Top Machine Learning Jobs

Design, implement, train, evaluate, optimize, and deploy advanced AI/ML solutions including Agentic AI, RAG, and LLMs for constrained/edge environments; translate mission stakeholder requirements into scalable, resource-aware models and recommend performance improvements.

Provide advanced discovery and analytical support using machine learning, statistical analysis, scripting, automation, and TechSIGINT tools. Develop analytical prototypes, create data visualizations, document methods and collaborate with analysts and engineers to support mission-focused discovery efforts and adopt new analytical technologies.

Develop, test, and apply quantum computing workflows or quantum-inspired approaches to analyze sensing and energy-industry datasets. Work may include signal processing and machine learning. Produce technical documentation, presentations, and publishable research (journal or conference paper).

Designs, prototypes, validates, and deploys predictive and risk models for P&C underwriting. Works with structured and unstructured data, automates ML solutions, ensures model governance (MDC/MRM), communicates findings to business partners, and collaborates with engineering and IT to productionize analytics.

Design, build, and deploy production-grade AI/ML models, agentic workflows, and data pipelines using Python, Databricks, LangChain/LangGraph, and Azure. Collaborate with cross-functional teams to translate requirements into scalable LLM applications, implement feature engineering and validation frameworks, and embed responsible AI practices including fairness, security, and monitoring.

The Machine Learning Engineer will design, build, and deploy ML applications at scale, collaborating with cross-functional teams and applying best practices in ML engineering and cloud technologies.

Lead a team of Machine Learning Engineers in designing, building, and implementing ML applications, focusing on data-intensive solutions and infrastructure decisions, while promoting best practices in AI.

Design, build, and productionize large-scale foundation models and ML systems (transformer architectures, self-supervised learning). Architect and implement training, data pipelines, deployment, monitoring, and CI/CD. Collaborate with cross-functional teams, ensure model governance, reliability, and performance, and apply ML engineering best practices at scale.

Develop scalable machine learning services for data enrichment, managing the ML lifecycle from model training to deployment, and ensuring performance and reliability standards.

This role involves building and optimizing ML systems and frameworks, collaborating with teams to enhance research and development in ML models.

As a Machine Learning Engineer in Fraud Risk, you'll build and maintain ML systems for fraud detection and anomaly analysis, ensuring robust performance and real-time decision making.

Support large-scale image and video dataset curation and labeling workflows; run and document model training and evaluation jobs; maintain benchmarks; analyze failure cases with senior engineers; perform foundational tasks across the ML lifecycle to enable production deployments.

Build and deploy machine learning models on large healthcare datasets, extract actionable insights, develop scalable data processing systems, collaborate with cross-functional teams, and communicate findings to stakeholders to improve patient outcomes.

Design, develop, and implement AI/ML models; analyze large datasets; integrate AI solutions into existing systems; apply MavLink for autonomous systems; collaborate across teams and improve ML development processes.

The Principal Machine Learning Infrastructure Researcher will design and model performance for ML systems using Lightmatter technology while leading a small team and publishing research findings.

Design, build, and own a multi-tenant enterprise SaaS backend that feeds ML data and BI. Collaborate with product, data science, UX, and users to deliver scalable, reliable customer-facing AI solutions. Drive systems design, database schemas, development lifecycle standards, technical docs, and code reviews while using AI/agentic tools to accelerate development.

Lead analytics and ML work to drive Aetna Medicare growth: design experiments, segmentation, forecasting, and end-to-end ML system architecture; prototype models, collaborate with engineering and product teams for production deployment, and communicate data-driven recommendations to cross-functional stakeholders.

Lead development of large-scale, low-latency ML systems for ad serving to optimize pricing, allocation, and auctions. Design experiments, build production ML pipelines and model serving, partner cross-functionally, and provide technical leadership and mentorship.

Apply machine learning and AI to structured and unstructured IoT and other datasets: clean and integrate data, develop and deploy user-facing models, create visualizations/dashboards or lightweight web apps, advise clients, lead teammates, and apply inferential statistics and research design. Requires TS/SCI with polygraph.

Design, train, test, deploy, and maintain AI/ML models for national security customers. Prototype user-facing AI solutions, evaluate and tune outputs (prompts, retrieval logic, heuristics), build/demo APIs or rapid UIs, and collaborate with data engineers, data scientists, MLOps, and stakeholders to deliver production-ready AI capabilities.

Design, prototype, and deploy AI-powered solutions by partnering with business stakeholders and engineering teams. Analyze processes, identify AI automation opportunities, build POCs/MVPs using LLMs and agents, define KPIs, document and train users, and drive adoption and scalability of reference AI solutions across the organization.

As an AI/ML Engineer, develop and optimize analytics frameworks to enhance autonomous vehicle development, focusing on automation tools and collaboration with engineering teams.

Lead the design and implementation of advanced ML models for autonomous driving, collaborating with teams, mentoring engineers, and driving technical initiatives.

Lead a team to measure and improve enterprise search relevance and ranking using ML and LLM/RAG techniques. Manage end-to-end model development, evaluation, deployment, cross-functional collaboration, and team growth to ensure scalable, production-grade search quality.

As a Senior Machine Learning Engineer, you will lead model validation for AI systems, challenge model soundness, and build validation tools for high-stakes areas such as credit and fraud prevention.