Senior/Staff Applied AI Engineer, Agent Harness at Transparent Search Group in San Francisco, California, United States

Job description

• This role builds the layer that turns model capability into systems that actually work for users. You will develop the core agent harness, including the execution loop, tool-use strategies, context construction and model-facing experimentation, and improve agent behavior across real customer workflows and long-horizon tasks.
• A defining principle at Init: the creative step happens once, when a workflow is authored, and what runs afterward is deterministic, compiled, type-checked code rather than open-ended tool-chaining. You will own the boundary between what the model decides at runtime and what ships as code.
• You will build and run evals against replicas of real customer environments, reliable enough to gate a release. You will analyze production failures and trace them to the right layer (model, prompt, tool contract, environment state or retry logic). And you will extend the computer-use agent to systems with no API, owning the guarantees that let it run in production: a fresh microVM per task, credentials injected at session start and never seen by the model, every action recorded, and any session stoppable mid-run.

Responsibilities

• The core agent harness: execution loop, tool-use strategies, context construction and model-facing experimentation
• Agent behavior across real customer workflows and long-horizon tasks
• The boundary between runtime model decisions and deterministic, compiled, type-checked execution
• Evals against replicas of real customer environments, reliable enough to gate releases
• Production failure analysis and systematic robustness improvements, traced to the right layer
• Extending computer use to systems with no API, with production guarantees (per-task microVM, model-blind credentials, full action recording, stoppable sessions)
• Feedback loops and data systems that bring better real-task data into evaluation and training

Requirements

• 4+ years of experience
• Experience with agent frameworks or tool-using LLM systems
• Strong Python or TypeScript and comfort with modern AI tooling
• Experience with model evaluation, fine-tuning or prompt design
• The ability to own systems end to end and debug across the stack
• A focus on systems and user outcomes, not just model metrics
• You enjoy debugging messy, real-world failures and turning them into improvements
• Ability to work in person in San Francisco, Monday to Friday, at startup intensity
• Built computer-use or browser-automation agents
• Experience with virtualization and sandboxed execution environments, and scaling them
• AI research published at top conferences
• Shipped systems where correctness had to survive partial failure (idempotency, resumability, compensating actions)
• Integrated enterprise SaaS APIs (identity providers, directory services, ticketing, ERPs)
• Experience with large, messy datasets or production logs
• Been an early engineer somewhere you also talked to customers

Skills

Required

• Strong Python or TypeScript
• Comfort with modern AI tooling
• Focus on systems and user outcomes

Preferred

• AI research published at top conferences
• Integrated enterprise SaaS APIs

Benefits

• Meals in office, health insurance, unlimited PTO.