Head of Data Labs Engineering & AI Platform

Invisible Technologies Inc.; New York, San Francisco (NY, CA); Hybrid; USD 252,000 - 300,000; Full time

Job summary

Invisible Technologies Inc. is seeking a Director of Engineering for Data Labs to steer the benchmarking systems, data pipelines, and RL environments behind AI training engagements.

You will define the technical roadmap, balance platform investments with engagement-specific solutions, and lead a team of engineers and senior ICs while collaborating with research, product, and go-to-market teams. You will shape long-term AI data and evaluation strategies, ensuring reproducibility across

Qualifications

• 10+ years in software engineering.
• 5+ years in engineering leadership.
• Proven track record setting multi-year tech strategy and platform investments.
• Deep understanding of the AI training and evaluation landscape.
• Hands-on familiarity with LLM training pipelines and RL infrastructure.

Responsibilities

• Own the multi-year technical strategy for Data Labs and translate into a roadmap.
• Define shared platform vs engagement-specific solutions and balance reuse with autonomy.
• Set architecture direction for benchmarking, data pipelines, and RL environments.
• Identify reusable data assets, frameworks, and pipelines to improve margins.
• Partner with product and commercial teams to shape offerings and the technical narrative.
• Represent engineering in executive conversations and pre-sales discussions.

Skills

ML/AI systems

Engineering leadership

Executive communication

Architectural design

Client engagement

Job description

Invisible Technologies Inc. is seeking a Director of Engineering for Data Labs to steer the benchmarking systems, data pipelines, and RL environments behind AI training engagements.

You will define the technical roadmap, balance platform investments with engagement-specific solutions, and lead a team of engineers and senior ICs while collaborating with research, product, and go-to-market teams. You will shape long-term AI data and evaluation strategies, ensuring reproducibility across