Top Machine Learning Jobs

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.

Develop and productionize applied AI/ML solutions for healthcare, including multimodal LLMs, document understanding, search, Q&A, and conversational AI. Responsibilities include evaluating and fine-tuning open-weight models, building ML data pipelines, managing model lifecycles, troubleshooting deployments, ensuring HIPAA compliance, and driving technical innovation across the team.

Own the production serving path for multimodal AI models, including Rust inference runtime nodes, GPU kernels, routing, batching, streaming, quantization, observability, and low-latency optimization. Productionize research checkpoints and improve GPU utilization, numerical precision, latency, and cost while collaborating closely with researchers.

Own the production lifecycle for machine learning and LLM-backed systems, including training and post-training pipelines, inference serving, model releases, monitoring, retrieval infrastructure, and cost and latency optimization. Partner with AI researchers and engineering teams to move prototypes into reliable production capabilities across customer tenants. Develop ETL and GraphQL features, enforce tenant isolation, and support deployments in regulated or customer-controlled environments.

The Senior Applied Scientist will design and optimize credit risk models using machine learning, collaborating with cross-functional teams to translate business problems into technical solutions and drive scalable risk decisions.

Develop and implement machine learning models for semiconductor applications, including signal processing, sensor data analysis, and edge computing. Responsibilities include feature engineering, data preprocessing, model evaluation and optimization, ML pipeline development, and collaboration with data scientists and engineers. The role requires Python proficiency, machine learning framework experience, data modeling skills, and knowledge of ML deployment processes.

Build and deploy reliable AI agents that automate client workflows and integrate with enterprise systems. Responsibilities include mapping processes, designing automation roadmaps, developing evaluation frameworks and regression tests, monitoring agent performance, instrumenting audit trails, scaling systems to production, and leading workforce adoption and change management. The role also codifies reusable patterns across engagements and requires customer-facing collaboration, production AI experience, Python or TypeScript development, Secret clearance, and up to 10% travel.

Design, develop, test, evaluate, and deploy AI/ML models across generative AI, classification, prediction, computer vision, NLP, and reinforcement learning. Provide AI/ML engineering support using algorithms, software development, and cloud computing. The role requires five years of AI/ML development, cloud-based deployment, and Python or R experience, plus U.S. citizenship and an active TS/SCI clearance with polygraph.

Lead and contribute to Waymo’s planet-scale ML data infrastructure and inference platform. Partner with modeling teams to manage data quality, distributions, freshness, feature stores, and ML flywheels. Mentor engineers, lead complex cross-functional projects, and support scalable data stores, model hosting, bulk and online inference, and high-QPS production services.

Designs, deploys, and scales production AI agents and agentic workflows within enterprise Java and Spring applications. Establishes evaluation, monitoring, guardrail, safety, testing, observability, deployment, and operational standards for LLM systems. Leads architectural decisions across AI services and microservices, productionizes models, evaluates emerging technologies, mentors engineers, and influences technical strategy across teams.

Build and deploy production AI agents, agentic workflows, and ML-enabled enterprise features within Java/Spring microservices. Implement evaluation, monitoring, guardrails, safety, testing, and observability for LLM applications. Collaborate with AI scientists, engineers, product managers, and technical specialists to productionize models, integrate RAG and tool calling, prototype solutions, and improve AI capabilities. The role also involves design reviews, agile planning, software quality ownership, and staying current with emerging AI technologies.

Lead the architecture and development of Root’s machine learning platform for insurance pricing. Build infrastructure for feature pipelines and stores, reproducible training and orchestration, model registries, serving, validation, observability, and research-to-production workflows. Establish versioned contracts and technical standards, automate workflows using LLMs, write and review critical code, influence cross-team strategy, and mentor senior engineers. Collaborate closely with data scientists and researchers to create scalable, reliable, and reproducible ML capabilities.

