Your Impact at LILA

We're building autonomous robotic systems that serve as the intelligent physical infrastructure of our Scientific Superintelligence Platform at Lila. To support our robotics research roadmap, we need to scale robot data collection to produce high-quality robot data, collected by humans, at scale, day after day while improving per-operator throughput, lowering churn, and keeping data quality consistent across every supervisor and shift

We're hiring an Operations Manager to own that scale-up end-to-end.

As Data Operations Manager, you'll own the day-to-day function that generates the training data powering Lila's robotic systems. That means managing the supervisors who run our data collection shifts, setting the conditions for consistent operator performance, and making sure every hour of work on the floor translates into high-quality, usable data for our research team.

This is a builder role. The team is early — a growing group of operators across two shifts — and the processes, metrics, and programs that will scale it are still being shaped. You'll design and own them. You'll report directly to the Robotics team and serve as the primary operational link between the floor and Robotics Research.

What You'll Be Building

• Run the operation: Own the day-to-day operation across all shifts. Set throughput targets, monitor adherence, debug when shifts under-deliver, and make weekly calls on staffing, scheduling, and task allocation
• Manage the team: You'll manage the supervisors who directly run our operators. Set clear goals, run a consistent performance loop, and grow the supervisory bench as shifts, and sites, expand
• Drive retention and engagement: Data collection is physical, repetitive, attention-heavy work. Design and continuously refine onboarding, training, performance incentives, recognition, and scheduling so operators stay engaged and the bar keeps rising. Approach this with a product mindset: hypothesize, ship, measure, iterate
• Own throughput and quality metrics. Set targets, build the dashboards, and run the review cadence that make performance visible across the team. Reduce reject rates, compress reset time, and increase usable hours per shift
• Plan capacity: Translate research priorities into concrete operational plans — operator allocation, supervisor coverage, equipment requirements, and risk. Forecast throughput, flag potential misses early, and re-plan as priorities evolve
• Scale the footprint: Partner with facilities, recruiting, and leadership to expand the operation across additional shifts and, in time, additional sites. Maintain an operational playbook that makes opening each new shift or site repeatable rather than bespoke
• Be the bridge to research: Meet with the research team to understand upcoming priorities: which tasks, which embodiments, what success looks like. Translate those needs into shift plans, and surface friction (poor reset ergonomics, ambiguous instructions, unreliable hardware) before it costs the team hours

What You’ll Need to Succeed

• 4+ years of operations or program management experience, including direct people management
• A track record of running shift-based, throughput-driven operations — ****warehousing, manufacturing, contact centers, data labeling, clinical ops, field operations or comparable
• Demonstrated success reducing churn or improving retention in a frontline workforce: We want to hear the specifics: the baseline, the intervention, and the outcome
• Comfort with metrics: You should be able to look at our shift summary, immediately spot what's interesting, and know what to ask for next
• Builder mindset: This is a function being built, not inherited. Some processes are still taking shape, and we are looking for the right person who sees that as an opportunity to grow and deliver
• Strong written communication — ****you will be the connection between the floor and research and engineering teams, and much of that work happens in documentation
• Willingness to be on-site and on-floor: This role depends on presence and proximity to the team

Bonus Points For

• Experience managing managers or building a supervisor layer from scratch
• Background in robotics, autonomous systems, or physical AI data collection
• Experience scaling a frontline team from under 20 to 100+
• Familiarity with workforce management software, shift-scheduling tooling, or QA workflow tools
• Background in industrial engineering, operations research, or a related quantitative field

Your Impact at LILA

As a Robotics Scientist at Lila, you will lead the research and development of autonomous robotic systems that serve as the intelligent physical infrastructure of our scientific superintelligence platform. You’ll develop novel algorithms and deploy intelligent robotic solutions that interact seamlessly with human scientists and complex lab environments. Your work will accelerate our mission by enabling fully autonomous workflows for scientific discovery, combining cutting-edge robotics, machine learning, and systems engineering.

