Director of Engineering, Data Labs

PromptCube; United States; Remote; USD 252,000 - 300,000; Full time

Job summary

Invisible Technologies is seeking a Director of Engineering for Data Labs, the team building benchmarking systems, data pipelines, and RL environments for AI training engagements. The role involves multi-year technical strategy, platform investments, and leading engineering managers, FDEs, and senior ICs, while advising executives and clients.

This is a remote US role. You will determine platform vs engagement investments, drive architectural decisions, and shape data strategies to accelerate

Qualifications

• 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 platform investments with commercial payoff.
• Deep understanding of AI training/evaluation landscape, including fine-tuning and evaluation methodologies.
• Hands-on familiarity with LLM training pipelines to make and defend architectural decisions.

Responsibilities

• Own Data Labs' multi-year technical strategy and translate it into a roadmap tied to where the AI training and evaluation market is heading.
• Set architectural direction for benchmarking infrastructure, data pipelines, and RL environments to remain reproducible and portable across client deployments.
• Lead multiple teams through engineering managers, FDEs, and Staff+ ICs; coach managers and build hiring pipelines.
• Represent engineering in executive conversations and pre-sales as a trusted technical advisor to client leaders.

Skills

Software engineering

Engineering leadership

Technical strategy

AI/ML systems

LLM pipelines

Tools

Benchmarking infrastructure

Data pipelines

RL environments

Job description

Role overview

Invisible Technologies, an AI platform and services company, is hiring a Director of Engineering for Data Labs, the team building benchmarking systems, data pipelines, and reinforcement-learning environments for AI training engagements. The role sets multi-year technical strategy and platform investment direction while leading engineering managers, forward-deployed engineers, and senior ICs and advising executives and clients. The position is listed as United States – Remote.

Responsibilities

• Own Data Labs' multi-year technical strategy 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 shared systems speed teams up without creating over‑dependency.
• Set architectural direction for benchmarking infrastructure, data pipelines, and RL environments so they remain 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.
• Build a current view of how frontier labs and enterprises train, evaluate, and deploy models; track competitors across AI data, evaluation, and RL environments; and turn client signals, research, and market trends into investment recommendations and differentiated offerings.
• Define Invisible's benchmarking and evaluation approach, including pre‑and post‑engagement comparisons, capability-gap analyses, and regression tracking; tie evaluation design to client success metrics and validate automated checks against human judgment.
• Identify reusable data assets, frameworks, and pipelines that create durable advantages and improve margins, and shape practices for data licensing, privacy, and contamination risk as a strategic differentiator.
• Lead multiple teams through engineering managers, senior managers, FDEs, and Staff+ ICs; coach managers, build hiring pipelines, establish planning, quality, and operational practices, and delegate day‑to‑day delivery.
• Represent engineering in executive conversations, strategic client relationships, and pre‑sales, acting as a trusted technical advisor to client research and engineering leaders and feeding what you learn back into the roadmap.

Qualifications

• 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, with confidence acting as a technical advisor in client and pre‑sales conversations.

Application conditions

• The listed location is United States – Remote; the employer states that remote work does not imply worldwide eligibility.
• Residency, work authorization, time‑zone, language, and travel requirements are not stated in the posting and should be confirmed with Invisible.
• Invisible describes itself as a hybrid organization with offices and team members worldwide; its general hiring guidelines say most positions involve in‑office collaboration and are tied to specific locations, with any location‑based or hybrid expectations communicated by Talent Acquisition during recruiting.
• The posting does not state an employment type or contract duration.
• Hiring may use AI to support the interview process, and Invisible may use automated decision‑maker technologies to filter candidates based on application‑question responses and other provided information; candidates who object can contact the company.
• Applicants who need an accommodation for the application or interview process can submit a request through Invisible's accommodation form.

Preferred qualifications

• 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.

Compensation and benefits

• The base salary range for SF/NY is $252,000–$300,000 USD per year.
• The annual salary includes a 20% annual bonus and an additional incentive structure.
• Bonuses and equity are included in all full‑time offers.
• Pay is structured in geographic tiers; an Invisible Talent Acquisition Partner confirms the applicable tier during the interview process.
• For candidates outside the U.S., compensation is adjusted to reflect local market conditions and cost of living.
• Final compensation depends on location, job‑related experience, skills, knowledge, internal pay equity, and overall market conditions; additional total‑compensation and benefits details are discussed during hiring.

Employer-published remote role. Listed location(s): United States

• Remote. Remote does not imply worldwide eligibility. Check the original listing for residency, work authorization, time zone, language and travel requirements.

About Invisible

Invisible Technologies makes AI work. Our end‑to‑end AI platform structures messy data, automates digital workflows, deploys agentic solutions, measures outcomes, and integrates human expertise where it matters most.

Our platform cleans, labels, and structures company data so it is ready for AI. It adapts models to each business and adds human expertise when needed, the same approach we have used to improve models for more than 80% of the world’s top AI companies, including Microsoft, AWS, and Cohere.

Our successes span industries, from supply chain automation for Swiss Gear to AI‑enabled naval simulations with SAIC, and validating NBA draft picks for the Charlotte Hornets.

Profitable for more than half a decade, Invisible reached $134M in revenue and ranked as the number two fastest growing AI company on the 2024 Inc. 5000. In September 2025, we raised $100M in growth capital to accelerate our mission of making AI actually work in the enterprise and to advance our platform technology.

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 organization 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

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.

*Bonuses and equity are included in all full‑time 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.

What It’s Like to Work at Invisible:

At Invisible, we’re not just redefining work—we’re reinventing it. We operate at the intersection of advanced AI and human ingenuity, pushing the boundaries of what’s possible to unlock productivity and scale. Ownership is at the core of everything we do. Here, you won’t just execute tasks—you’ll build, innovate, and shape the future alongside world‑class clients pushing the boundaries of AI.

We expect bold ideas, relentless drive, and the ability to turn ambiguity into opportunity. The pace is fast, the challenges are big, and the growth is unmatched. We’re not for everyone, and we’re okay with that. If you’re looking for predictable routines, this isn’t the place for you. But if you’re driven to create, thrive in dynamic environments, and want a front‑row seat to the AI revolution, you’ll fit right in.

Country Hiring Guidelines:

Invisible is a hybrid organization with offices and team members located around the world. While some roles may offer remote flexibility, most positions involve in‑office collaboration and are tied to specific locations. Any location‑based requirements or hybrid expectations will be communicated by our Talent Acquisition team during the recruiting process.

AI Interviewing Guidelines:

Our hiring team thoughtfully uses AI to support an efficient, engaging, and inclusive interview process. Since AI can also be a helpful tool for candidates, we've outlined expectations for using it ethically throughout your interview journey.

Accessibility Statement:

We are committed to providing reasonable accommodations for individuals with disabilities. If you require an accommodation to participate in the application or interview process, please submit your request using our accommodation request form. A member of our team will follow up to support your request.

Equal Opportunity Statement:

We’re an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, disability, or veteran status, or any other basis protected by law.

Due to a high volume of candidates, Invisible may use automated decision‑maker technologies to filter candidates based on response to our application questions and other provided information. Our use of automated decision‑making enables us to be efficient by providing a manageable list of possible candidates that meet our mandatory hiring criteria. If you object to our use of automated decision‑making please contact us.