Data Operations Lead

Bedrock Robotics Inc.; San Francisco (CA); On-site; USD 140,000 - 190,000; Full time

Job summary

Bedrock Robotics Inc. seeks a Data Operations Lead to own data collection for the fleet, ensuring terabytes of daily data are reliably captured and delivered. You’ll scale machine deployments, define collection strategies, and shape data center plans to support engineering needs.

You will oversee field operations across autonomous systems and work to improve data completeness, labeling, and uptime while building efficient, cost-effective data pipelines for real-world deployment.

Qualifications

• Experience coordinating large-scale data collection and processing.
• Ability to define data collection strategies and monitor execution.
• Proven track record building data pipelines for engineering teams.

Responsibilities

• Own ongoing data collection support for the fleet ensuring terabyte-scale daily data volumes are produced, retained, and delivered.
• Oversee field operations across autonomous and Bedrock Intelligence machines at engineering data standards.
• Define efficient data collection methods that meet engineers’ needs to mature models.
• Treat data yield as a key operational metric and partner with teams to close data-loss gaps.
• Develop an economic service by clarifying SLAs, simulating network strategy, and optimizing operations.
• Ensure field-side reliability of data offload, connectivity, storage, and handoff with proactive cadence.

Skills

Data operations

Data pipeline design

Field operations

Job description

Join the team bringing advanced autonomy to the built world

At Bedrock, we're moving AI out of the lab and into the real world. Our team includes veterans who helped launch Waymo, scaled Segment to a $3.2B acquisition, and grew Uber Freight to $5B in revenue. Today, we're deploying autonomous systems on heavy construction equipment across the country, improving safety on job sites and accelerating schedules on critical infrastructure projects. We're not here debating the future of AI. We're deploying it in the real world. In just two years, we've raised $350M and achieved the first fully autonomous excavator deployments in construction. This is where algorithms meet steel-toed boots. You'll work alongside construction veterans and world-class engineers to solve physical-world problems that simulations can't touch. If you're ready to do meaningful work on hard problems, we'd love to have you join us.

ABOUT THE ROLE

We are seeking a Data Operations Lead to own ongoing data collection support for the fleet - ensuring machines in the field reliably produce, retain, and deliver terabyte-scale daily data volumes. Over the next twelve months, we will rapidly scale our machine count, partners and sites while also adding net-new machine types and a next-generation platform. You will be the single-threaded owner to define our data collection strategies as well as oversee execution. You will help define how the data is collected, when the data is collected, and the data center strategy for supporting efficient upload. You will provide the critical data our Engineering teams rely on, including helping to define service level agreements and methods, monitoring, and infrastructure needs.

This is a field-forward leadership role. You will build the organization and the systems that keep data flowing, ensuring our engineering team has what they need in an efficient and economic method.

WHAT YOU'LL DO

• Own ongoing data collection support for the fleet ensuring machines in the field reliably produce, retain, and deliver the terabyte-scale daily data volumes Engineering depends on.

• Own field operations across Autonomous and Bedrock Intelligence machines at customer and engineering product standards: higher monitoring frequency, faster response, and stricter data completeness than the data collection fleet, because customers are paying for and acting on what these machines produce.

• Own field operations across Data Collection machines at engineering product standards: define efficient means of data collection that still enable engineers with the critical data they need to mature our models.

• Own data yield as a first-class operational metric alongside machine uptime: what percentage of operating hours produce usable, complete, correctly-labeled data, and what is driving the gap. Work with engineering and product to prioritize and close places where data loss happens in the pipeline.

• Build an overall economic service by ensuring SLAs are clear and costs understood, run simulations to better understand network strategy and SLA trade-offs and to optimize operations.

• Own the field-side reliability of the data pipeline offload, connectivity, storage, and handoff and the operating cadence that catches degradation before Engineering or a paying customer discovers it downstream.