Top Machine Learning Engineer Jobs

Develop and deploy machine learning planning algorithms for autonomous trucks. Own the full ML lifecycle, including data curation, feature engineering, training, evaluation, deployment, monitoring, and improvement. Design trajectory and behavior-planning models, safety guardrails, validation pipelines, and performance metrics. Investigate failures and edge cases, optimize inference, and collaborate with perception, prediction, mapping, runtime, and systems teams to deliver reliable production systems.

Build and operate the ML execution platform supporting data generation, simulation orchestration, training, fine-tuning, benchmarking, and production deployments. Manage scalable compute, storage, networking, observability, reproducibility, CI/CD, and customer deployments across cloud, hybrid, and on-premises environments. Partner directly with customers and the founding team to deliver reliable, secure, and repeatable systems for industrial AI workloads.

Build and operate Kubernetes-based AI/ML infrastructure, including GPU scheduling, multi-tenant isolation, platform services, infrastructure as code, GitOps, CI/CD, observability, and reliability practices. Deploy reproducible platforms in cloud, restricted, and disconnected environments. Develop runbooks, support incident response, contribute to open-source infrastructure projects, and collaborate with security engineers and government stakeholders. The role requires production infrastructure experience, cloud expertise, programming skills, and eligibility for a U.S. security clearance.

Optimize large-scale machine learning training systems across CPUs, GPUs, and accelerators. Responsibilities include benchmarking, distributed-training optimization, GPU kernel and framework development, end-to-end pipeline efficiency, numerical optimization, and training infrastructure. The role partners with ML researchers, quantitative researchers, HPC engineers, systems engineers, and hardware specialists to improve performance, scalability, utilization, reproducibility, and cost across on-premises and cloud environments.

Build and operate a reliable ML platform for large-scale distributed GPU training. Responsibilities include Kubernetes infrastructure, developer-facing CLIs and SDKs, AWS infrastructure, data and artifact management, experiment tracking, model registries, CI/CD, observability, documentation, security guardrails, and platform adoption. The role partners closely with researchers and engineers to improve training speed, reliability, usability, and cost efficiency for autonomous transportation systems.

Develop and operate scalable machine learning runtime platforms and inference applications. Responsibilities include building Python-based frameworks, automation, microservices, infrastructure as code, containerized deployments, CI/CD and GitOps workflows, observability, capacity management, and distributed systems. The engineer partners with data scientists and ML engineers, owns features end to end, evaluates ML tooling, mentors team members, resolves operational issues, and participates in rotational on-call support for federal government environments.

Designs, develops, tests, and productizes scalable AI/ML algorithms and models. Responsibilities include model architecture, data pipeline integration, metrics analysis, deployment, scheduling, monitoring, retraining, and iterative improvement. The engineer will support mission-critical government systems, apply software engineering and model evaluation practices, communicate with customers, and potentially lead geographically distributed teams. The role requires advanced machine learning expertise, strong analytical and communication skills, U.S. citizenship, and an active or reinstatable TS/SCI with Polygraph clearance.

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.

Design, develop, and deploy reinforcement learning, behavior cloning, and imitation learning models for autonomous truck behavior planning. Own reinforcement learning pipelines from environment construction through training and modeling, contribute to distributed training infrastructure, analyze model performance and failure modes, and collaborate with simulation and planning teams to deploy models across driving scenarios.

Build ML infrastructure and tooling for large-scale model training, evaluation, tracking, deployment, and monitoring. Develop orchestration systems, model registries, automated deployment and continuous-training pipelines, testing environments, and drift detection for edge deployments in manufacturing. Collaborate with ML researchers, data engineers, and software engineers to create scalable, user-friendly research-to-production workflows.

Build and scale machine learning training, inference, and Generative AI systems for personalized text, image, audio, and video content. Define model-generation roadmaps, develop and operate models at scale, prototype innovative solutions, establish testing and fine-tuning processes, and partner with product, design, business, and engineering teams. Drive data-informed product development through feature and company-level metrics.

Design, develop, evaluate, and refine machine learning models using Python and leading ML frameworks. Analyze datasets with MongoDB, build preprocessing and data pipelines, tune hyperparameters, benchmark model performance, document experiments, and provide data-driven recommendations. Collaborate remotely with cross-functional contributors to improve and operationalize models in cloud or enterprise environments.

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.

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.

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.

Develop machine learning, statistical, graph-based, and data-mining algorithms for national-security missions. Build, optimize, and validate AI/ML and large language models; create feature vectors, experiments, simulations, visualizations, and predictive analytics; and implement prototypes in production frameworks. Collaborate with subject-matter experts to automate analysis, guide analytic development, and deliver data-driven insights. Experience with LLMs, RAG, agents, GPU computing, and active TS/SCI clearance with polygraph is required.

Architect and develop scalable AI-driven IVR, conversational, NLP, and LLM solutions for high-volume customer interactions. Build classification and clustering models, perform SQL-based analysis, design RAG and agentic workflows, and establish LLMOps practices for monitoring and lifecycle management. Partner with platform, speech engineering, and product teams while mentoring junior and offshore engineers.

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.

Develop, optimize, package, validate, and deploy AI/ML applications in secure, mission-critical environments. Collaborate with analysts and platform, infrastructure, and security teams; support MLOps, CI/CD, distributed systems, troubleshooting, performance tuning, accreditation, compliance, audits, and technical documentation. The role uses machine learning frameworks including TensorFlow, PyTorch, Scikit-learn, Keras, computer vision, and GANs.

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.

As a Machine Learning Engineer, you will develop machine learning models, automate data engineering pipelines, and support customer-centric projects while collaborating with data scientists and engineers.

Build and operate scalable ML infrastructure and platform capabilities across cloud and on-premises environments. Develop Python-based developer tooling, services, and production systems supporting experimentation, distributed training, model deployment, and operations. Lead complex initiatives from architecture through rollout, improve reliability and developer productivity, and partner with ML and infrastructure teams. The role emphasizes Kubernetes, AWS, distributed compute, GPU-intensive workloads, observability, and production excellence.

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.

Deploy and optimize computer vision, signal processing, and machine learning algorithms on GPU-enabled embedded systems. Improve real-time performance on constrained low-SWaP platforms, profile CPU/GPU bottlenecks, select algorithms based on hardware constraints, containerize deployments, and produce Linux-based production software. Collaborate with machine learning and software engineers to transition models into operational environments.