Top Senior Machine Learning Jobs

Build AI-training products from acquired codebases, workspaces, and databases. Develop reinforcement-learning environments, agentic task suites, evaluations, verifiers, benchmarks, and training datasets. Create reproducible pipelines using repository ingestion, test harnesses, Docker sandboxes, reward scripts, and QA tooling. Collaborate with AI labs and buyers, identify commercial opportunities, and protect sensitive data through strong security, privacy, licensing, and PII practices.

As a Machine Learning Engineer, you'll own the design, development, and operation of production-grade ML systems in clinical workflows, ensuring model accuracy, reliability, and integration into patient-facing applications.

Lead design and implementation of data pipelines and analytic tools for imagery curation and ML model development. Integrate new sensors and data types, perform quantitative analyses, build visualization and monitoring tools, manage labeling campaigns and ETL processes, and provide recommendations for training/test/validation splits to support operational intelligence missions.

Support a US government customer in Chantilly, VA by applying advanced analytics, statistical modeling, and machine learning to cyber-related data. Collaborate with cyber SMEs to build AI/ML models and analytics, work with cyber datasets (e.g., Shodan, Censys.IO), manage and process large volumes of unstructured text, and operate in Linux and Windows environments. Requires active TS/SCI with poly.

Pre-sales Solutions Architect who defines technical strategy and drives Databricks adoption across large enterprise accounts. Leads technical engagements, builds executive relationships and internal champions, mentors team members, and maps ML/AI use cases to Databricks solutions. Supports proofs-of-concept and guides production implementations.

Designs and operates production multi-agent AI systems, LLM workflows, scoring and optimization algorithms, and closed-loop autonomous processes. Responsibilities include AI model evaluation, production monitoring, governance, third-party AI integration, federated data retrieval, and identifying patentable inventions. The role requires deep machine learning expertise, algorithm development, and deployment experience across complex operational environments.

The Sr. Machine Learning Engineer will develop and deploy ML solutions for healthcare, manage data pipelines, and work with large datasets to enhance healthcare delivery.

As a Staff Applied ML Scientist, you will develop and maintain fraud detection models, conduct research on fraud types, and collaborate across teams to drive product development and real-time decision-making.

As a Senior Applied ML Scientist, you'll develop fraud detection models, research new fraud types, and write production code while collaborating across teams to enhance SentiLink's identity verification solutions.

Leads architecture and development of production-grade mapping and localization ML systems for autonomous vehicles. Designs neural models for detection, segmentation, tracking, depth estimation, 3D reconstruction, and semantic mapping; owns data strategy, evaluation, deployment, and fleet validation. Mentors engineers, drives technical roadmaps, collaborates cross-functionally, and advances vectorized HD mapping, foundation models, and real-time optimization.

Leads the design and development of large-scale search and discovery relevance systems, including machine learning infrastructure, serving, pipelines, and AI products. Drives technical strategy, execution, operational excellence, and business impact while partnering with senior stakeholders. Explores deep learning and large language model innovations, improves search quality, resolves complex technical challenges, and recruits and develops high-performing engineering talent.

Develop and optimize large-scale advertising ranking models for CTR and CVR prediction, calibration, user representations, and sequence modeling. Own machine learning systems end to end, including data pipelines, feature engineering, training, evaluation, deployment, and maintenance. Collaborate with platform, product, data science, and engineering teams to run experiments and improve advertiser outcomes, revenue, and user relevance. Apply deep learning and recommendation techniques within production latency, reliability, and cost constraints.

Designs and implements AI/ML capabilities for cyber and intelligence workflows, including LLM-powered and agentic automation. Captures mission-user workflows, integrates AI systems with APIs and enterprise data stores, and delivers reliable solutions for classified environments. Collaborates with analysts, operators, developers, and leadership to improve analytical efficiency, data accessibility, and decision-making while meeting security and governance requirements.

Lead the architecture and production deployment of agentic AI systems for classified and mission-critical government environments. Build systems for geospatial reasoning, retrieval, embeddings, persistent memory, orchestration, evaluation, and reliability. Own shared ML infrastructure, define technical direction, partner across engineering and research teams, mentor engineers, and drive ambiguous initiatives from prototype to supported production capability.

The Research Engineer will develop and deploy machine learning applications focused on severe weather, collaborating on system processes, and maintaining production environments.

Design, build, and productionize low-latency ML models and inference infrastructure for ad serving, inventory forecasting, yield optimization, bidding and allocation. Handle massive data with Spark, build scalable simulation systems, collaborate with product, science, and engineering to deploy models at scale, and ensure advertiser outcomes and member experience across client and server integrations.

Design, implement, and deploy large-scale machine learning and optimization algorithms for ad ranking. Build and evaluate models, create online/offline evaluation frameworks, partner with product to define objectives and constraints, and communicate results to stakeholders to drive ad quality and performance.

Design, productionize, and deploy machine learning forecasting models for ad inventory and campaign performance in low-latency, real-time advertising environments. Build scalable simulation, budgeting, and pacing systems; collaborate with cross-functional teams to deploy models at scale and optimize ad monetization.

Design, build, and evaluate large-scale ML-driven bidding algorithms for ad optimization (clicks, conversions, CPA, ROAS). Develop online/offline evaluation frameworks, inform auction/pricing design, partner with product, and communicate results to technical and non-technical stakeholders.

Conduct machine learning research operations using existing infrastructure. Characterize neural network quality, failure modes, and edge cases; coordinate experiments with researchers; manage concurrent experiments and deadlines; review results; recommend theoretical or process improvements; and prepare technical reports communicating qualitative and quantitative findings.

Build and maintain scalable data platforms and pipelines that integrate structured and unstructured data. Retrieve, merge, and analyze data from APIs and databases using Python, R, and SQL. Prepare datasets for AI and generative AI models, evaluate machine learning performance, and guide multidisciplinary teams in agile client projects supporting fraud detection, cancer research, and national intelligence missions.

Build and deploy scalable machine learning models and pipelines, integrate ML into products, and develop robust systems using Python, FastAPI, React, and AWS. Lead technical design, code and architecture reviews, agile ceremonies, incident response, and reliability practices. Mentor engineers, improve testing and CI/CD, drive cross-functional alignment, and establish high standards for software quality, documentation, observability, and maintainability.

As a Staff Machine Learning Engineer, you will lead the development and deployment of AI models for autonomous driving, focusing on safety and reliability. Responsibilities include driving the roadmap, training models, and collaborating across teams.

Lead reinforcement learning development for end-to-end autonomous driving models. Responsibilities include designing offline and off-policy RL methods, improving reward models, building large-scale training and evaluation workflows, diagnosing distribution shift and optimization failures, validating policies through simulation and on-road testing, productionizing successful methods, and mentoring engineers. The role also leads development of learned emergency trajectory models for safety-critical maneuvers such as evasive steering and emergency braking.

Owns the architecture, development, evaluation, and production deployment of generative AI, agentic AI, computer vision, and reinforcement learning systems for mission-critical government applications. Responsibilities include building scalable ML infrastructure, improving models through tuning and retraining, developing evaluation benchmarks, partnering with users and research teams, mentoring engineers, and designing for classified and resource-constrained environments.