Top Machine Learning Engineer Jobs

Design, build, and optimize scalable data pipelines for large-scale structured and unstructured healthcare data to support ML and GenAI workloads. Implement ingestion, transformation, validation, orchestration, monitoring, and data foundations (feature pipelines, embeddings/vector stores). Collaborate with architects, data scientists, and DevOps to deploy and secure cloud-based pipelines while ensuring compliance with SOC 2, HIPAA, and GDPR. Mentor junior engineers and drive best practices in version control, metadata, reproducibility, performance tuning, and cost optimization.

Build infrastructure for AI/ML environments, collaborate with teams for improvements, and interact with customers to enhance product features.

As a Senior ML Infrastructure Engineer, you will design scalable architectures for ML, build robust data pipelines, manage GPU clusters, and drive improvements in CI/CD workflows while collaborating with cross-functional teams.

Build and productionize LLM-powered tools, RAG systems, and agentic workflows. Deploy and operate AI infrastructure on Kubernetes, manage model serving and vector stores, integrate identity and access controls, automate CI/CD and data pipelines, and establish evaluation and observability for deployed services in a defense-oriented environment.

Lead development and refinement of machine learning pricing models (especially reinforcement learning) for Airbnb hosts. Collaborate with data scientists, product, and engineers to design, test, integrate, and monitor models, analyze performance, and prioritize projects to improve host outcomes.

Design, develop, and test AI/ML and optimization software for energy research on HPC systems. Build and evaluate models (deep learning, probabilistic methods, large-scale inference), productionize solutions, collaborate with researchers, mentor juniors, author technical publications, and support multiple projects across the research-to-implementation stack.

Lead a team building large-scale ML model evaluation and orchestration frameworks for autonomous vehicles. Own data analysis workflows, dataset creation, statistical model evaluation, regression reporting, and tool development (AWS Kubernetes orchestration, ReSimulation). Provide technical leadership, mentorship, and manage project timelines and deliverables.

Architect, build, and operate cloud and on‑prem AI/ML training and inference platforms (including federated learning). Partner with scientists and external collaborators, benchmark and optimize ML infrastructure, ensure security/compliance and cost effectiveness, mentor engineers, and represent the Institute in vendor and cross‑institutional technical engagements.

Develop and deploy AI/ML algorithms and software for semiconductor inspection, metrology, and process control. Build scalable model training, validation, deployment, and inference systems; optimize performance with GPU acceleration and parallel computing; collaborate across teams to integrate AI into production, cloud, and edge platforms; evaluate emerging AI technologies and deliver proof-of-concepts while maintaining software quality.

Assist in deploying, monitoring, and maintaining machine learning models and infrastructure. Support data curation, implement and test ML algorithms, help build CI/CD pipelines, automate training/deployment, troubleshoot production issues, and follow organizational security and safety practices while working under supervision.

Design, build, and deploy production ML models (time series forecasting and NLP). Perform EDA, feature engineering, model evaluation, and translate business problems into ML solutions. Maintain production model-serving pipelines and collaborate across teams to deliver scalable data-driven systems.

Design and implement software solutions with cross-functional teams; produce technical specifications and documentation; write, test, deploy code; perform code reviews; provide technical support; collaborate on QA/testing; stay current with tech trends and recommend integrations.

Contractor role to design, harden and productionize ML and LLM systems for enterprise clients: build data/training pipelines, MLOps (deployment, CI/CD, monitoring, versioning), RAG and retrieval systems, benchmarking and cost control, short audits, and client-facing advisory work. Work as part of Janea teams on time-bound engagements.

Lead applied AI research to design and prototype Transformer-based Payment Foundation Models and generative AI solutions for fintech use cases. Drive self-supervised pre-training, fine-tuning, model optimization, benchmarking, and evaluation; collaborate cross-functionally to transition prototypes to production and ensure responsible, secure AI deployment while mentoring engineering teams.

Lead development and deployment of cutting-edge ML models for pricing, design and analyze experiments, collaborate with product and engineering, present findings to stakeholders, and lead technical projects to influence team output and business metrics.

Lead the design and delivery of a production-grade localization ML stack for autonomous vehicles: architect models for landmark-based localization across sensors, own data and evaluation pipelines, drive feature development to deployment, define failure modes and safety coverage, and mentor engineers while integrating cutting-edge multimodal and foundation-model techniques into robust, real-time localization systems.

Design, build, and operate production agentic AI systems on AWS Bedrock AgentCore using Strands/LangGraph. Implement tool integrations (MCP/Gateway), evaluation pipelines and LLM-as-judge CI gates, observability, cost/latency controls, and guardrails for agents operating on live dealer data.

Build end-to-end ML software for autonomy foundation models: data pipelines, training/evaluation workflows, model implementation (LLM/VLM/VLA), simulator integration, and low-latency serving and inference tooling for vehicle deployment.

Design, train, deploy, and monitor ML/AI models and MLOps pipelines (including NLP, LLM, RAG, generative AI). Optimize performance, integrate models into applications/APIs, ensure security, documentation, and collaboration with engineers, analysts, and SMEs for federal missions.

Ownership of hardened batch pipelines, feature infrastructure, model governance (versioning, lineage, reproducibility), and third-party data integrations. Implement MLOps tooling for consistent, auditable deployments, keep systems audit-ready, and integrate surveillance, KYC, screening, market, and blockchain data sources in a regulated environment.

Build and maintain low-level networking software that interfaces accelerators and high-speed networks. Diagnose and optimize kernel- and user-space networking (layers 2-4), implement collectives/RPC, benchmark and improve congestion control, and develop drivers and protocols to reduce latency for distributed ML workloads.

Design, automate, and manage end-to-end ML pipelines and scalable infrastructure on Azure. Build CI/CD with GitHub Actions, containerize models with Docker and AKS, monitor model lifecycle and data drift, implement cost controls, and collaborate with data science, DevOps, and IT to deploy resilient, production-grade ML solutions.

Design and operate scalable infrastructure and automation for real-time video ML workloads. Build Kubernetes orchestration, CI/CD pipelines, IaC, monitoring, and high-performance Linux/networking systems for cloud and edge deployments, collaborating with ML engineers to validate and scale model deployments.

Develop and apply AI/ML techniques for anomaly detection in cybersecurity, build and optimize models and tools using Python, Pandas, and C/C++, analyze and troubleshoot security events, work with embedded DoD devices, and support on-site activities with quarterly travel while maintaining an active Secret clearance.

As a Machine Learning Engineer, you'll build ML infrastructure, develop scalable data pipelines for farm images, and collaborate with farmers and agronomists to apply ML solutions.