Top Machine Learning Jobs

Architect, deploy, operate, and monitor on-premises LLM infrastructure and agentic workflows. Build RAG pipelines, MCP servers, data pipelines, CI/CD integrations, testing environments, tracing and evaluation tools, and model-serving network requirements. Support Kubernetes-based AI/ML infrastructure and software development environments while collaborating with cross-functional teams. The role requires a bachelor's degree with four years of related experience or a master's degree with two years, plus TS/SCI clearance.

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.

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.

Lead hands-on development and production deployment of machine learning, large language model, and generative AI solutions. Set technical direction, make architecture decisions, establish MLOps pipelines, monitor model performance, optimize and fine-tune generative models for NLP, and ensure reliability and scalability. Partner with product, data science, engineering, and business stakeholders, communicate technical results clearly, mentor engineers, and guide delivery across multiple workstreams.

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.

Lead the design, development, productionization, and monitoring of machine learning models and pricing algorithms for Uber’s large-scale, real-time marketplace. Develop optimization solutions using deep learning, causal modeling, reinforcement learning, and algorithmic game theory. Set technical direction, mentor engineers, collaborate cross-functionally, and deliver scalable systems serving billions of trips and over one million predictions per second.

Develop, deploy, and support secure, scalable production software and AI/ML solutions. Collaborate with product, UX, and engineering to deliver features, create tests and automation, instrument monitoring and dashboards, and participate in agile processes while continuously learning and improving team practices.

Develop and deploy applied AI and machine learning systems for customer-facing agents. Responsibilities include model fine-tuning, reinforcement learning, evaluation design, large-scale text and audio data work, post-training, voice AI, long-horizon optimization, and production deployment. The role translates customer and product challenges into ML problems and carries solutions from experimentation through measurable customer impact.

Build scalable infrastructure for LLM-based agentic AI supporting autonomous security operations. Responsibilities include designing agent orchestration frameworks, LLM inference and resource-management systems, MCP servers, supporting APIs, and distributed backend services. The role collaborates with machine learning, security research, engineering, and product teams. Required experience includes backend infrastructure, distributed systems, APIs, microservices, cloud deployment, containerization, orchestration, and Python, Java, or Go.

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.

Leads the architecture and development of production LLM, retrieval, knowledge-graph, and machine learning systems for investment research. Owns ML application architecture, data pipelines, model serving, evaluation, monitoring, MLOps standards, and engineering quality. Partners with quants and portfolio managers, mentors engineers, evaluates AI technologies, and drives governed delivery across teams in a regulated environment.

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.

Supports enterprise data modeling, integration, quality improvement, and machine-learning projects in a manufacturing environment. Responsibilities include product-family classification using BOM data, feature development, model testing, validation, MLOps and AI research, agentic workflow mapping, controlled AI testing, documentation, and communicating findings to technical and business stakeholders. The intern collaborates across departments while gaining practical experience in data engineering, machine learning, and AI-enabled process improvement.

Design, evaluate, and deploy machine learning policies for autonomous vehicle interactions, including yielding, lane changes, merges, and cut-ins. Integrate learned models into closed-loop simulation, evaluate safety metrics, and transition models into production. Collaborate across prediction and planning teams while replacing rule-based heuristics with scalable, data-driven solutions. The role requires strong C++ software engineering, machine learning expertise, autonomous systems experience, and cross-functional collaboration.

Develop, train, deploy, and optimize AI/ML models for object detection, data triage, search, inference, automated discovery, and decision-making. Manage model versioning systems, contribute to AI/ML capability development, and identify model vulnerabilities while supporting a government customer.

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.

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.

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.

Build and operate the machine learning inference stack for SambaNova’s RDU accelerators. Develop production Python features including speculative and constrained decoding, tool calling, prompt caching, long-context inference, and vLLM integration. Maintain public inference APIs, create accuracy and regression tests, and troubleshoot latency, throughput, and reliability issues. The role requires strong systems and programming fundamentals, experience with LLM serving frameworks and inference techniques, and PyTorch knowledge.