Top Senior Machine Learning Jobs

Leads and develops a team of autonomy engineers building multi-agent resource allocation, planning, coordination, and edge AI systems for spacecraft. Provides technical oversight on optimization, reinforcement learning, validation, architecture, and constrained real-time deployment. Owns team roadmaps, delivery, cross-functional dependencies, process improvement, and stakeholder communication while managing hiring, performance, career development, and team culture. The role bridges autonomy expertise with spacecraft constraints including compute, latency, power, and communication limitations.

Designs and develops AI/ML observability systems, large-scale data processing infrastructure, storage, data movement, and machine learning platform capabilities. The role supports reliable training, deployment, monitoring, model evaluation, explainability, and Generative AI use cases. Responsibilities include technical leadership, performance optimization, reliability improvements, database and compiler development, enterprise software engineering, Java application development, SQL, testing, debugging, and documentation.

Develops and adapts large language models and NLP methodologies to create intelligence-analysis capabilities. Responsibilities include building, tuning, evaluating, and deploying AI/ML models; preparing data; using deep learning frameworks and GPUs; developing semantic search and text-focused solutions; and communicating modeling results. The role requires strong Python or R, SQL, version control, cloud, and machine learning experience, along with an active TS/SCI clearance with polygraph and onsite work in McLean, Virginia.

Lead design and delivery of end-to-end revenue optimization products using forecasting, ML/reinforcement learning, and optimization. Partner with cross-functional teams to identify opportunities, prototype solutions, implement analytics, and support deployment while developing the analytics team and driving commercial impact.

Lead the development and production deployment of applied machine learning and LLM solutions across product, marketing, and operations. Own ranking, recommendation, filtering, personalization, experimentation, and model evaluation systems. Partner with business and technical teams to identify valuable use cases, establish modeling best practices, and ensure reliable production deployment. Manage and mentor applied ML practitioners while maintaining technical rigor, business alignment, and measurable customer and revenue impact.

Leads engineering and machine learning teams building enterprise-scale AI Search, retrieval, ranking, recommendation, personalization, and RAG systems. Responsibilities include setting architecture and strategy, deploying production ML models, improving relevance and performance, driving reliability and observability, partnering with product and engineering stakeholders, and mentoring technical teams delivering AI-powered experiences to millions of users.

Lead the design, development, deployment, and operation of production AI and ML systems. Build high-throughput Python services and Databricks pipelines, support traditional ML and LLM applications, establish testing and deployment standards, and develop monitoring for quality, reliability, latency, throughput, and cost. Partner with product, research, and responsible AI teams on governance and release criteria. Provide architectural leadership, conduct code reviews, mentor engineers, and resolve complex technical dependencies.

Research Engineer responsible for developing, training, evaluating, and deploying healthcare AI models and pipelines for conversational voice agents. The role focuses on instruction following, tool calling, retrieval, memory, speech and audio research, model optimization, benchmarking, failure analysis, and production integration. Candidates should have 5+ years of experience, including AI/ML engineering or research, production LLM deployment, Python proficiency, and a record of converting research prototypes into reliable measurable improvements.

Develop and optimize on-device AI capabilities for NVIDIA’s GeForce G-Assist. Evaluate Small Language Models for accuracy, robustness, conversational reliability, and contamination issues. Build multimodal and hybrid local-cloud architectures, optimize llama.cpp inference using C/C++, and integrate retrieval-augmented generation and agentic workflows involving planning and tool use. Collaborate with engineering and product teams to balance model performance, system reliability, and user experience.

Lead ML training performance for large-scale VLM, VLA, foundation-model, and reinforcement-learning systems. Design distributed parallelism, mixed-precision strategies, gradient checkpointing, GPU kernels, profiling workflows, and performance benchmarks. Own RL optimization strategy and roadmap, diagnose bottlenecks across compute and networking layers, partner with research and engineering teams on architecture, and mentor engineers.

Build and ship production AI systems for enterprise portfolio and planning workflows. Develop anomaly and signal detection, LLM-powered insight synthesis, natural-language planning rule translation, evaluation frameworks, and MCP tools. Partner with external AI providers initially, then establish internal standards for prompt design, model selection, agent architecture, reliability, and evaluation. Collaborate closely with product, design, and domain engineering teams.

Develop and productize machine learning prediction systems for autonomous driving. Responsibilities include designing end-to-end prediction solutions, researching deep learning models, supporting diverse driving environments, prioritizing data collection and labeling, using simulation and augmentation, and meeting safety, latency, and software robustness requirements. The role also involves collaboration with data teams and deployment of real-time ML solutions to embedded platforms.

