Top Machine Learning Engineer Jobs

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.

Designs, develops, deploys, and operates intelligent automation solutions using AI/ML, open-source LLMs, and MCP integrations. Builds document-processing and data-cleansing bots, agentic workflows, and reusable automation components. Owns MLOps pipelines, model retraining, governance, token optimization, monitoring, troubleshooting, and performance tuning. Partners with stakeholders to identify automation opportunities, ensures security and compliance, and evaluates emerging models and frameworks.

Designs, develops, deploys, and supports full-stack intelligent applications integrating AI/ML models with business workflows. Builds reusable document processing, data extraction, classification, and data cleansing solutions; develops MCP servers and tools; manages model deployment, monitoring, retraining, and optimization. Troubleshoots production issues, performs root cause analysis, maintains operational documentation, and collaborates with business, data, infrastructure, security, and platform teams.

Design and implement AI/ML solutions for cyber and intelligence workflows, including LLM-powered workflows, agent-based automation, and API or enterprise data integrations. Partner with mission users, analysts, developers, and leadership to document operational processes, build reliable production capabilities, and meet classified-environment security and governance requirements.

Build and maintain scalable AI/ML web applications, backend systems, data pipelines, and model evaluation tools for scientific and drug discovery workflows. Partner with scientists, product managers, and engineering teams to translate user needs into technical solutions. Contribute to AI/ML algorithm development, cloud-native architecture, code reviews, performance optimization, and emerging GenAI and agent-based capabilities.

Build machine learning infrastructure spanning large-scale data generation, distributed multi-GPU model training, training and inference optimization, and production LLM deployment and monitoring. The role also contributes to AI detection research and innovation. Candidates need strong Python and modern ML framework skills, deep learning experience, and knowledge of transformers and LLM fundamentals.

Build machine learning infrastructure spanning large-scale data generation, distributed multi-GPU training, model optimization, and production LLM inference. The role also contributes to ML research and innovation, requiring strong Python and modern ML framework skills, transformer and LLM knowledge, and comfort bridging research and engineering. Preferred experience includes CUDA, distributed training, vLLM, Spark, Beam, Airflow, MLOps, DevOps, and cloud infrastructure.

Productionalize and optimize machine learning systems for scalable, low-latency solutions. Responsibilities include assessing ML pipelines, improving coding and deployment processes, automating model delivery, configuring cloud infrastructure, monitoring metrics, responding to incidents, troubleshooting platforms, and deploying models across environments. The role collaborates with data scientists and engineers to deliver production-ready models and performance metrics.

Develop AI agents, chatbots, and machine learning models for a modernized federal data science platform. Research and standardize AI capabilities in Databricks and Palantir, prompt-engineer LLMs, integrate AI into DevOps pipelines, and train models on production datasets for prediction and anomaly detection. The role requires collaboration with developers and customers, database familiarity, Agile experience, documentation, and an active or obtainable DoD Secret clearance.

Develop core machine learning algorithms for unstructured data, including representation learning, clustering, outlier detection, active learning, and label-quality improvement. Apply deep learning research to customer problems, collaborate on product scalability, evolve the engineering stack, and mentor team members. The role uses Python and modern ML infrastructure across AWS, Kubernetes, FastAPI, and related technologies.

Build and lead production AI/ML, Generative AI, and data engineering solutions for healthcare analytics and patient safety platforms. Develop full-stack applications, microservices, APIs, data pipelines, dashboards, self-service analytics, RAG systems, and event-driven architectures using cloud-native technologies. Apply engineering best practices including testing, CI/CD, observability, security, and performance optimization. Mentor engineers, lead technical initiatives, and collaborate with product, analytics, business, and platform teams.

Build and scale core ML platform infrastructure for model training, deployment, feature storage, real-time retrieval, inference, and monitoring. Lead architecture and technical execution, write production backend code, conduct design and code reviews, mentor engineers, improve reliability and cost efficiency, and partner with ML, product, data science, infrastructure, and security teams on platform strategy and roadmap.

Lead the design, development, deployment, and operation of machine learning models and platforms at scale. Build distributed ML infrastructure, data pipelines, cloud-based architectures, and production services. Develop and test application code, automate deployment, monitor and retrain models, and apply CI/CD, governance, security, and responsible AI practices. Collaborate with Product, Data Science, and Agile teams to solve complex business problems.