Build end-to-end machine learning systems spanning research reproduction, model evaluation, inference infrastructure, backend services, and product interfaces. Design rigorous experiments, evaluation datasets, and verification workflows; analyze model internals; investigate provenance and evasion; and convert findings into reliable production systems with APIs, background jobs, observability, testing, documentation, and user-facing React/TypeScript experiences.

Own the architecture and hands-on development of Fanatics’ enterprise AI/ML platform, including the LLM gateway, agent and MCP runtime, retrieval infrastructure, evaluation systems, governance, and cost analytics. Set technical standards, make build-versus-buy decisions, deliver scalable platform capabilities, improve observability and security, mentor engineers, and communicate architectural trade-offs to senior leadership.

Build production machine learning benchmarks and evaluation systems for foundation models. Own backend infrastructure including data pipelines, execution environments, storage, and orchestration, as well as sandboxed environments for agentic evaluations. Partner with researchers, enterprise customers, and company leadership to scope and deliver technical solutions. Identify scalable evaluation patterns, communicate clearly, and operate effectively in an ambiguous, high-ownership startup environment.

Develop machine learning models to predict printability of complex CAD geometries using image, thermal, simulation, and sensor data. Design and maintain structured and unstructured data systems supporting diverse data ingestion, storage, and retrieval. Deploy models into production in collaboration with preprint, process, and applications teams. Experience with geometric data, production software engineering, and Python or C++ is required; powder bed fusion and manufacturing data workflows are preferred.

Designs, deploys, and supports production AI/ML solutions across data ingestion, feature engineering, model training, MLOps, monitoring, and lifecycle management. Builds AWS cloud-native applications, generative AI systems, knowledge graphs, and real-time or batch pipelines. Partners with stakeholders on analytical solutions, ensures data quality and AI governance, leads technical design, and mentors engineers.

Train and fine-tune multi-task transformer models for autonomous driving using JAX/Flax on TPUs. Build data and evaluation pipelines, conduct rigorous model evaluations, contribute production-quality code, and communicate findings through design documents and presentations. Collaborate with engineering, data science, and evaluation stakeholders.

Design and operationalize AI/ML capabilities for secure DoD environments, including agentic workflows, production APIs, MLOps, model deployment, monitoring, validation, and continuous improvement. Integrate structured and unstructured data with Foundry applications and mission systems. Collaborate with engineering and mission teams, troubleshoot production systems, and apply responsible AI, security, governance, and model-management practices.

Designs, develops, tests, deploys, trains, monitors, and improves machine learning models for business and national security mission requirements. Responsibilities include selecting datasets, statistical analysis, algorithm execution, model evaluation, data pipeline integration, system testing, and scalable solution development. The role requires strong understanding of AI/ML architectures, application development, infrastructure, data engineering, and governance, with potential leadership of distributed teams.

Develop backend features, automated workflows, APIs, streaming services, and machine learning pipelines using Python, Docker, Kubernetes, and AWS. Collaborate on deep learning and computer vision applications, improve system performance and reliability, resolve bottlenecks, and follow software development and coding best practices in an Agile environment. The role requires an active Top Secret/SCI clearance and supports defense and intelligence missions.

Designs, develops, evaluates, and deploys machine learning models for sensor-data event detection, classification, and characterization in classified national-security environments. Builds end-to-end ML pipelines, integrates capabilities into operational software, evaluates failure modes, collaborates with scientists and analysts, and mentors junior developers. The role requires production-quality Python, deep knowledge of modern ML architectures, technical documentation, and active Top Secret clearance with eligibility for SCI and CI Polygraph.

Develop and deploy computer vision and machine learning models for semantic and instance segmentation, object and damage detection across 2D and 3D data. Responsibilities include training loops, dataset curation, labeling guidelines, evaluations, ablation experiments, model export, inference code, testing, profiling, and production support. Collaborate with research, backend, operations, and labeling teams while documenting experiments and improving model performance.