Top Machine Learning Engineer Jobs

The role involves developing and maintaining behavioral models for road users to enhance autonomous vehicle decision-making, collaborating with other teams and optimizing outputs for safety and effectiveness.

The Senior Machine Learning Engineer at Grailed will enhance personalization and recommendations, develop AI/ML solutions, and collaborate across departments for data-driven insights.

As an AI/ML Engineer, you'll implement machine learning techniques, build AI models, and support national security applications, collaborating with various teams.

Design, build, and deploy production-grade AI/ML systems (NLP/LLMs, computer vision, generative AI). Create scalable data pipelines, microservices/APIs, and cloud- or edge-deployed models. Ensure security, observability, and performance; perform model optimization, versioning, A/B testing, and documentation. Collaborate with engineers, mission SMEs, and product teams to meet operational requirements.

Design and lead development of ML and AI solutions: explore, clean, analyze structured and unstructured data, build predictive models, apply NLP/text-mining techniques, integrate enterprise systems, visualize results, and advise clients while guiding teammates.

Lead a Labs team as a hands-on engineer building hybrid AI/optimization systems: design ML-augmented optimization, prototype and productionize solutions, architect MLOps/CI-CD, benchmark solvers, mentor engineers, and collaborate with researchers and stakeholders to scale reusable components and APIs across Kroger.

Lead architecture and implementation of Vumedi's AI discovery platform: design personalized recommendation systems, semantic search, retrieval and ranking pipelines, vector/embedding-based search, RAG/knowledge-graph integrations, scalable inference and retrieval services, and evaluate emerging AI technologies while partnering with product, data science, and engineering to deliver production LLM-powered discovery for physicians.

Build and maintain MLOps pipelines, deploy and monitor ML models as scalable services, transform and integrate healthcare data (FHIR/HL7), ensure reproducible feature management, and uphold HIPAA/HITRUST security for production ML in clinical systems.

Design, build and deploy production AI/ML solutions from complex, distributed farm data. Engineer features, select models, run experiments, move models to scalable real-time production, monitor and observe AI systems, evaluate emerging AI technologies, and collaborate with product, engineering and domain teams. Contribute to user-facing features with React/TypeScript as needed.

Design, build, and deploy production-grade AI/ML solutions that convert complex, distributed farm data into actionable intelligence. Engineer features, select models, run experiments, and move models to scalable, real-time production. Integrate, fine-tune, monitor, and observe models on cloud infrastructure (AWS, Databricks). Partner with product, engineering and domain teams and contribute to user-facing experiences when needed.

Design and build scalable document intelligence ML systems using LLMs, NLP, computer vision, and traditional deep learning. Develop entity extraction, resolution, and classification models; create ML pipelines for preprocessing, training, and inference; perform EDA; collaborate with engineers and product teams; and maintain production-grade, testable code.

Design, build, and scale retrieval-augmented generation (RAG) systems: chunking, embeddings, vector search, LLM orchestration, and production ML pipelines. Work cross-functionally to deploy robust retrieval, indexing, and evaluation solutions over large proprietary datasets, collaborating with MLOps, Product, and Design to productionize and maintain systems.

Build and productionize agentic AI systems that retrieve and synthesize structured and unstructured financial data. Work across the ML lifecycle: experiment, deploy, monitor, and evaluate agents and retrieval methods. Collaborate with data, product, design, and MLOps teams to create scalable, robust solutions that connect data to business insights.

Lead data science work with clients: analyze structured and unstructured data, develop predictive models and NLP solutions, visualize results, integrate enterprise systems, and guide teammates to deploy AI/ML workflows that inform decisions.

Design, develop, and deploy AI/ML models and pipelines for chemical and biological threat characterization. Build data preprocessing, embedding, entity extraction, and model monitoring systems; integrate models into secure cloud environments via APIs and MCP. Create model cards, explainability tools, and support knowledge graphs. Ensure DoD cybersecurity compliance, participate in Agile teams, and assist knowledge transfer and documentation.

Design and implement advanced ML models (reinforcement learning, neural nets, metaheuristics) for logistics optimization, predictive analytics, anomaly detection, and real-time decision systems. Lead architecture design for scalable, secure AI systems, mentor teams, align technical roadmaps with defense requirements, and conduct feasibility studies and technology evaluations for contested and uncertain logistics environments.

Develop and deliver production-ready 3D and camera-based perception ML models (BEV emphasis). Own model training, evaluation, debugging, and integration into autonomy systems. Improve training pipelines, tooling, and robustness; lead small technical initiatives and mentor junior ML engineers while collaborating cross-functionally to deploy perception models at scale.

Develop software and machine learning algorithms for human-computer interface products. Design LLM-based system architectures for conversational, retrieval, and automation tasks. Use data science and statistics to analyze and optimize model performance. Build and optimize systems for latency, reliability, and cost for real-time ML applications.

Design, develop, and deploy machine learning models and scalable MLOps pipelines. Collaborate with data engineers and product teams to build, monitor, and optimize production ML systems, document experiments, and identify ML opportunities.

Build and evaluate next-generation ranking and recommendation models using LLMs, RLHF, and preference learning. Develop large-scale user understanding systems, apply RL and optimization to bidding and delivery, design experiments with causal inference and A/B testing, and collaborate with engineering to deploy research into production while communicating results to technical and non-technical stakeholders.

Optimize and deploy multi-modal models (transformers, diffusion, VLMs) to run on-device across mobile and desktop. Own export, quantization, kernel tuning, runtime integration, and profiling to meet strict latency, memory, and power budgets. Build packaging, benchmarking, and telemetry; partner with research to productionize models and mentor junior engineers.

Lead technical direction and implementation of computer vision and multi-modal ML models (transformers, diffusion, VLMs, JEPA) from research to production. Define model architecture, training, evaluation, and deployment across cloud and on-device targets. Mentor engineers, set engineering standards, partner with research and platform teams, and optimize for latency, memory, and cost to deliver scalable AI-driven game experiences.

Lead end-to-end on-device ML inference and integration for real-time game experiences: optimize and export models, tune kernels and runtimes (WebGPU/native), set performance standards, mentor engineers, and build CI/benchmarking and deployment pipelines to meet strict latency, memory, and power budgets.

Build and maintain large-scale user understanding, conversion prediction, and ad ranking models. Monitor and calibrate model performance, address data drift and delayed feedback, and improve recommendation and bidding strategies. Set long-term technical direction with other leads and communicate complex technical concepts to non-engineering stakeholders.

Design, develop, and deploy Generative AI and ML solutions (LLMs, RAG, agentic frameworks) for healthcare claims adjudication. Build end-to-end pipelines, apply MLOps, ensure LLM security and responsible AI, deploy on cloud platforms, integrate data warehousing, collaborate with product and platform teams, and mentor junior engineers.