Design, build, deploy, and operate machine learning models, platforms, and data pipelines at scale. Develop production ML software, cloud architectures, distributed systems, and automated testing and deployment workflows. Monitor and retrain models, optimize infrastructure, ensure responsible and explainable AI practices, and collaborate with Product, Data Science, and Agile teams to solve complex business problems.

Build and deploy enterprise agentic AI solutions, including agents, MCP tools, plugins, orchestration architectures, evaluation harnesses, and secure APIs. Integrate agents with search and collaboration platforms, instrument production behavior, manage cost and latency, and test against prompt injection and tool vulnerabilities. The role requires backend software engineering, production agent development, MCP experience, LLM evaluation, and AI coding assistant expertise, with preferred experience in managed runtimes, retrieval systems, and enterprise security.

Lead development and deployment of an agentic AI/ML pipeline for identifying, prioritizing, and pricing high-cost healthcare claims. Responsibilities include model development, confidence scoring, HITL safety design, ML lifecycle management, monitoring, workflow automation, responsible AI, and enterprise-scale analytics using Databricks and Snowflake. The role also evaluates emerging AI technologies and supports regulatory-aware automation and healthcare data transformation.

Designs, deploys, and scales AI and machine learning solutions for enterprise healthcare applications. Responsibilities include building and tuning production models, developing MLOps frameworks and deployment pipelines, monitoring model performance, automating workflows, evaluating emerging AI technologies, and ensuring security, scalability, responsible AI, and governance standards. The role collaborates with software engineers, data scientists, and business stakeholders.

Develop, tune, validate, test, and deploy machine learning and LLM solutions. Responsibilities include neural network tuning, model development and evaluation, experiment design, benchmark testing, RAG testing, GPU optimization, SQL analysis, and AI system validation. The role requires onsite work in Northern Virginia and an active TS/SCI clearance with full-scope polygraph.

Leads the development and global scaling of Fuelight, an AI-enabled resource allocation and investment optimization product. Responsibilities include building production machine learning applications, MLOps capabilities, scalable architectures, decision-support experiences, explainable AI features, and integrations with data and optimization products. Partners with engineering, data science, product, and business stakeholders while establishing engineering standards and ensuring cybersecurity, privacy, responsible AI, and governance compliance.

Leads the technical vision, architecture, and roadmap for SimpliSafe’s machine learning data and training platform. Builds scalable data and feature pipelines, distributed training infrastructure, experimentation and model lifecycle systems, CI/CD automation, and AWS-based compute optimization. Establishes standards for reproducibility, governance, and versioning while mentoring engineers, leading cross-team initiatives, evaluating technologies, and supporting platform reliability through on-call participation and incident postmortems.

Design, develop, evaluate, and refine machine learning models using Python. Manage training data with MongoDB, build preprocessing and data pipelines, tune hyperparameters, benchmark model performance, document experiments, and deliver data-driven insights. Collaborate remotely with cross-functional contributors and support deployment or operationalization of models in cloud or enterprise environments.

Develop and refine machine learning models using Python, analyze datasets with MongoDB, and integrate preprocessing and data pipelines. Evaluate models, tune hyperparameters, benchmark performance, document experiments, and deliver data-driven recommendations. Collaborate remotely with cross-functional contributors to improve and operationalize machine learning solutions in cloud or enterprise environments.

Lead the design, development, deployment, monitoring, and improvement of interconnected customer lifetime value models covering conversion, retention, premiums, and claim losses. Serve as a hands-on technical leader, guide modeling strategy, evaluate experiments and business impact, partner with engineering and business teams, support production ML systems, establish technical standards, and coach other data scientists.

Build and evaluate secure, robust machine learning systems for government sponsors. Responsibilities include researching AI vulnerabilities, developing adversarial ML defenses, designing prototypes, experimenting with ML frameworks and algorithms, testing system performance and security, transitioning capabilities to sponsors, collaborating across technical teams, and mentoring engineers. The role requires onsite work in Pittsburgh or Arlington, up to 25% travel, and an active Department of War security clearance.

Lead the design and delivery of scalable machine learning systems and customer-facing generative AI applications for Rovo Chat. Build agent harnesses that improve quality, reliability, and latency; advance state-of-the-art AI capabilities; lead engineers through technical design and launch; and collaborate cross-functionally with teams and internal customers. The role also involves applying a deep understanding of the machine learning project lifecycle and supporting platform-based product development.