, cleaning, preprocessing, and visualization. Build/train LLMs and Agent+RAG systems, collaborate with stakeholders and AI teams, and apply graph-based analysis to support enterprise integration and analytics.