Top Machine Learning Engineer Jobs

Develop and implement AI/ML algorithms for defense and commercial applications, analyzing image, text, and graph datasets to build actionable models. Use cloud services to train and deploy models, apply statistical and machine learning techniques, review research, and create technical documentation. The role involves collaboration with software developers and researchers and requires an active TS/SCI clearance with CI Poly.

Build and lead full-stack software engineering initiatives using distributed microservices, cloud platforms, APIs, databases, and modern front-end technologies. Provide technical leadership across Agile teams, establish engineering standards, mentor developers, partner with architects and product managers, reduce technical debt, improve system performance, and influence long-term technology strategy. The role emphasizes AWS and other cloud technologies, open-source frameworks, CI/CD, automated testing, observability, and AI-assisted development tools.

Design, build, deploy, and scale production machine learning systems across data processing, training, evaluation, inference, and operations. Develop reusable infrastructure and services, integrate research innovations into reliable products, drive architecture, and solve scalability, performance, reliability, security, privacy, and Responsible AI challenges. Provide hands-on technical leadership and mentorship across complex engineering initiatives.

Design, deploy, and scale production AI/ML solutions for healthcare, including predictive models, generative AI, statistical analysis, microservices, APIs, and MLOps pipelines. Analyze complex clinical datasets, implement model monitoring and governance, evaluate emerging AI technologies, and collaborate with engineering, data, product, and clinical teams to improve care delivery and operational efficiency.

Build and operate production machine learning systems enabling autonomous medical coding at scale. Responsibilities include data discovery, feature engineering, model training and evaluation, deployment, monitoring, MLOps pipelines, LLM integration, observability, governance, fairness assessment, and A/B testing. The role also involves designing highly available low-latency architectures, defining technical roadmaps, and collaborating with product, design, data, and platform teams.

Leads enterprise AI application architecture and hands-on development for LLM, agentic, multi-agent, and RAG solutions. Establishes scalable design patterns, governance standards, observability, reliability, and compliance practices. Collaborates with product, engineering, and compliance teams, evaluates emerging AI technologies, optimizes performance, and mentors senior engineers. The role requires extensive software engineering and enterprise AI architecture experience, with expertise in cloud-native systems, Kubernetes, CI/CD, MLOps, and production AI platforms.

Design, build, deploy, and support production AI applications for complex enterprise workflows. Develop LLM, retrieval, agentic, API, and distributed-service capabilities; create evaluation, testing, tracing, and observability practices; investigate AI failures; and optimize quality, security, performance, maintainability, and cost. Collaborate with domain experts, architects, data teams, and software engineers to translate specialized knowledge into reliable, explainable, and maintainable solutions.

Architect and build Verizon’s enterprise AI control plane, integrating AI developer platforms with governance hubs. Develop APIs, CI/CD governance pipelines, responsible AI and security guardrails, agent simulation, observability telemetry, IAM, and secure routing for autonomous workflows. Collaborate with platform engineering, DevOps, DataOps, data science, and AI product teams to operationalize LLMs and agentic systems at enterprise scale.

Leads enterprise FinOps AI and cloud engineering initiatives, defining agentic AI architectures, MCP integrations, policy-as-code, IaC guardrails, automation pipelines, security controls, telemetry, and executive dashboards. Manages multi-workstream, multi-cloud delivery squads, scales reusable FinOps components, supports client consulting and sales pursuits, and ensures compliance with enterprise AI standards. The role requires extensive cloud, FinOps automation, program leadership, and agentic AI experience.

Design, train, evaluate, and optimize machine learning models for wireless network use cases and real-time edge inference. Build telemetry data pipelines, deploy containerized models on Kubernetes and GPU infrastructure, develop AI-driven network applications, and implement MLOps practices. Benchmark resource contention, ensure model security and provenance, document results, and collaborate with wireless, RF, cybersecurity, and government stakeholders. The role requires hands-on lab work, technical communication, and eligibility for a Secret clearance.

Build and improve ML and LLM systems for CustomerLake personalization use cases, including recommendations, targeting, churn, and lifetime-value modeling. Develop production evaluation and monitoring frameworks tied to business outcomes, inspect model behavior, and advance novel methods. Partner with product, engineering, and design to create scalable solutions, establish ML/AI best practices, and lead ambiguous 0-to-1 initiatives from concept through launch and adoption.

