Job description

Haize Labs gets LLM apps out of POCs and into production. We eliminate the risk and improve the reliability of LLM apps by haizing them -- i.e. rigorously, proactively, and continuously fuzz-testing them. We are looking for Software Engineering Interns to help develop our reliability platform, with a focus on: Data-efficient alignment of evaluation models Dynamic testing of AI applications Observability and anomaly detection Discrete optimization (with applications in architecture search and automated prompting) Our work is both intellectually stimulating and practically useful. Your work will result in net-new primitives, frameworks, and algorithms for developing robust LLM applications. You work will directly influence how LLM apps are tested, verified, and deployed everywhere. Annual Salary $100,000 – $125,000 USD Responsibilities Work directly with customers to adapt our core R&D for different domains. Build out core infra, cloud tooling, and UX around our algorithms. Ship delightful tools that are used by AI application developers all across the world. Qualifications High-agency, customer-centric full-stack experience, e.g. ex-founder or ex-founding engineer. Strong open source presence or strong track record of software engineering projects and employment. Experience with ML in an applied setting. Can ramp up quickly to understand our research. Logistics Location policy: In NYC. US visa sponsorship: If you are exceptional, we will sponsor. Compensation and Benefits: We provide generous salary, equity, and benefits We're Not Here to Play Games. We're not here to write GPT wrappers or get rich quick off the AI bubble. We're here to solve the hardest problem in AI: making it safe, reliable, and production-ready. Since our company's inception in 2024, we've amassed amazing customers like OpenAI, Anthropic, AI21, and several others. We've developed best-in-class tooling for evaluation, dynamic testing, red-teaming, observability, and continuous robustification. And we’re backed + advised by the founders of Cognition, Hugging Face, Weights and Biases, Nous, Etched, Okta, Replit and C-suite execs from Google, Stripe, Databricks, Robinhood, and more. Our core team is exceptionally fit for this mission. We turned down Stanford PhDs, got into & rejected Y Combinator, wrote ML-guided matchmaking for 50,000+ students, built an educational nonprofit supporting 60 countries, and did some other cool things along the way. Our early hires include an MIT PhD with 21,000+ Physics/ML/Stats citations, a Datadog engineering manager who led their GenAI observability team, a Citadel quant with a huge open-source presence, and more. We can only serve our mission with an incredibly high talent-density team. Come here to push yourself, learn fast, experience excellence, grow with each other, and pursue your life's work.

Requirements

Must-haves

Haize Labs gets LLM apps out of POCs and into production. We eliminate the risk and improve the reliability of LLM apps by haizing them -- i.e. rigorously, proactively, and continuously fuzz-testing them.
We are looking for Software Engineering Interns to help develop our reliability platform, with a focus on:
Data-efficient alignment of evaluation models
Dynamic testing of AI applications
Observability and anomaly detection
Discrete optimization (with applications in architecture search and automated prompting)
Annual Salary
Work directly with customers to adapt our core R&D for different domains.
Build out core infra, cloud tooling, and UX around our algorithms.
Ship delightful tools that are used by AI application developers all across the world.
High-agency, customer-centric full-stack experience, e.g. ex-founder or ex-founding engineer.
Strong open source presence or strong track record of software engineering projects and employment.
Experience with ML in an applied setting.
Can ramp up quickly to understand our research.
Location policy: In NYC.
US visa sponsorship: If you are exceptional, we will sponsor.
Compensation and Benefits: We provide generous salary, equity, and benefits
We're Not Here to Play Games.
We're not here to write GPT wrappers or get rich quick off the AI bubble. We're here to solve the hardest problem in AI: making it safe, reliable, and production-ready.
We can only serve our mission with an incredibly high talent-density team. Come here to push yourself, learn fast, experience excellence, grow with each other, and pursue your life's work.

Nice-to-haves

None flagged.

Application prep

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Keywords to mirror

llmaiml

Platform: greenhouse
Effort: quick