The job, in the employer's words
About the company
Our client is a fast-growing software company.
The role
Raydar is recruiting for this role on behalf of our client. Design and build the core software that lets large language models carry out multi-step work reliably in production. You will shape how models are prompted and combined with conventional code, measure quality through rigorous testing, and diagnose real-world failures to make systems more robust.
What you'll do
- Develop the core execution framework for AI agents, including orchestration loops, tool usage and context handling.
- Run experiments with models and iterate on agent behavior across realistic, long-running workflows.
- Define where model decisions end and deterministic, type-checked code begins.
- Build evaluation suites on realistic test environments that are dependable enough to gate releases.
- Analyze production failures and trace each one to the right layer, then deliver systematic fixes.
- Extend browser and computer-use agents to systems that lack APIs, with strong safety, security and auditability guarantees.
- Create feedback loops and data systems that bring higher-quality real-task data into evaluation and training.
Requirements
What we're looking for
- Experience with agent frameworks or LLM systems that use tools.
- Strong Python or TypeScript skills and comfort with modern AI tooling.
- Hands-on background measuring or improving model quality, whether through testing, tuning or crafting instructions for models.
- Ability to own systems end to end and debug across the whole stack.
- A systems mindset focused on user outcomes as well as model metrics.
- Satisfaction in tracking down unpredictable production problems and converting what you learn into lasting fixes.
- At least 4 years of relevant experience.
- Ability to work in person five days a week at a startup pace.
Bonus points
- Experience building computer-use or browser-automation agents.
- Familiarity with virtualization and sandboxed execution environments, including scaling them.
- AI research experience with publications at leading conferences.
- Experience delivering systems where correctness must hold through partial failure, such as idempotency and resumability.
- Experience integrating enterprise SaaS APIs.
- Comfort working with sizable, unstructured data sources such as application logs.
- Early-stage engineering experience that included working directly with customers.
Benefits
Compensation and benefits
- Base salary: USD 200,000 to 300,000 per year
- Health insurance
- Unlimited paid time off
Location and work model
- San Francisco, CA, United States
- On-site, five days a week in office
- Full-time
Benefits mentioned
- Compensation and benefits
- - Base salary: USD 200,000 to 300,000 per year
- - Health insurance
- - Unlimited paid time off
- Location and work model
- - San Francisco, CA, United States
- - On-site, five days a week in office
- - Full-time
Requirements mentioned
- What we're looking for
- - Experience with agent frameworks or LLM systems that use tools.
- - Strong Python or TypeScript skills and comfort with modern AI tooling.
- - Hands-on background measuring or improving model quality, whether through testing, tuning or crafting instructions for models.
- - Ability to own systems end to end and debug across the whole stack.
- - A systems mindset focused on user outcomes as well as model metrics.
- - Satisfaction in tracking down unpredictable production problems and converting what you learn into lasting fixes.
- - At least 4 years of relevant experience.
- - Ability to work in person five days a week at a startup pace.
- Bonus points
- - Experience building computer-use or browser-automation agents.
- - Familiarity with virtualization and sandboxed execution environments, including scaling them.
- - AI research experience with publications at leading conferences.
- - Experience delivering systems where correctness must hold through partial failure, such as idempotency and resumability.
- - Experience integrating enterprise SaaS APIs.
- - Comfort working with sizable, unstructured data sources such as application logs.
- - Early-stage engineering experience that included working directly with customers.
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