Design and build production-grade AI memory systems, including retrieval pipelines, storage tiers, privacy filtering, memory extraction, adaptive updates, hybrid search, and evaluation frameworks. Ship and maintain LLM-powered features, diagnose RAG and prompt failures, implement agentic workflows and tool integrations, and ensure reliable degradation and fallback strategies. Collaborate with product management to define feasible memory-system capabilities while owning production reliability.

Design and build realistic simulated workplace environments, evaluation tasks, benchmarks, graders, and scoring systems for training and assessing AI models. Generate synthetic records, files, and communications; prevent models from exploiting evaluation systems; align benchmarks with real-world performance; and convert user feedback into new test cases. The role is early-career and emphasizes reinforcement learning or model post-training experience.

Develops predictive models and data-driven solutions by analyzing complex datasets and applying advanced statistical and machine learning techniques. The role communicates insights to stakeholders, collaborates with cross-functional teams, and integrates analytics into business strategy. It requires extensive data analysis experience, an active TS/SCI clearance with polygraph, and U.S. citizenship. The position is onsite in Herndon, Virginia, with less than 10% travel.

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.

Designs, builds, deploys, and operates mission-grade AI/ML capabilities, including scalable data pipelines, model training, REST inference services, cloud-native MLOps, and production monitoring. The role manages AWS workloads, containerized deployments, infrastructure as code, DevSecOps, data governance, encryption, compliance evidence, and end-to-end model lifecycles. It applies supervised, unsupervised, reinforcement learning, NLP, computer vision, and generative AI to mission use cases in accredited cloud environments.

Develop scalable machine learning software for virtual screening pipelines that integrate ML with physics-based methods such as docking and free energy perturbation. Collaborate with research scientists to adapt workflows, create new discovery methodologies, benchmark novel ML approaches, and improve drug discovery accuracy. The role requires production software development, Python, PyTorch, and knowledge of machine learning applied to biological or chemical systems.

Design, develop, deploy, and optimize scalable machine learning models and workloads on cloud platforms. Build ML scoring processes, APIs, microservices, automated workflows, monitoring, and alerting. Support ML go-to-market initiatives and collaborate with Product and Engineering teams on compliant architecture and deployment. The role requires Databricks, cloud ML, end-to-end ML systems, data pipelines, FastAPI, data warehouses, Docker, Kubernetes, and strong software engineering practices.

Design, train, evaluate, and deploy production machine learning, deep learning, and reinforcement learning systems for semiconductor manufacturing. Build RL environments, reward functions, policies, agents, simulation workflows, and scalable MLOps pipelines. Research generative AI, LLMs, RAG, and multi-agent systems while partnering with software engineers, data scientists, cloud architects, and semiconductor experts to integrate intelligent capabilities into production solutions.

Build and scale pipelines that transform conversation data into structured customer memory. Develop LLM extraction and retrieval systems using embeddings, vector search, and ranking; create evaluation benchmarks and regression gates; diagnose production issues through logs, metrics, and traces; and partner with Product and ML teams to improve customer-focused AI systems.

Build and optimize scalable pre-training pipelines for foundation models. Implement distributed training across GPUs, TPUs, and high-performance clusters; translate research experiments into production workflows; develop monitoring and fault-tolerance systems; and benchmark and tune performance across hardware and software stacks.

Lead large-scale machine learning initiatives, develop and deploy production ML models, optimize data pipelines and distributed systems, and build cloud-based ML platforms. The role uses Python, Java, Go, Scala, and modern ML frameworks and requires technical leadership across architecture, modeling, infrastructure, and engineering talent development.

Lead large-scale machine learning initiatives, develop and deploy production ML models, optimize cloud-based data pipelines and infrastructure, and build intelligent systems using advanced algorithms and open-source frameworks. The role involves programming in multiple languages, distributed computing, Kubernetes, and cloud platforms. It also includes mentoring and recruiting engineering talent, evangelizing best practices, and driving technical innovation across the organization.

Join Waymo's Simulator Team to develop ultra-realistic sensor data and perception simulation for autonomous vehicles using advanced ML technologies. Collaborate with foundational AI research teams and drive innovation in simulation realism.

Support early-career AI/ML development for Intelligence Community missions by prototyping GenAI, LLM, NLP, and computer vision solutions. Responsibilities include configuring development environments, containerizing applications with Docker, integrating prototypes into CI/CD pipelines, deploying to AWS or Azure secure environments, preparing customer demonstrations, and documenting APIs and systems. The role requires hybrid work in Northern Virginia, including classified SCIF and customer facilities.