Note: The job is a remote job and is open to candidates in USA. Invisible Technologies is hiring a Director of Engineering to set the technical strategy and roadmap for Data Labs, which builds benchmarking systems, data pipelines, and RL environments for AI training engagements. The role leads engineering teams, guides platform and evaluation strategy, and partners with clients and internal research, product, and commercial leaders.
Responsibilities
- 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
- 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
- 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
- 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
- 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
Skills
- 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
- 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
Benefits
- 20% annual bonus
- Additional incentive structure
- Equity included in all full-time offers
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