Own the modeling work behind June’s AI interview evaluation engine. Build, evaluate, and deploy models that score and summarize interviews, develop offline and online evaluation infrastructure, and monitor calibration, drift, and bias. Partner with engineering and product to integrate models into the product and improve them through real-world usage. Help establish data-team tooling and standards while working with LLMs, NLP, ambiguous data, and fairness considerations.
About June
The role
What you'll do
Hiring is still one of the slowest, most inconsistent processes inside any company. June is changing that. Our AI Interviewer runs first-round conversations with candidates at scale — structured, consistent, and fast — then hands recruiters a clear evaluation instead of a stack of resumes. We plug into the ATS teams already use (Gem, Greenhouse, Ashby, Lever, Workday, and 80+ others), and teams using June are filling roles dramatically faster than they were before.
Every interview we run makes the product smarter. That's the opportunity here.
We're looking for a Senior Data Scientist to own the modeling work behind our evaluation engine. You'll be joining a small data team — small enough that what you build ships, and you'll see it affect real hiring decisions within weeks, not quarters.
This is a modeling-heavy role. You'll work on the hard, central problem at June: turning unstructured interview conversations into signals that are accurate, fair, and that hiring teams actually trust.
- Build, evaluate, and ship models that score and summarize candidate interviews — from problem framing through production
- Design the evaluation infrastructure that tells us whether a model is actually better: offline benchmarks, human-labeled ground truth, and online measurement
- Dig into calibration, drift, and bias. Our models influence people's careers, and we hold them to a high standard
- Partner directly with engineering and product to get models into the product, then iterate on what real usage teaches us
- Help shape how the data team works as it grows — tooling, standards, and what we choose to measure
- 5+ years building and shipping ML models in production, not just in notebooks
- Strong applied experience with LLMs or NLP — fine-tuning, evaluation, prompting, retrieval, or all of the above
- Fluency in Python and SQL, and real comfort with the messy middle: ambiguous data, unclear labels, no obvious benchmark
- A rigorous instinct for evaluation. You're skeptical of your own results before anyone else is
- Clear communication with non-technical partners. You can explain why a model made a call to someone who has to defend it to a candidate
- Comfort working at startup pace, with the scope and ambiguity that comes with it
- Experience with fairness, bias measurement, or working under regulatory constraints (EEOC, NYC Local Law 144, EU AI Act)
- Background in HR tech, marketplaces, or another domain where model decisions carry real weight for individuals
- Experience standing up data infrastructure early at a company
You'll work on a product where the model is the product, with a short line between your work and the outcome. We move quickly, we're in the office together, and we're building something that changes how hiring actually works.
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