AI Engineer at Hill Research AI Engineer at Hill Research in Skillman, New Jersey Posted in about 23 hours ago.

Type:

full-time Hill Research is looking for a hands-on engineer who can build AI-agent workflows, develop full-stack product features, debug complex systems, and improve the engineering processes connecting code to production.

ABOUT HILL RESEARCH

Hill Research develops AI-enabled software for clinical-trial and pharmaceutical workflows. Our TriClick platform helps teams process clinical documents, manage data workflows, and produce traceable outputs. Our goal is not simply to generate results with AI. We build systems that must be reliable, secure, reproducible, and suitable for sensitive, regulated workflows.

ABOUT THE ROLE

This is not purely an ML research or traditional DevOps position. Depending on the problem, you may:

• Investigate a React interface
• Debug a Python API or asynchronous job
• Trace a pull request through CI/CD and deployment
• Improve monitoring and observability
• Design validation and safety controls for AI agents You should be comfortable using AI coding tools such as Codex or Claude Code while remaining personally responsible for understanding, reviewing, testing, and verifying the resulting work.

WHAT YOU'LL DO

• Design and maintain tool-using AI agents, structured outputs, validation gates, evaluation workflows, and human-approval boundaries
• Build and debug frontend features using React and TypeScript
• Develop Python APIs and services using Django or FastAPI
• Investigate asynchronous jobs, queues, retries, state transitions, and frontend/backend consistency issues
• Trace changes across pull requests, commits, CI runs, artifacts, deployments, environments, logs, and user-visible behavior
• Improve CI/CD pipelines, deployment reliability, monitoring, runbooks, and routine engineering operations
• Work with AWS, containers, databases, Redis, and infrastructure-as-code workflows
• Write behavior-focused tests and perform runtime verification
• Communicate clearly about what is confirmed, assumed, incomplete, or still unverified

WHAT WE'RE LOOKING FOR

• Strong computer-science and software-engineering fundamentals
• Proficiency in Python and practical experience with APIs and backend services
• Experience with JavaScript or TypeScript and modern frontend development
• Ability to debug across application, infrastructure, and deployment boundaries
• Understanding of asynchronous processing, idempotency, retries, state management, and failure recovery
• Practical experience using AI coding tools without blindly accepting generated code
• Familiarity with Git, pull requests, automated testing, CI/CD, and code review
• An evidence-based approach to problem-solving: reproduce, narrow, hypothesize, instrument, fix, test, and verify
• Strong ownership, honest communication, and careful security judgment
• Ability to work effectively in a small, cross-functional engineering team

NICE TO HAVE

• Experience with AWS, Docker, Terraform, Kubernetes, Redis, PostgreSQL, or message queues
• Experience building or evaluating LLM applications, RAG systems, tool-calling agents, or multi-agent workflows
• Familiarity with observability, tracing, incident response, and production debugging
• Experience with clinical, healthcare, pharmaceutical, or other regulated software
• Understanding of tenant isolation, auditability, data provenance, and reproducible AI outputs
• Experience with MCP or structured agent-tool integrations Prior clinical-trial experience is welcomed but not required.

We value engineers who can learn an unfamiliar regulated domain, communicate uncertainty honestly, and apply sound engineering judgment.

WHAT SUCCESS LOOKS LIKE

During your first few months, you will:

• Independently investigate and resolve full-stack and deployment-related problems
• Deliver focused product improvements with meaningful tests
• Prove which code and artifact are running in each environment
• Improve the reliability and observability of AI and asynchronous workflows
• Reduce repetitive manual work through focused automation and better runbooks

HOW WE WORK

We encourage responsible use of AI engineering tools. We care less about memorizing every framework and more about whether you can:

• Reconstruct a real system precisely
• Explain your personal contribution
• Find and prove the root cause of a failure
• Verify AI-generated work independently
• Communicate clearly and complete the operational loop

HOW TO APPLY

Please submit your résumé and, if available, links to relevant GitHub projects or technical work.