Top AI & Machine Learning Jobs

Build and maintain production computer vision preprocessing pipelines for virtual try-on, including segmentation, pose estimation, human parsing, and body mapping. Validate inputs, benchmark and integrate vision models, evaluate performance on internal datasets, perform data quality checks, and collaborate with ML scientists to productionize research.

Build and operate AI-native full-stack products, including multi-agent LLM orchestration, scalable FastAPI services, data pipelines, React and Next.js interfaces, experimentation platforms, evaluation systems, and reliability infrastructure. The role owns features from design through deployment, optimizes performance, leads incident response, partners with customers and cross-functional teams, and mentors engineers. It requires strong Python and TypeScript skills, experience with LLM systems, event-driven workflows, observability, enterprise security, and rapid product delivery.

Leads Hazen’s enterprise AI strategy, roadmap, governance, adoption, and implementation across engineering and corporate functions. Builds and manages an AI team, prioritizes use cases, oversees enterprise platforms and custom AI solutions, establishes responsible-use standards, and partners with IT, cybersecurity, Legal, and business leaders. Drives training and change management while ensuring data security, professional accountability, and regulatory compliance.

Leads AI projects from discovery through implementation, evaluation, and maintenance. Builds secure, production-ready AI workflows integrated with organizational data, applications, APIs, repositories, cloud platforms, and business systems. Establishes standards for AI architecture, governance, monitoring, permissions, guardrails, and responsible use. Advises leadership on models, vendors, risks, and adoption strategies; supports users through technical guidance and training; and mentors junior software engineers.

Owns the architecture and safe operation of high-volume autonomous AI development pipelines. Responsibilities include generator-verifier systems, LLM agent orchestration, sandboxing, CI/CD, evaluation suites, security boundaries, AWS Bedrock inference, compliance guardrails, staged deployment, and human review standards. The role establishes engineering practices for multiple delivery teams and serves as the final accountability gate before agent-generated code reaches production.

Serves as a hands-on technical lead building, deploying, and operating governed agentic AI and LLM solutions on Databricks. Develops RAG and multi-agent systems, MCP integrations, secure tools, prompt and context engineering, AI Gateway controls, identity authorization, observability, CI/CD, testing, and monitoring. Partners with business, security, risk, compliance, and data teams to deliver measurable outcomes in a regulated banking environment, while establishing governance standards and mentoring engineers.

Develops and evaluates AI-powered software, automation scripts, prototypes, and enterprise solutions using programming languages, LLMs, APIs, and AI-assisted coding tools. Collaborates with engineers, architects, and business development teams on technical research, federal proposals, workflow automation, and solution documentation. The role also assesses AI-generated code for quality, security, performance, and maintainability while supporting adoption of approved AI technologies.

Design and build Uber’s large-scale machine learning training infrastructure, addressing challenges involving massive datasets, models, distributed systems, and GPU resources. Mentor engineers, establish technical standards, review architectures and code, optimize performance and utilization through statistical analysis, and develop tools that improve engineering velocity. Lead complex projects from inception through global productionization while coordinating with Product, Data Science, and Operations teams.

Develop and deploy computer vision and machine learning solutions for autonomous vehicle perception, including object and signal recognition. Build production software in Python and C++, integrate models with vehicle systems, process camera and LiDAR data, and create testing and evaluation tools. Train, optimize, and analyze AI models, debug algorithms, improve inference performance, and research emerging technologies for safer autonomous driving systems.

Build and operate production AI infrastructure for multi-agent systems across customer clouds. Develop Python or Go tooling, CLIs, controllers, and services; deploy and optimize LLM inference with Kubernetes and vLLM; manage MLOps, model releases, observability, CI/CD, GitOps, security, and multi-cloud platform reliability. The role also involves Kubernetes operators, Terraform, GPU scheduling, agent runtimes, evaluation pipelines, and developer self-service automation.