Director of Engineering, Data Labs at Invisible Technologies in New York, NY

Job description

About Invisible Invisible is an Applied AI research company closing the gap between frontier models and the enterprise. We provide the platform infrastructure that makes AI work in production at scale: unify fragmented data, map complex processes, orchestrate and evaluate agents, and stand up hyper-personalized workflow applications fast for companies like Grubhub, Alliance Global, and Thomson Reuters. Where models fall short, we build RL environments and data to fix them. The result: a virtuous cycle between enterprise deployment and model training. Recognized as Best Predictive Analytics Platform of the Year at the 2026 AI Breakthrough Awards, Invisible makes AI work. About the Role We're looking for a Director of Engineering to set the technical strategy for Data Labs, the team that builds the benchmarking systems, data pipelines, and RL environments behind our AI training engagements. The frontier labs and enterprises we work with are changing how they train, evaluate, and deploy models every quarter. Your job is to see where that market is going and make sure Data Labs is built for it before our clients ask. You'll decide which capabilities we invest in, which we standardize into platform, and which we leave to individual engagements. You'll track how training methods, evaluation practices, and data needs are shifting across the industry, and turn that into a technical roadmap and a point of view our commercial and research teams can sell against. You'll lead a group of engineering managers, forward deployed engineers (FDEs), and senior ICs, and partner closely with applied research, product, and go-to-market leadership. This is a hands-on strategic role. You won't spend much time writing code, but you'll stay close enough to the technical work to make and defend hard architectural calls, and close enough to clients to know which calls matter. What You'll Do Technical Strategy & Roadmap Own the multi-year technical strategy for Data Labs, and translate it into a roadmap tied to where the AI training and evaluation market is heading. Decide where to build shared platform versus engagement-specific solutions, weighing reuse against team autonomy so platforms speed teams up rather than creating over-dependency. Set architectural direction for benchmarking infrastructure, data pipelines, and RL environments so they stay reproducible, portable across client deployments, and ready for the next generation of training methods. Anticipate technical shifts 6–18 months out, such as new post-training techniques, agentic evaluation, and synthetic data, and place the bets that keep Invisible ahead of them. Set expectations for how AI is used in design reviews and day-to-day engineering work. Market Intelligence & Competitive Positioning Build a clear, current view of how frontier labs and enterprises are training, evaluating, and deploying models, and what that means for the data and infrastructure they'll need from us. Track the competitive landscape across AI data, evaluation, and RL environment providers, and help define where Invisible differentiates. Turn client signals, research developments, and market trends into investment recommendations for executive leadership. Partner with product and commercial teams to shape new offerings, pricing logic, and the technical story behind them. Evaluation & Data Strategy Define Invisible's point of view on benchmarking and evaluation, including pre- and post-engagement comparisons, capability gap analyses, and regression tracking. Make sure evaluation design ties to client-defined success metrics, validate automated checks against human judgment, and be explicit about what each approach can't see. Identify reusable data assets, frameworks, and pipelines that become durable advantages and improve margins across engagements. Shape practices for data licensing, privacy, and contamination risk as a strategic differentiator, not just a compliance requirement. Client & Executive Engagement Represe