Director of Engineering, Data Labs

Invisible Technologies

3d•$252,000 - $300,000•Hybrid

About The Position

Invisible Technologies is hiring a Director of Engineering to set technical strategy for Data Labs, which builds benchmarking systems, data pipelines, and reinforcement-learning environments for AI training engagements. The role requires anticipating changes in how frontier labs and enterprises train, evaluate, and deploy models, then translating those changes into a technical roadmap and a commercially relevant point of view. The Director will decide which capabilities should become shared platform investments and which should remain engagement-specific, and will set architectural direction for reproducible, portable benchmarking infrastructure, data pipelines, and RL environments. The role also shapes evaluation and data strategy, including client-defined success metrics, automated and human evaluation, reusable data assets, and data licensing, privacy, and contamination practices. This hands-on strategic leadership role leads engineering managers, forward deployed engineers, and senior individual contributors across multiple teams. It partners closely with applied research, product, commercial, and go-to-market leadership; represents engineering in executive and client conversations; and delegates day-to-day delivery to capable managers while maintaining close involvement in technical decisions and client needs. Invisible is a hybrid organization with offices and team members around the world. Most roles involve in-office collaboration and are tied to specific locations; location-based requirements and hybrid expectations will be communicated during recruiting. The stated base salary range for San Francisco and New York is $252,000–$300,000 USD annually. The position also includes a 20% annual bonus and an additional incentive structure; full-time offers include bonuses and equity. Compensation outside the U.S. is adjusted for local market conditions and cost of living.

Requirements

10+ years of software engineering experience, including significant experience building ML or AI systems.
5+ years of engineering leadership experience, including managing managers across multiple teams.
A track record of setting multi-year technical strategy and making platform investment decisions that produced commercial results.
Deep, current understanding of AI training and evaluation, including frontier-lab and enterprise approaches to fine-tuning, RLHF/RLAIF, and evaluation methodology.
Hands-on familiarity with LLM training pipelines sufficient to make and defend architectural decisions.
Experience building benchmarking, data curation, or RL infrastructure that produced measurable client outcomes.
Ability to interpret market and competitive signals and turn them into a technical roadmap and clear product point of view.
Strong cross-functional partnership with applied research, product, and commercial teams.
Excellent executive communication skills and confidence serving as a technical advisor in client and pre-sales conversations.

Nice To Haves

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 organization through rapid growth or significant change.

Responsibilities

Own the multi-year technical strategy for Data Labs and translate it into a roadmap aligned with the direction of the AI training and evaluation market.
Decide which capabilities should be shared platform investments and which should be engagement-specific, balancing reuse with team autonomy.
Set architectural direction for benchmarking infrastructure, data pipelines, and RL environments to ensure reproducibility, portability across client deployments, and readiness for new training methods.
Anticipate technical shifts, including post-training techniques, agentic evaluation, and synthetic data, and make strategic investment decisions.
Set expectations for the use of AI in design reviews and day-to-day engineering work.
Maintain a current view of how frontier labs and enterprises train, evaluate, and deploy models, and identify the data and infrastructure needs this creates.
Track competitors across AI data, evaluation, and RL environment providers and help define Invisible's differentiators.
Translate client signals, research developments, and market trends into investment recommendations for executive leadership.
Partner with product and commercial teams to shape offerings, pricing logic, and their technical positioning.
Define Invisible's point of view on benchmarking and evaluation, including pre- and post-engagement comparisons, capability gap analyses, and regression tracking.
Ensure evaluation design reflects client-defined success metrics, validate automated checks against human judgment, and clarify the limitations of each approach.
Identify reusable data assets, frameworks, and pipelines that create durable advantages and improve engagement margins.
Shape data licensing, privacy, and contamination-risk practices as strategic differentiators.
Represent engineering in executive discussions, strategic client relationships, and pre-sales, communicating technical realities clearly and honestly.
Advise client research and engineering leaders and bring insights from those relationships back into roadmap decisions.
Help leaders balance client-specific requests against platform and business goals, with clear short- and long-term paths.
Lead and grow an engineering organization across multiple teams, including engineering managers, senior managers, FDEs, and Staff+ individual contributors.
Coach managers to develop sound technical judgment and strategic awareness in their teams.
Build hiring pipelines that support a rigorous, client-focused, strategy-aware engineering culture.
Ensure the organization has the planning, quality, and operational practices needed to execute reliably, delegating day-to-day delivery to strong managers.