Top AI & Machine Learning Jobs

The Machine Learning Researcher will build predictive models using deep learning, explore strategies to enhance portfolio performance, and update models based on research. They should have a strong foundation in ML with hands-on experience in relevant domains.

The AI Developer will design and implement AI solutions tailored for federal healthcare, collaborating with teams to optimize healthcare data analysis and improve service delivery. Responsibilities include developing AI software, ensuring compliance with regulations, and maintaining data privacy.

Build and prototype state-of-the-art large language models and AI assistants, take services from ideation to production, fine-tune open-source models, use LLM platforms, analyze model statistics and projections, and collaborate cross-functionally in a fast-paced hybrid Palo Alto environment.

Lead design, buildout, and operationalization of a secure enterprise AI platform in Azure GCC High. Establish frontier model access, RAG-enabled knowledge systems, governed AI APIs, RBAC and monitoring, and enable engineering teams to deploy AI-powered tools. Ensure compliance with FedRAMP/DoD/CMMC controls and coordinate with IT for secure networking and identity integration. Support AI adoption, prototyping, and lifecycle practices across internal and client projects.

Provide SETA technical advisory support to government teams developing and evaluating AI-enabled applications. Assess LLMs, RAG, agentic workflows, orchestration frameworks, prompts, evaluation harnesses, and integration risks. Advise on latency, reliability, observability, guardrails, and system integration. Develop guidance, prototypes, and briefings; support test and evaluation activities and collaboration with engineers, product leads, and mission stakeholders.

Run demos and onboard customers, build engineer-to-engineer trust, create working proofs of concept from messy problems, be the expert Rerun user and relay customer feedback to the roadmap, and coordinate cross-functionally to improve POCs and onboarding.

As a Machine Learning Engineer, you'll build ML infrastructure, develop scalable data pipelines for farm images, and collaborate with farmers and agronomists to apply ML solutions.

Two-year rotational program (six four-month rotations) embedding recent graduates across business functions to identify, design, build, and deploy AI and automation solutions. Drive production-ready automations, validate data integrity, document technical work, present results to leadership, train successors, and expand an organization-wide automation library.

Own post-sales lifecycle for strategic accounts: drive adoption, retention, and expansion; triage technical/compliance incidents by reading transcripts, logs, prompts, evals, and integration data; implement prompt and logic fixes; escalate clear diagnoses to engineering; run QBRs and handoffs; convert customer feedback into prioritized product signal.

Architect and build AI-native production systems across a multi-language stack, design offline and online evaluation frameworks for LLMs, monitor live model behavior and drift, lead architecture reviews, mentor senior engineers, and prototype and ship AI capabilities while ensuring reliability and correctness in a regulated domain.