Data Collection Lab Manager

About Generalist

At Generalist, we are on a mission to build general intelligence for the physical world and make it useful to everyone. We believe the industries and homes of the future will depend on humans and machines working together in new ways. Robots can help us build more and get more done.

We build embodied foundation models, starting with a focus on dexterity. This requires advancing the frontiers of data, models, and hardware, to enable robots to intelligently interact with the physical world.

The company embraces both large-scale AI and robotics as core to its DNA. Our team of researchers, roboticists, and company builders come from OpenAI, Boston Dynamics, Google DeepMind, and other frontier labs—with a track record of shipping AI breakthroughs. Before Generalist, we pioneered large embodied multimodal models and vision-language-action models (PaLM-E, RT-2, Gemini Robotics), launched and scaled ChatGPT and GPT-4 to hundreds of millions of users, engineered the foundations of autonomous driving, built next-generation robots (Atlas, Spot, Stretch) and pushed the limits of what they can do (from parkour to manipulation, and testing robustness).

We are an equal opportunity employer, and we do not discriminate on the basis of race, religion, color, national origin, sex, sexual orientation, age, veteran status, disability, genetic information, or other applicable legally protected characteristic.

Data Collection Lab Manager (Shift Leader)

Generalist is looking for a Data Collection Lab Manager to lead and evolve the operations behind our on-site data collection efforts.

You’ll act as a shift leader to own the systems, processes, and team responsible for delivering high-quality data that supports model development across the organization. This is a highly operational, hands-on , in-lab role for someone who enjoys building structure in ambiguous environments, improving systems over time, and leading teams with high standards and accountability.

The right person for this role is equally comfortable jumping into operational problems, managing people, improving process quality, and building the infrastructure needed to scale reliably.

Responsibilities

Lab Operations:

• Own the refinement and execution of day-to-day DataLab operations and ensure reliable, high-quality data delivery

• Manage task rollout and retirement scheduling based on organizational priorities

• Establish and improve QA processes to maintain high data quality standards

• Identify operational bottlenecks and implement scalable process improvements

• Oversee device and hardware management to ensure uninterrupted lab operations

• Manage lab inventory including ordering, receiving, storage, distribution, and removal

• Maintain operational oversight of data custody, tracking, reporting, and issue investigation

• Build and maintain operational systems, trackers, reporting cadences, and documentation

Team Leadership:

• Recruit, hire, and onboard high-performing data collection staff

• Support and manage lab leads in day-to-day operational execution

• Coach, mentor, and develop DataLab employees

• Build a strong operational culture centered around ownership, reliability, and continuous improvement

• Help define team structure and operational processes as organizational needs evolve

Qualifications:

• 5+ years of experience in high-performance operational environments with significant ownership and accountability

• Experience managing teams in fast-moving or semi-structured organizations

• Strong operational instincts and attention to detail

• Proven ability to build processes and improve systems in ambiguous environments

• Excellent written and verbal communication skills

• Strong interpersonal and people leadership skills

• Comfortable using metrics, operational reporting, and process tracking to drive decisions

• High reliability, strong follow-through, and a consistent ownership mindset

Nice to Have:

• Experience managing physical operations, labs, logistics, or production environments

• Experience with QA or operational quality systems

• Startup experience

• Familiarity with AI/ML data operations or human-in-the-loop workflows