Research Engineer, Post-Training

About Actively AI Actively AI is defining a new category: Intelligence-Led Revenue. Revenue organizations have always been bottlenecked on human capacity. Reps triage which accounts get attention. Context disappears at every handoff. On any given day, the vast majority of accounts have exactly zero people thinking about them. Actively addresses this at the structural level. Our platform deploys Per-Account AgentsTM across our customers’ TAM, working 24/7 to research, identify opportunities, and advance next steps without being asked. Leading enterprises including Ramp, Ironclad, and Samsara are already making this shift. Our co-founders are former Stanford AI researchers, and the team comes from Harvard, CMU, Berkeley, Brex, Scale AI, and Google. We've raised $68M from TCV, First Harmonic, Bain Capital Ventures, First Round Capital, and more. 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 (preferably in startup environments). At least 3 years of full-time experience. Unique ability to understand the end user tasks, domain, and build user empathy and intuition around judging what good, great, and bad looks like. Why Join At Actively, you write playbooks rather than follow them. You own outcomes, not just tasks, and see directly how your work changes what customers can do — across a product and go-to-market motion that is scaling fast. The team is high-caliber and low-ego: people who work from first principles, move with urgency, and care deeply about building something that drives real value. If that's the kind of challenge you're looking for, Actively is the right place. 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