Designs, trains, evaluates, and deploys reinforcement learning systems for complex decision-making problems. Responsibilities include developing RL algorithms, reward functions, simulation environments, neural network policies, and large-scale training pipelines on GPU clusters. The role requires taking RL solutions from research into production while ensuring stability, safety, and continuous improvement, with opportunities to work on RLHF, multi-agent or hierarchical RL, robotics, control systems, and autonomous driving.

Designs, builds, and operates reliable, high-performance inference platforms for large machine learning models in production. Responsibilities include request routing, batching, caching, autoscaling, GPU utilization, observability, performance engineering, capacity planning, and incident response. The role requires expertise in distributed systems, ML serving frameworks, Kubernetes, cloud platforms, and production services optimized for latency, throughput, cost, and quality.

Leads architecture and implementation of enterprise AI and data platforms supporting vector search, RAG, LLM, and Agentic AI applications. Establishes data governance, lineage, traceability, monitoring, and evaluation controls; drives technical decisions around scalability, security, reliability, and cost. Partners with engineering, data science, cybersecurity, and mission teams, communicates technical tradeoffs to executives, evaluates emerging technologies, and mentors engineers. The role requires active TS/SCI with CI polygraph clearance and extensive cloud, data engineering, MLOps, and AI platform experience.

Leads and manages ML engineers working across computer vision, robotics, agentic workflows, and ML platform infrastructure. Owns technical direction, hiring, team development, roadmap commitments, production deployment, monitoring, incident response, reliability, and cross-functional collaboration with product and software engineering teams. This player-coach role requires hands-on oversight of production ML systems, platform architecture, evaluation, safety, and operational quality.

Develop and deploy production-grade AI and machine learning systems using LLMs, RAG, and agentic AI frameworks. Build scalable data foundations, APIs, and cloud infrastructure while implementing monitoring, governance, observability, and MLOps practices. Partner with technical and business teams to deliver AI solutions for lead generation, recommendations, coverage optimization, and client engagement. Evaluate emerging technologies and ensure solutions are reliable, secure, maintainable, and aligned with business outcomes.

Own the architecture and engineering of production AI/ML infrastructure powering Relay. Build model-serving, inference, evaluation, orchestration, deployment, observability, and lifecycle systems. Optimize model performance, latency, throughput, scalability, reliability, and cost while establishing feedback loops for continuous quality improvement. Partner with engineering and product teams to advance agentic AI capabilities and make foundational architectural decisions in a rapidly evolving environment.

Lead development and productionization of personalization and recommendation algorithms (ranking, retrieval, content/user understanding, post-ranking). Apply modern AI/LLM techniques, design and run offline and online experiments, drive technical vision and roadmap, collaborate with engineering/product/data teams, and build maintainable, production-grade ML systems at scale.

Lead and mentor an agile engineering team to design, build, test, and operate secure, cloud-native production software. Provide technical direction, deliver high-quality code, drive product design decisions, and advocate firmwide engineering practices while applying AI/ML expertise.

Designs, develops, tests, deploys, monitors, and continuously improves machine learning models for complex business and national security mission requirements. Integrates data pipelines, evaluates model performance, applies statistical analysis, ensures data and output quality, and transforms prototypes into scalable production solutions. Provides system integration oversight and may lead distributed teams. Requires advanced education or equivalent experience and an active TS/SCI clearance with polygraph.

Designs, operates, and improves large-scale AI infrastructure for GPU training and inference. Responsibilities include managing accelerator clusters, distributed training frameworks, scheduling, high-performance storage, networking, observability, fault tolerance, security, automation, cost optimization, and developer tooling. The engineer partners with research and machine learning teams on capacity planning and reliable production workflows across on-premises, cloud, and hybrid environments.

Set technical direction for production AI agents, LLM and vision-language model post-training, evaluation, MLOps, and data improvement systems. Architect agent orchestration, retrieval, observability, and failure recovery; establish quality and release standards; and ship measurable model improvements. This hands-on individual contributor will co-own the ML roadmap, lead technical reviews, mentor engineers, support hiring, and implement privacy-conscious, HIPAA-aware practices.

Designs, develops, and improves AI and machine learning models for diverse R&D projects, including generative AI, predictive maintenance, data intelligence, and bioinformatics. Collaborates with technical teams and customers to define requirements, solve problems, conduct research, and deliver software solutions. Requires strong programming, analytical, mathematical, communication, and problem-solving skills, along with the ability to obtain and maintain a U.S. government security clearance.

Leads enterprise AI architecture for generative AI, agentic systems, traditional machine learning, and computer vision. Designs secure, scalable platforms; establishes MLOps and LLMOps standards; prototypes and writes production code; leads client solutioning and pre-sales; ensures regulatory compliance; mentors architects and engineers; and governs technical standards across strategic engagements.