5-7 years of experience in machine learning or related fields
Proven track record in post-training model advancement
Strong foundation in probability, statistics, and ML theory
Experience with RLHF, RLAIF, or similar methodologies
Ability to empathize with end users and understand their tasks
Responsibilities
Develop and iterate on post-training recipes for model behavior
Design evaluations that accurately capture performance metrics
Investigate and resolve discrepancies in training results
Advance alignment techniques for agent behavior in complex tasks
Measure performance scaling with data and compute, innovating when necessary
Benefits
Competitive Early-Stage Equity
Health, Dental, Vision Coverage
Unlimited PTO + Recharge Days
Catered Lunch on Tuesday & Friday, Dinners every day!
Fully Stocked Kitchen
Cutting-Edge Tech & Tools
Annual Off-sites & Monthly Events
Commuter Benefits
Cozy Office in NYC
Full Job Description
About the Role:
We're looking for research engineers to help accelerate post-training models specific to the GTM and sales domain. Our product which is used by thousands of sales reps and leaders daily across the entire lifecycle of prospecting, deal execution, expansion, and retention, and there is substantial insane demand for higher quality intelligence across these tasks. This role involves working directly with our founders to help us fine-tune intelligence and push the Pareto Frontier in our domain. What You'll Accomplish
Post-Training Recipe Development: Iterate on the full stack of datasets, training stages, and hyperparameters that determine model behavior. Measure how choices compound across evals and production performance, not just isolated benchmarks.
Evaluation Design and Integrity: Build evals that actually capture what matters. The loop never ends: define, optimize, realize the gaps, and rebuild. You'll be responsible for making numbers go up and making sure the numbers mean something.
Deep Understanding: When training produces results that don't make sense, you dig until you understand why. The goal isn't just to fix it; it's to carry that understanding forward to the next problem. This requires rapidly understanding domain context.
Alignment and Agent Behavior: Apply and advance techniques like RLHF, RLAIF, and constitutional approaches to shape how agents reason, act, and collaborate with humans in long-horizon tasks.
Scaling and Exploration: Measure how performance scales with data and compute, and develop new methodologies when existing ones hit ceilings. We expect both rigor and invention.
We are looking for evidence of:
A track record of advancing ML systems through post-training, alignment, or related methods: RLHF, RLAIF, preference modeling, reward learning, or equivalent
Strong fundamentals in probability, statistics, and ML theory. The ability to look at experimental data and distinguish real effects from noise and bugs
Evidence of original contributions or working on similar problems in industry
Unique ability to understand the end user tasks, domain, and build user empathy and intuition around judging what good, great, and bad looks like.
Benefits
Competitive Early-Stage Equity
Health, Dental, Vision Coverage
Unlimited PTO + Recharge Days
Catered Lunch on Tuesday & Friday, Dinners every day!
