Top AI & Machine Learning Jobs

Develop and optimize large-language-model inference and post-training systems for strategic customers. Configure inference engines, tune performance, and implement techniques including KV caching, speculative decoding, parallelism, and quantization. Lead fine-tuning and reinforcement-learning workflows, guide production deployments, support customer onboarding, and provide product feedback. Partner with Solutions Architecture, Engineering, Sales, and customers to deliver successful proofs of concept, platform adoption, and tailored model optimization.

Design and build production agentic systems for creative video workflows. Responsibilities include architecting agents that gather context, plan, select tools, and execute tasks; integrating advanced models; and improving quality through experimentation, evaluation, and production feedback. The role spans product, machine learning, and systems engineering, with end-to-end ownership of agent infrastructure and experiences.

Designs and delivers GenAI training focused on workflow integration, use-case identification, responsible use, and tool adoption. Conducts needs analyses, develops curricula and job aids, collaborates on change management, measures learning outcomes, and continuously improves content. Delivers frequent virtual training sessions using communication platforms, coordinates with partners, reports learner feedback, supports internal tools, and may travel for onsite instruction.

The Senior AI Engineer will lead the design, development, and deployment of AI solutions for federal customers, ensuring compliance and governance.

Own full-stack AI product features from concept through launch, adoption, and iteration. Translate user, partner, and enterprise needs into reliable product experiences involving AI chat, search, research, workflow automation, administration, and integrations. Build and test across front-end, back-end, APIs, and infrastructure-adjacent areas; make product and technical tradeoffs; measure adoption and reliability; and establish reusable engineering patterns. Collaborate with product, design, enterprise teams, leadership, open-source contributors, and external partners in a remote environment.

Build retrieval-augmented generation systems and engineer AI context using Azure AI Search and Cosmos DB vector search. Integrate a parts agent with an existing manuals agent and develop hands-on generative AI platform capabilities.

Build production-grade LLM applications (agentic chatbots, semantic search, RAG systems), fine-tune foundation models, implement RAG and agent architectures, integrate AI assistants with systems, design evaluation/governance pipelines, collaborate with cross-functional teams, and mentor junior data scientists.

Supports an intelligence mission team by defining, documenting, and validating requirements for AI-enabled enterprise applications. Collaborates with customers, developers, systems engineers, and domain experts; translates requirements into Jira stories and technical specifications; performs functional testing; evaluates AI architectures and model performance; and manages system-level integration tradeoffs through Agile delivery.

Teach introductory and upper-division Computer Science and Data Science courses, develop curricula, conduct an active research program involving students, pursue external grants, advise students, participate in university service, and contribute to interdisciplinary and community engagement initiatives.

The AI/ML Engineer will design and deploy machine learning solutions, build model architectures, collaborate with teams, and ensure model performance and scalability.