Top AI & Machine Learning Jobs

Leads and manages ML engineers working across computer vision, robotics, agentic workflows, and ML platform infrastructure. Owns technical direction, hiring, team development, roadmap commitments, production deployment, monitoring, incident response, reliability, and cross-functional collaboration with product and software engineering teams. This player-coach role requires hands-on oversight of production ML systems, platform architecture, evaluation, safety, and operational quality.

Leads the DataOps and MLOps platform for Databricks on Azure, establishing CI/CD, orchestration, observability, governance, infrastructure automation, and reliable production deployment of data pipelines and machine-learning models. Owns Dagster, dlt, dbt, MLflow, Unity Catalog, Terraform, security, cost optimization, incident practices, compliance automation, and model monitoring. Provides technical leadership, mentors engineers, and ensures data and ML systems are reliable, auditable, secure, and cost-effective in a regulated insurance environment.

Develop analytical and AI-enabled capabilities for a federal data modernization effort. Translate stakeholder questions into statistical analyses, predictive models, forecasts, and scenario analyses; build governed datasets using Databricks, BigQuery, Python, SQL, and PySpark; validate and document models; maintain analytical products; and communicate visualizations, dashboards, findings, assumptions, and limitations to technical and non-technical audiences.

Lead data science research and production development for attribution, identity resolution, tracking quality, and incrementality measurement. Design experiments, build and monitor production pipelines, evaluate third-party identity vendors, and develop ML and graph-based solutions. Translate technical findings into recommendations for Product, Engineering, and leadership while shaping measurement strategy and build-versus-buy decisions.

Build and operate AI agent applications that analyze enterprise code, identify and prioritize security risks, propose and verify fixes, and open merge-ready pull requests. Develop context engineering, evaluation benchmarks, and reliable production systems across thousands of repositories. Partner with customers on integrations, review code, and mentor engineers. Requires deep Python expertise, agentic workflow experience, application security knowledge, and production software development experience.

Leads AI strategy, use-case prioritization, solution delivery, governance, and adoption across the Americas. Partners with business and technology leaders to deliver production-grade predictive analytics, optimization, generative AI, and automation solutions. Coordinates regional pilots, vendors, data readiness, infrastructure, security, global standards, and stakeholder adoption across logistics, ports, warehousing, and corporate functions.

Build and maintain core AI framework infrastructure, including Python APIs, graph execution, training workflows, validation systems, numerical parity tests, and reusable framework components. Develop JAX and PyTorch tooling, improve research-to-production workflows, and collaborate with researchers and hardware engineers. The role also involves API design, testing, documentation, developer ergonomics, and shaping software architecture connecting model design, simulation, training, validation, and hardware execution.

Develops data pipelines, curates and analyzes large datasets, builds quantitative models, validates data and results, and deploys analytics solutions with product teams. Responsibilities include discovering useful datasets, ensuring data quality and reliability, supporting customer-driven requirements, and integrating solutions into interconnected systems. The role requires strong Python and data-handling skills, database expertise, large-scale batch processing experience, and U.S. citizenship.

Build and deploy production machine learning applications, APIs, inference services, and data platforms. Engineer large-scale pipelines using distributed technologies, data modeling, ETL/ELT, transformation, and data quality practices. Translate defense and mission needs into scalable solutions across cloud, containerized, and GPU/CPU environments while collaborating with engineering, data, and mission teams.

Leads architecture, development, and delivery of AI-powered applications and enterprise data platforms. Designs Generative AI solutions using LLMs, agents, RAG, and Vertex AI; develops full-stack cloud-native applications; builds Databricks, Spark, and Lakehouse data architectures; and establishes GCP cloud standards. Provides technical leadership, mentoring, architecture guidance, governance, observability, CI/CD practices, technical evaluations, and technology roadmaps across engineering, data, analytics, and product teams.