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# Senior AI Engineer

- **Company:** Wintermeyer Ventures
- **Location:** San Francisco, CA, United States
- **Experience:** Expert
- **Salary:** $200,000 - $250,000
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Software Debugging, Software Engineering, Systems Integration, Test Case, Large Language Models, Automation Anywhere
- **Published:** November 11, 2026
- **Apply:** https://www.careerbuilder.com/job-details/senior-ai-engineer-san-francisco-ca--4527ccca-d558-48f5-9fdf-04790b0acb5d

## About the Role

Artificial Intelligence (AI), Artificial Intelligence (AI), Trading/Stockbroking, Prototyping, Test Case, Debugging Skills, Software Engineering, Production Systems, Engineering, Performance Analysis, * Four to ten years of software engineering experience, including ownership of production systems through launch, operation, and debugging.

 * Hands-on experience building LLM or agent systems used by real users, including orchestration or harnesses, tool calling, and evaluations.
 * Experience measuring and improving production LLM quality, latency, or cost using evaluations, trace inspection, and performance analysis.
 * Strong software engineering skills in addition to any data science background, and the ability to move from prototypes to reliable production features.
 * Experience at an early-stage startup and good judgment about when agents should act autonomously or hand work to a person.
 * Strong computer science fundamentals and experience building practical AI workflows or connected-system integrations.

## Description

Join an early-stage AI company building software for physical commodity trading and operations. You will build reliable agent systems that turn modern models into useful workflows, from early experiments through production use., * Build agent harnesses that provide models with the tools, context, and execution environments needed to complete tasks across the product and connected systems.

 * Design agent planning, action-taking, state management, failure recovery, and handoffs to people.
 * Prototype model-powered features against real customer problems and develop promising experiments into production capabilities.
 * Create evaluations for task completion and decision quality, and turn production failures into reproducible test cases.
 * Inspect execution traces and debug prompts, context, tool interfaces, orchestration, and model behavior.
 * Improve agent autonomy using appropriate permissions, checkpoints, and human intervention.
 * Evaluate models and approaches for capability, reliability, latency, and cost in real workflows.
 * Build reusable tools and harness components that make features easier to develop and operate.