Top AI & Machine Learning Jobs

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.

Designs, develops, and maintains AI-enabled business system solutions, automation pipelines, enterprise integrations, analytics, and reusable application components. Partners with stakeholders and platform owners to translate business needs into secure, scalable solutions across Oracle, Salesforce, Foundry, and low-code platforms. Supports AI governance, data protection, compliance, data lineage, architecture, documentation, and operational procedures.

Build and optimize AI agents, automated workflows, and marketing technology integrations that support demand generation, outbound prospecting, campaign execution, pipeline growth, and marketing operations. The role involves connecting Salesforce, HubSpot, product, customer, and CRM data; evaluating emerging AI technologies; and implementing monitoring, safeguards, testing, and feedback loops to ensure reliable, measurable business outcomes.

Design and build production AI infrastructure that automates financial reporting, reconciliation, variance analysis, and narrative generation. Develop AI agents, data pipelines, retrieval systems, and workflow orchestration with strong reliability, auditability, governance, and human review controls. Partner with Finance, Data Platform, Security, Risk, Audit, and Compliance teams; establish applied AI standards; evaluate tooling and models; mentor engineers; and translate between technical and financial requirements.

Build and deploy applied AI solutions for legal and business workflows. Partner with attorneys and operational leaders to identify use cases, design AI applications, orchestrate agentic workflows, engineer prompts, integrate solutions with enterprise systems and APIs, validate outcomes with stakeholders, document delivery, and monitor emerging AI technologies. The role requires software solution delivery experience, hands-on AI building, prompt engineering, API integration, cloud knowledge, and strong communication skills.

Lead enterprise workforce AI initiatives using Microsoft 365 Copilot, Copilot Studio, Azure AI services, and Power Platform. Guide AI governance, security, compliance, responsible adoption, and scalable productivity solutions. Partner with business and technology teams, mentor engineers and citizen developers, troubleshoot outages, conduct capacity planning, coordinate third-party support, and lead service restoration. The role requires after-hours maintenance and participation in an on-call rotation.

Build and deploy AI-powered go-to-market agents, growth experiments, enrichment and signal-detection systems, lead scoring, outbound programs, attribution, SEO tooling, and sales enablement workflows. The role requires independently shipping experiments, measuring pipeline and conversion impact, contributing to Foley’s shared AI platform and ontology, and applying appropriate consent, privacy, deliverability, and release guardrails.

Build and operate production machine learning and generative AI infrastructure, including Azure and Databricks pipelines, CI/CD workflows, RAG systems, APIs, model-serving services, infrastructure as code, automated testing, monitoring, versioning, and governance. The role requires collaboration with AI, ML, data, infrastructure, security, and governance teams to deliver scalable, secure, reproducible, and high-performance AI solutions.