Top Senior Machine Learning Jobs

Build and maintain machine learning training, evaluation, data access, and model infrastructure for biological research. Responsibilities include designing large-scale data storage and versioning systems, maintaining a model zoo, supporting lab-in-the-loop training, making build-versus-buy decisions, and collaborating with AI scientists to develop novel models.

Technical lead for end-to-end ML and CV pipelines to reconstruct, validate, and maintain map primitives from multi-modal vehicle sensor data. Architect scalable offline and production map reconstruction systems, define technical strategy, drive cross-functional integration, mentor senior engineers, and ensure robustness, safety, and operational scalability for national-scale mapping.

Lead development of large-scale, low-latency ML systems for ad serving to optimize pricing, allocation, and auctions. Design experiments, build production ML pipelines and model serving, partner cross-functionally, and provide technical leadership and mentorship.

Design, productionize, and scale machine learning and optimization models, pipelines, libraries, and deployment platforms. Partner with data scientists, engineers, product managers, and business stakeholders to deliver ML capabilities, personalization systems, and reusable end-to-end workflows. Establish ML engineering best practices, troubleshoot data pipelines, mentor junior professionals, and help define BetterHelp’s ML platform strategy.

Own robotic grasping intelligence by designing bespoke neural networks, building cloud training and evaluation pipelines, deploying models to robot fleets, and improving performance through field telemetry. Develop simulation-first manipulation strategies using robotics concepts including motion planning, kinematics, geometry, and perception. Collaborate with perception and motion-planning engineers while setting technical direction for a small applied ML team.

Lead the technical direction, architecture, and delivery of large-scale machine learning systems for autonomous vehicle data. Develop autonomy algorithms, foundation models, deep scene understanding, causal models, high-quality datasets, and auto-labeling systems. Mentor senior engineers, define ML platform requirements, lead complex projects from conception through production, and coordinate integration across Uber engineering teams.

Designs and implements AI and machine learning capabilities for cyber and intelligence workflows. The role translates mission-user processes into LLM-powered, agentic, or hybrid solutions; integrates AI with APIs, enterprise data, and mission platforms; and ensures reliable, secure production deployment in classified environments. Collaboration with analysts, operators, developers, and leadership is central. Required qualifications include Python, TS/SCI with polygraph, and relevant AI/ML or software engineering experience.

Design and productionize machine learning, deep learning, LLM, personalization, recommendation, search, and retrieval systems. Collaborate with product, engineering, and analytics teams to deploy AI solutions that improve customer experiences, business growth, and operational efficiency. The role requires driving projects from ideation through deployment and delivering measurable business impact in a fast-moving fintech environment.

Leads the architecture, development, and operation of Disney’s AI and machine learning platform for finance. Responsibilities include managing a global engineering team, building production LLM applications and governed data foundations, defining AI/ML roadmaps, enforcing RBAC and engineering quality, guiding build-versus-integrate decisions, partnering with finance stakeholders, and driving enterprise adoption. The role is hands-on, involving architecture, implementation, technical reviews, code review, model evaluation, and delivery oversight.

Own the full lifecycle of production classification and detection models, including transformer fine-tuning, entity detection, behavioral risk scoring, training, serving, versioning, evaluation, drift detection, and retraining. Build SageMaker-based ML systems and define output contracts supporting audit and policy enforcement. Lead the evaluation harness and mentor a co-op while operating as the company’s sole ML engineer.

Build and calibrate an agent evaluation platform for multi-step LLM agent trajectories. Design rubrics, deterministic validators, LLM judges, confidence scoring, and human calibration workflows. Fine-tune judge models, analyze simulation-versus-production divergence, and develop process reward models for agent optimization. Establish versioned scenarios, simulated environments, and reliable measurement standards while maintaining production-grade Python code.

Owner of product features for Card Intelligence ML initiatives; collaborate with data scientists, engineers, and stakeholders to define roadmaps, break down epics into MVPs, prioritize features, and accelerate model-to-market delivery for fraud prevention.

The role requires developing machine learning models for cyber threat detection, optimizing response times, and utilizing innovative tools for cybersecurity applications.

Develop and maintain Retrieval-Augmented Generation pipelines, LLM integrations, REST APIs, and self-hosted LLM deployments. Conduct LLM alignment red teaming, independently research emerging AI/ML methods, and develop advanced AI applications. The role requires strong Python and AI/ML framework expertise, with preferred experience in model evaluation, fine-tuning, inference optimization, vector databases, AWS, Docker, and scalable data architectures.