What You'll Be Building

• Design and develop autonomous robotic systems for transport and workcell operations, integrating advanced path planning, navigation, and motion planning algorithms
• Build production-grade mobile manipulation platforms leveraging ROS/ROS2 and modular robotic architectures
• Advance robotic perception by integrating sensing modalities such as 3D vision, LIDAR, and tactile sensors to enable robust, adaptable task execution
• Use simulation environments to model, test, and optimize task planning, scheduling, and robot behaviors in diverse lab scenarios
• Collaborate with AI, mechanical, and software engineering teams to translate theoretical robotics research into real-world autonomous systems

What You’ll Need to Succeed

• Ph.D. in Robotics, Computer Science, Mechanical/Electrical Engineering, or a related field, or equivalent research experience
• Expertise in motion planning, path planning, and navigation for manipulation and mobile robotics
• Proficiency with ROS/ROS2, C++, and Python, with hands-on experience building robotic systems in real-world environments
• Deep understanding of perception systems and sensor integration (e.g., camera, LIDAR, tactile sensors)
• Proven ability to take robotic systems from concept through to deployment

Bonus Points For

• Experience with dual-arm mobile manipulation systems or other high-DOF robotic platforms
• Familiarity with simulation tools such as Gazebo, Isaac Sim, or PyBullet
• Background in machine learning for perception, control, or adaptive planning
• Experience with real-time decision-making in human-robot collaboration scenarios
• Strong publication or patent record in robotics, autonomy, or perception

Your Impact at LILA

As a Robotics Scientist at Lila, you will lead the research and development of autonomous robotic systems that serve as the intelligent physical infrastructure of our scientific superintelligence platform. You’ll develop novel algorithms and deploy intelligent robotic solutions that interact seamlessly with human scientists and complex lab environments. Your work will accelerate our mission by enabling fully autonomous workflows for scientific discovery, combining cutting-edge robotics, machine learning, and systems engineering.

What You'll Be Building

• Pioneering approaches for precise and dexterous robotic manipulation that leverage foundation models, reinforcement learning, diffusion-based methods, and human guidance to enable adaptive and intelligent robotic systems capable of complex tasks across diverse scientific environments
• Developing novel human-robot interaction frameworks that incorporate imitation learning, and learning from human guidance, feedback, demonstrations and corrections, creating intelligent robotic agents that can seamlessly integrate with human scientific workflows and rapidly adapt to new experimental contexts
• Advancing dexterous manipulation research through cutting-edge machine learning approaches, including diffusion models and adaptive learning algorithms, that synthesize multi-modal sensing (tactile, visual, and language) to develop generative skill representation sand sophisticated motor learning policies for intelligent robotic systems
• Designing autonomous robotic systems with trust calibration mechanisms, enabling intelligent agents that can dynamically adjust their behaviors based on contextual information in complex scientific tasks

What You’ll Need to Succeed

• Ph.D. in Robotics, Machine Learning, Computer Science, or a related field with demonstrated expertise in foundation models for robotic learning
• Advanced proficiency in reinforcement learning, diffusion-based methods, imitation learning, and adaptive learning algorithms for robotic manipulation
• Expert-level experience with machine learning frameworks (PyTorch, TensorFlow) and deep learning architectures for developing foundation models, with specific expertise in diffusion-based generative models for robotics
• Proven track record of developing multi-modal perception systems integrating tactile, visual, language and other contextual sensing for intelligent robotic agents
• Strong publication record in robot learning, demonstrating innovative approaches to trust calibration, contextual learning, and generative robotic skill learning

Bonus Points For

• Research contributions to foundation models and diffusion methods in robotics
• Experience with large-scale machine learning model development, particularly generative and diffusion-based approaches
• Expertise in human-in-the-loop learning, correction-based training paradigms, and diffusion-guided skill transfer
• Demonstrated ability to translate theoretical machine learning research, especially diffusion and generative models, into practical robotic implementations