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Salary: USD 252,000 - 300,000 / annual
Invisible Technologies is seeking a Director of Engineering to lead the Data Labs organization, which builds benchmarking systems, data pipelines, and reinforcement learning environments that power the company's AI training engagements with frontier labs and enterprises.
In this hands-on strategic role, you will set the technical direction and multi-year roadmap for Data Labs, anticipating market shifts 6–18 months ahead in training methods, evaluation practices, and data infrastructure. You'll decide which capabilities become shared platform versus engagement-specific solutions, balancing reuse against team autonomy. You'll own architectural decisions for benchmarking infrastructure, data pipelines, and RL environments to ensure they remain reproducible, portable across client deployments, and ready for next-generation training techniques.
You'll build and maintain a clear view of how frontier labs and enterprises train, evaluate, and deploy models, tracking competitive landscape shifts across AI data, evaluation, and RL providers. This market intelligence feeds into investment recommendations for executive leadership and shapes new product offerings and pricing logic in partnership with product and commercial teams.
On evaluation and data strategy, you'll define Invisible's point of view on benchmarking and evaluation methodologies, ensure evaluation design ties to client success metrics, identify reusable data assets and frameworks that improve margins, and shape practices around data licensing, privacy, and contamination risk as strategic differentiators.
You'll represent engineering in executive conversations, strategic client relationships, and pre-sales, translating technical reality into clear narratives. You'll act as a trusted technical advisor to client research and engineering leaders, bringing insights back to inform the roadmap.
Organizationally, you'll lead and grow an engineering team of multiple groups including engineering managers, senior managers, forward-deployed engineers (FDEs), and Staff+ individual contributors. You'll coach managers to build sound technical judgment and strategic awareness, establish hiring pipelines that reinforce a rigorous, client-focused engineering culture, and ensure the org has the planning, quality, and operational practices needed to execute reliably.
Invisible is a profitable, rapidly growing AI services and platform company. The company reached $134M in revenue, ranked #2 fastest-growing AI company on the 2024 Inc. 5000, and raised $100M in growth capital in September 2025. The company works with clients including Microsoft, AWS, Cohere, Swiss Gear, SAIC, and the Charlotte Hornets across supply chain automation, AI-enabled simulations, and model validation.
REQUIREMENTS:
- 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
- Track record of setting multi-year technical strategy and making platform investment decisions with commercial payoff
- Deep, current understanding of the AI training and evaluation landscape, including frontier lab and enterprise approaches to 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 translate them into technical roadmap and product point of view
- Strong cross-functional partnership skills with applied research, product, and commercial teams
- Excellent executive communication and confidence acting as 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
- Visible point of view on AI data and evaluation market (talks, writing, 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