Build scalable evaluation, introspection, visualization, and metrics tools for autonomous vehicle machine learning models. Develop workflows processing billions of examples and connect offline evaluation with data, training, simulation, and launch decisions. Collaborate with modeling, data, infrastructure, and validation teams to improve model performance, safety, reliability, and development speed. Responsibilities include software design, implementation, maintenance, code reviews, and production tooling across backend, frontend, and data infrastructure.

Develops and evaluates artificial intelligence and machine learning algorithms for U.S. Government mission systems. Responsibilities include selecting and analyzing data, training and improving models, conducting testing and validation, overseeing system integration, researching AI/ML methods, converting prototypes into scalable solutions, and verifying data and model quality. The role requires collaboration, independent work, and support for critical software and systems under a government contract.

Develop machine learning and generative AI methods to optimize chip layout for performance, power, and area. Automate physical design, verification, and exploratory PDK generation; build benchmarking, modeling, and QA systems; and evaluate advanced device architectures and process technologies for sub-2nm nodes. Collaborate with global R&D organizations and research consortia, transfer technology to Samsung’s Korea-based development teams, and guide cross-functional technical initiatives.

Own the technical vision and system architecture for Digit’s safety-critical perception systems in dynamic environments. Lead architectural tradeoffs across sensing, ML models, redundancy, latency, compute, and robustness. Define verification and validation standards, support safety cases and hazard analysis, evaluate emerging technologies, and collaborate with navigation, controls, hardware, and safety teams. Mentor senior engineers and represent perception architecture to executives, customers, regulators, and partners.

Build Moveworks’ agent evaluation and reward-modeling platform. Design configurable rubrics, deterministic validators, LLM judges, confidence scoring, and calibration against human-labeled trajectories. Analyze simulator-to-production divergence, maintain evaluation datasets, fine-tune smaller judge models, and develop process-level reward signals for optimizing prompts, tool selection, planning, retrieval, and routing. Establish versioned scenarios and reliable measurement standards for agent performance in stateful enterprise environments.

Develop and integrate production AI/ML capabilities into Oracle E-Business Suite on OCI for federal financial management. Build NLP and document-processing solutions, REST API integrations, ML lifecycle workflows, cognitive performance metrics, and data-mining capabilities. Participate in SAFe Agile delivery, author user stories, support DevSecOps integration, resolve technical impediments, and contribute to transition and knowledge-transfer materials. The role requires collaboration with architecture, product, and government teams in a cleared defense environment.

Develop AI/ML solutions for semiconductor design and verification workflows. Build scalable pipelines that extract and analyze EDA logs, reports, run artifacts, and design metadata. Create models, heuristics, and analytics for STA, DRC/LVS, ECO optimization, debug, and regression acceleration. Integrate solutions into CAD infrastructure, automation systems, schedulers, and design databases while collaborating with EDA vendors.

Develop and productionize AI/ML solutions for healthcare, focusing on multimodal LLMs, conversational AI, document understanding, search, and Q&A. Responsibilities include evaluating and fine-tuning models, building training data pipelines, managing the full ML lifecycle, deploying scalable solutions, troubleshooting integrations, documenting architecture, and ensuring HIPAA compliance. The role requires strong Python and PyTorch skills, experience with LLM training platforms and cloud ML services, and close collaboration in a fast-paced startup environment.

Leads an ML engineering team while designing, productionizing, and deploying AI systems for document understanding, search, Q&A, GenAI, virtual agents, NLP, NLG, and multimodal analysis. Oversees the ML lifecycle, APIs, code reviews, testing, troubleshooting, documentation, and healthcare data security. Manages projects, team performance, professional development, and cross-functional collaboration while ensuring HIPAA compliance and reliable, high-performance solutions.

Design, develop, and productionize machine learning systems for document understanding, search, Q&A, GenAI, virtual agents, and RAG applications. Build Python backend services, AI APIs, multi-agent architectures, retrieval infrastructure, and CI/CD pipelines. Manage the full ML lifecycle, including training, validation, deployment, telemetry, A/B testing, and automated evaluation. Collaborate cross-functionally while ensuring scalable, secure, HIPAA-compliant healthcare solutions.

Design, develop, and productionize machine learning and generative AI systems for document understanding, search, Q&A, virtual agents, and RAG applications. Build Python backend services, APIs, multi-agent architectures, retrieval infrastructure, and CI/CD pipelines. Manage the full ML lifecycle, evaluation, testing, telemetry, deployment, and troubleshooting while collaborating with cross-functional teams and maintaining healthcare data security and HIPAA compliance.