Note: The job is a remote job and is open to candidates in USA. Blockhouse is an institutional trading infrastructure platform for high- and mid-frequency trading strategies across centralized and decentralized perpetual venues and asset classes. The Applied AI Engineering Intern will build and improve the harness around an AI agent, including data-source integrations, orchestration, evaluation, isolation, credential boundaries, and durable workflows.
Responsibilities
- Add a data source end to end: the tool that reads it, the skill that teaches the agent to use it, and the eval that checks the answers
- Extend the orchestration layer: scheduled runs, durable multi-step workflows, delivery back into Slack
- Extend the eval layer: grounded checks, validating the LLM judge against human labels, per-workflow token metering
- Move a rule out of the standing prompt into a tool and show the prompt got shorter
- Work with a senior engineer on the isolation and credential boundary
Skills
- - You have built an LLM agent with tool use and multi-step turns that ran for real users, even a handful. You can explain what it does when the process dies mid tool call, how it chooses a tool, and what a turn costs
- - Python and Rust. Everything we have built on the runtime is Python; the runtime itself is Rust, and you will change both
- - Linux, Docker, git, and Kubernetes: NetworkPolicy, Helm, pod lifecycle. The runtime runs on it and every data source is wired through it
- - Postgres as a durable state store: transactions, idempotent writes, checkpoint and replay
- - Claude Code and the Anthropic API: tool definitions, skills, prompt caching, and how a harness loop is driven from a CLI
- - You understand why an agent should hold a placeholder instead of a key, why a keyword match over live answers is not an eval, and why an LLM judge is unvalidated until a human grades a sample of its verdicts
- - You prefer small, verified changes to rewrites, and you write them so someone who was not there can follow the reasoning
- - Contributions to an open-source agent runtime or coding-agent harness
- - You have built a Slack app: Events API, request signing, delivery of long-running replies
- - Security engineering at a network boundary: TLS-intercepting proxies, credential injection, egress allowlists
- - Published research or serious independent work on agent evaluation or multi-agent coordination
- - Trading systems: PnL, ledgers, positions, funding, and why a figure is relayed from the system that owns it
Benefits
- Meal / transportation stipend
- Exposure to live trading book, and the desk reads what the agent writes every weekday
- Immigration support (H-1B, F-1).
- Full-time offers for interns who ship
- Evaluation for equity and PnL split
- Remote work for US time zones; NYC in-person or hybrid preferred
Company Overview