Applied AI Engineering Intern

New York City Areainternshipentry

OtherAi Engineering Intern

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Quick Summary

Key Responsibilities

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,

Requirements Summary

NetworkPolicy, Helm, pod lifecycle. The runtime runs on it and every data source is wired through it. - P

Technical Tools

OtherAi Engineering Intern

What We Offer

~2 min read

Blockhouse is an institutional trading infrastructure platform for high and mid frequency trading strategies across centralized and decentralized perpetual venues - across all asset classes.

We run an AI agent for the desk. It lives in Slack and works through about thirty read-only tools against the firm's front, middle, and back office systems, alongside GitHub, Linear, and Slack. Skills teach it how each source is read, workflows run it unattended, and when the team agrees on a fix it opens the pull request. Today it answers the desk's questions about the portfolio and its risk, checks what the systems say against each other, drafts client reporting, drives a research loop and its backtests, and writes each weekday morning's report on what engineering shipped and where work is stuck. The runtime underneath is open source and self-hosted; everything on top is ours, and each new source it reaches adds capability without rework. The goal is an agent that closes the full loop a fund runs on, from finding an edge to trading and managing risk; it runs the operating layer today.

- 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

The internship is on the harness: everything around the model that makes an agent with real credentials safe to run on a real book. You will work directly with the founders and the engineers who own the systems the agent reads.

Responsibilities

~1 min read

- 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

Requirements

~2 min read

- 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