Robotics Engineer/Researcher - Data Engine & Deployment - Teleoperation Systems, Data Pipelines[...]

Proception Inc.; Mountain View (CA); Hybrid; USD 140,000 - 210,000; Full time

Benefits offered by this job

Competitive salary and equity

Health, dental, and vision insurance

Access to dexterous robots

Collaborative environment with leading

YC backed investors

Leadership opportunities

Job summary

Proception Inc. in Mountain View, CA seeks a Robotics Engineer/Researcher to own the data engine from teleoperation demonstration through curation, to on-robot deployment. You will build high-throughput, reliable systems that convert robot time into training data and trained policies into real-world robots.

The role emphasizes end-to-end data pipelines, multimodal sensor integration, and deploying learned policies on real hardware in collaboration with robotics and AI teams.

Qualifications

• BS, MS, or PhD in Robotics, CS, Electrical Engineering, or related field, or equivalent experience.
• Strong Python and C++ in Linux: robust, maintainable systems code for real hardware.
• Hands-on robotics experience: hardware bring-up, sensor/actuator integration, debugging on physical systems.
• Experience building teleoperation and demonstration-collection systems and scaling throughput and operator efficiency.
• Experience building data pipelines for multimodal robot data: ingestion, time synchronization, storage, curation, filtering, annotation tooling.
• Experience deploying learned policies on real robots: real-time inference, latency optimization, safety monitors.
• Comfortable with multimodal sensor streams and synchronized capture (RGB/depth, proprioception, tactile, force-torque).
• Data-quality mindset: metrics, visualization, automated QA, regression testing from collection to deployment.
• Experience with ROS 2 or similar middleware, real-time systems, and containerized deployment across fleets.
• Familiarity with robot learning workflows (imitation learning, vision-language-action models).
• Experience with dexterous hands, tactile sensing, or contact-rich manipulation setups.

Responsibilities

• Design and build teleoperation and demonstration-collection stack for throughput and data quality.
• Run data collection end-to-end: task design, operator onboarding, collection on real robots.
• Build pipeline from robot to training set: multimodal data ingestion, synchronization, storage, curation, annotation tooling.
• Develop QA, metrics, and visualization tooling to keep datasets consistent and trustworthy.
• Deploy trained policies to real hardware: real-time inference, latency optimization, safety monitors.
• Integrate sensors and hardware across collection and deployment rigs.
• Collaborate with AI, hardware, and perception teams to close the loop from deployment results back into data collection.

Skills

Python

C++

Robotics experience

Teleoperation systems

Data pipelines

Policy deployment

ROS 2

Imitation learning

Dexterous hands

Sensor fusion

Containerization

Education

BS/MS/PhD in Robotics/CS/EE or related

Tools

ROS 2

Docker

Linux

Job description

Robotics Engineer/Researcher - Data Engine & Deployment - Teleoperation Systems, Data Pipelines, On-Robot Systems

Join our team to build the data engine behind general-purpose robot policies. You'll own the pipeline from teleoperated demonstration collection through curation and quality control to on-robot deployment — the systems that turn robot time into training data, and trained policies into robots that work in the real world. We care more about your ability to build reliable, high-throughput systems around real robots than about any particular robot, task, or sensor you've used before.

Requirements

• 01 BS, MS, or PhD in Robotics, Computer Science, Electrical Engineering, or related field — or equivalent experience
• 02 Strong software engineering skills in Python and C++ in Linux environments: you write robust, maintainable systems code that runs on real hardware
• 03 Hands-on robotics experience: hardware bring-up, sensor and actuator integration, calibration, and debugging full-stack issues on physical systems
• 04 Experience building teleoperation and demonstration-collection systems — rigs, operator interfaces, and workflows — and scaling collection throughput and operator efficiency
• 05 Experience building data pipelines for multimodal robot data: ingestion, time synchronization across sensors, storage and dataset formats, curation, filtering, and annotation tooling
• 06 Experience deploying learned policies on real robots: real-time inference, latency and throughput optimization, safety monitors, and graceful failure handling
• 07 Comfortable working with multimodal sensor streams (RGB/depth cameras, proprioception, tactile, force-torque) — drivers, logging, and synchronized capture
• 08 Rigorous about data quality: metrics and visualization for dataset coverage and consistency, automated QA, and regression testing of the collection-to-deployment loop
• 09 (+) Experience with ROS 2 or comparable robotics middleware, real-time systems, and containerized deployment across a fleet of robots
• 10 (+) Familiarity with robot learning workflows (imitation learning, vision-language-action models) — enough to shape data collection around what models actually need
• 11 (+) Experience with dexterous hands, tactile sensing, or contact-rich manipulation setups

Details & responsibilities

• 01 Design and build the teleoperation and demonstration-collection stack — rigs, operator interfaces, and workflows that maximize throughput and data quality
• 02 Run data collection operations end to end: task and protocol design, operator onboarding, and day-to-day collection on real robots
• 03 Build the pipeline from robot to training set: ingestion, time synchronization of multimodal sensor streams, storage and dataset formats, curation, filtering, and annotation tooling
• 04 Develop the QA, metrics, and visualization tooling that keeps datasets consistent, well-covered, and trustworthy
• 05 Deploy trained policies to real hardware: real-time inference, latency optimization, safety monitors, and graceful failure handling
• 06 Bring up and integrate sensors and hardware — cameras, tactile, force-torque — across collection and deployment rigs
• 07 Collaborate across AI, hardware, and perception teams to close the loop from deployment results back into data collectio
• 01 Competitive salary and meaningful equity
• 02 Full health, dental, and vision insurance
• 03 Access to custom-built dexterous robots
• 04 Collaboration with leading researchers in robotics and AI
• 05 Backed by YC and top-tier investors
• 06 High-ownership role with the opportunity to lead core initiatives in real-world robot learning