AI Software Engineer
At a glance
Mid-level AI Engineering role at Rockstar. San Francisco · no salary stated · we estimate $183,750–$246,250.
Rockstar has not published a salary for this role. The figure below is our estimate, not theirs.
aimlroles summary, based on the employer's posting.
What you'll do
- Build the agent framework for routing tools, applying policies, retaining memory and grounding actions
- Turn conflicting information from emails, documents and legacy systems into a shared context graph
- Develop evaluation and reliability systems to make agents safer when handling financial actions
- Ship production APIs, interfaces and infrastructure, shaping the platform around enterprise customer workflows
What you bring
- At least four years as an engineer in environments focused on high-quality work
- You have built agent applications hands-on, including tool use, MCP and context engineering
- Strong Python or TypeScript skills, or a comparable language, plus familiarity with AWS or GCP
- You can weigh model options to balance reliability and cost, rather than optimize for a flashy demo
Who this fits
This suits an early-stage engineer who wants substantial product ownership, direct contact with enterprise customer operations and responsibility for production systems. The role is based on-site in San Francisco.
From the employer
Rockstar is recruiting for a well-funded seed-stage startup building the Agentic Execution System for global trade. Their agents don't assist with the work — they do it, reasoning over a live context graph of the business and executing actions with real financial consequence. They move real goods and real money for some of the largest enterprises in the world. The company is backed by leading VCs in NYC and SF, and the team brings deep experience across supply chain, financial technology, and applied AI.
Location: San Francisco — On-site
Employment Type: Full Time
About the Company
AI has learned to write code, draft contracts, and answer questions. It hasn't yet learned to run the physical world.
Global trade is the $10T proof of that gap. The physical world is a complex web of supply chains. Everything you own crossed oceans and borders to reach you, coordinated almost entirely by people. The data is too messy and too fragmented for traditional software to meaningfully automate, so the work stays manual and the cost of every mistake surfaces months too late.
The company is building the Agentic Execution System for global trade. Their agents don't assist with the work. They do it, reasoning over a live context graph of the business and executing actions with real financial consequence. They move real goods and real money for some of the largest enterprises in the world.
The company is backed by an oversubscribed seed round from leading VCs in NYC and SF, and the team brings deep experience across supply chain, financial technology, and applied AI.
The Role
As an early AI Engineer, the engineer owns the core of the system: the agentic harness that handles tool routing, policy, and memory, and the context graph it reasons over. The central challenge is reliability. These agents act on real money, on top of data that's messy, contradictory, and spread across many parties, and an agent that's right 95% of the time isn't good enough. Much of what this requires hasn't been solved anywhere yet.
The role comes with real product ownership. The engineer ships to production, works directly with how enterprise customers operate, and shapes what the platform becomes. The scope looks more like an early technical founder's than a spec to implement.
What the Engineer Will Build
- The agentic harness at the center of the platform: tool routing, policy enforcement, memory, and grounding for agents that execute real work end-to-end.
- A context graph of the physical economy: ingesting unstructured, contradictory data from emails, documents, and legacy systems and resolving it into a single source of truth.
- Evaluation and reliability infrastructure that makes agent reasoning safe enough for financial actions.
- The technical foundation of APIs, interfaces, and infrastructure that scales with customer usage and product complexity.
What the Company Requires
- 4+ years of experience in high-quality engineering environments
- Hands-on experience building agent applications: tool calling, MCP, context engineering
- Strong proficiency in Python, TypeScript, or similar; comfort with AWS/GCP
- Judgment about model tradeoffs: the ability to build the most reliable, cost-efficient system, not just the most impressive demo
What the Company Values
- Experience with LLM post-training and evaluation
- Proficiency with Neo4j or other graph databases (or the drive to learn fast)
- Startup experience: the ability to think like a PM and an engineer in the same conversation
- High ownership, direct communication, and a bias for action
- Interest in recruiting and mentoring future engineers