Director of Engineering, Data Labs

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

• Represent engineering in executive conversations, strategic client relationships, and pre-sales, translating technical reality into clear, honest narratives.

• Act as a trusted technical advisor to client research and engineering leaders, bringing back what you learn to inform the roadmap.

• Help leaders weigh client-specific asks against broader platform and business goals, with clear short- and long-term paths.

Organizational Leadership

• Lead and grow an engineering org of multiple teams, including engineering managers, senior managers, FDEs, and Staff+ ICs.

• Coach managers to build sound technical judgment and strategic awareness in their teams.

• Build hiring pipelines that reinforce a rigorous, client-focused, strategy-aware engineering culture.

• Make sure the org has the planning, quality, and operational practices it needs to execute on the strategy reliably, delegating day-to-day delivery to strong managers.

What We're Looking For

• 10+ years in software engineering, with significant experience building ML or AI systems.

• 5+ years in engineering leadership, including experience managing managers across multiple teams.

• A track record of setting multi-year technical strategy and making platform investment decisions that paid off commercially.

• Deep, current understanding of the AI training and evaluation landscape, including how frontier labs and enterprises approach fine-tuning, RLHF/RLAIF, and evaluation methodology.

• Hands-on familiarity with LLM training pipelines, deep enough to make and defend architectural decisions.

• Experience building benchmarking, data curation, or RL infrastructure that produced measurable client outcomes.

• Ability to read market and competitive signals and turn them into a technical roadmap and a clear product point of view.

• Strong cross-functional partnership with applied research, product, and commercial teams.

• Excellent executive communication, and confidence acting as a technical advisor in client and pre-sales conversations.

Nice to Have

• Prior experience at an AI services firm, applied research consultancy, frontier lab, or enterprise AI product company.

• A visible point of view on the AI data and evaluation market, such as talks, writing, or open-source work.

• Familiarity with data licensing, synthetic data generation, or contamination detection involving client data.

• Experience shaping new product lines or service offerings from a technical foundation.

• Experience leading an org through rapid growth or significant change.

What’s In It For You

Invisible is committed to fair and competitive pay, ensuring that compensation reflects both market conditions and the value each team member brings. Our salary structure accounts for regional differences in cost of living while maintaining internal equity.

For this position, the annual salary will include a 20% annual bonus and an additional incentive structure. The base salary range for SF/NY are:

Salary Range

$252,000 — $300,000 USD

Bonus and equity is included in all full-time, non commission offers. Final compensation is determined by a combination of factors, including location, job-related experience, skills, knowledge, internal pay equity, and overall market conditions. Because of this, every offer is unique. Additional details on total compensation and benefits will be discussed during the hiring process.

You can find more information about our geographic pay tiers here . During the interview process, your Invisible Talent Acquisition Partner will confirm which tier applies to your location. For candidates outside the U.S., compensation is adjusted to reflect local market conditions and cost of living.

What It's Like to Work at Invisible:

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