Director of Engineering, Data Labs

Invisibletech; United States; Hybrid; USD 210,000 - 360,000; Full time

Benefits offered by this job

Hybrid work model

Remote flexibility

Multiple offices

Job summary

Invisibletech in the United States seeks a senior engineering leader to drive multi-year technical strategy for benchmarking infrastructure, data pipelines, and reinforcement-learning environments. You will decide which capabilities become shared platform foundations and balance reuse with client needs.

You will lead multiple engineering teams, collaborate with research, product, and clients, and establish hiring and operating practices that ensure reliable delivery and measurable impact.

Qualifications

• 10+ years in software engineering, including significant work on machine learning or AI systems.
• 5+ years in engineering leadership, including management of managers across multiple teams.
• Track record setting multi-year technical strategy and making platform investments with commercial impact.
• Current understanding of AI training and evaluation, including fine-tuning, RLHF/RLAIF, and evaluation methods.
• Familiarity with LLM training pipelines and experience building benchmarking, data curation, or reinforcement-learning infrastructure.
• Strong executive communication and cross-functional partnership skills, including confidence advising clients and contributing to pre-sales discussions.

Responsibilities

• Define a multi-year technical roadmap for benchmarking infrastructure, data pipelines, and reinforcement-learning environments.
• Make platform investment and architecture decisions that balance reuse, portability, reproducibility, and team autonomy.
• Track changes in AI training and evaluation practices, including post-training, agent evaluation, and synthetic data, and turn findings into investment recommendations.
• Shape approaches to benchmarking, evaluation, data curation, and reusable assets, tying evaluation to client-defined measures and human judgment.
• Partner with research, product, commercial, and client leaders to shape offerings and explain technical trade-offs.
• Lead and grow managers, forward-deployed engineers, and senior individual contributors; build hiring and operating practices that support reliable delivery.

Skills

Software engineering

AI/ML systems

Engineering leadership

Executive communication

Cross-functional partnership

Pre-sales discussions

Job description

Role overview

Set technical strategy for an engineering group building benchmarking systems, data pipelines, and reinforcement-learning environments for AI training and evaluation work. You’ll decide which capabilities should become shared platform foundations and which should remain tailored to individual engagements, while anticipating changes in training methods and client needs. The role leads multiple engineering teams and stays close to architecture, market signals, and client outcomes.

Responsibilities

• Define a multi-year technical roadmap for benchmarking infrastructure, data pipelines, and reinforcement-learning environments.
• Make platform investment and architecture decisions that balance reuse, portability, reproducibility, and team autonomy.
• Track changes in AI training and evaluation practices, including post-training, agent evaluation, and synthetic data, and turn findings into investment recommendations.
• Shape approaches to benchmarking, evaluation, data curation, and reusable assets, tying evaluation to client-defined measures and human judgment.
• Partner with research, product, commercial, and client leaders to shape offerings and explain technical trade-offs.
• Lead and grow managers, forward-deployed engineers, and senior individual contributors; build hiring and operating practices that support reliable delivery.

Requirements

• 10+ years in software engineering, including significant work on machine learning or AI systems.
• 5+ years in engineering leadership, including management of managers across multiple teams.
• Track record setting multi-year technical strategy and making platform investments with commercial impact.
• Current understanding of AI training and evaluation, including fine-tuning, RLHF/RLAIF, and evaluation methods.
• Familiarity with LLM training pipelines and experience building benchmarking, data curation, or reinforcement-learning infrastructure.
• Strong executive communication and cross-functional partnership skills, including confidence advising clients and contributing to pre-sales discussions.

Nice to have

• Experience at an AI services firm, applied research consultancy, frontier lab, or enterprise AI product company; a public point of view through writing, talks, or open-source work; or familiarity with data licensing and privacy considerations.

Benefits and work setup

The organization describes itself as hybrid, with offices and teams in multiple locations. Remote flexibility and location expectations depend on the role and are discussed during recruiting.