--- title: 'Research Engineer at Mind Robotics' canonical: 'https://feeny.ai/job/research-engineer-mind-robotics-palo-alto-mf4c6a7jwttb' type: 'job' last_seen: '2026-10-16' --- # Research Engineer at Mind Robotics - **Company:** Mind Robotics - **Location:** Palo Alto, CA - **Employment:** full-time - **Work type:** onsite - **Posted:** 2026-01-26 - **Last confirmed live:** 2026-10-16 - **Apply:** https://jobs.ashbyhq.com/mindrobotics/bdba9018-fc6a-4bd9-92b5-73f56771c46b/application **Skills:** PyTorch, JAX, Distributed Training, GPU Optimization, Sharding, Parallelism > Build distributed training systems and core ML infrastructure to support large-scale model training. The role involves owning distributed training, optimizing performance across hundreds of GPUs, and collaborating with researchers to improve reliability and ease of use. ## Job description About Mind: Mind Robotics is building Physical AI for real-world industrial deployment, starting with the factory floor. We believe the hardest problems in AI are solved when researchers and engineers are hands-on with the physical world every day - and we're looking for people who are passionate about robotics, value ownership, and are excited to tackle difficult problems. Join us if you want to move beyond digital intelligence and put intelligence into motion. About the team and the role: At Mind Robotics, we’re building generalized physical AI—robotic systems capable of dexterous, adaptive, and reasoning-intensive work in real-world industrial environments. Our ability to iterate quickly on large-scale models depends on world-class ML infrastructure. We’re looking for a Research Engineer to build the core systems that enable fast, reliable, and scalable model training—powering everything from experimentation to production deployment. Responsibilities: - Design and implement scalable systems for training large ML models. - Enable efficient workflows for data ingestion, training, and iteration. - Develop and optimize distributed training systems across hundreds of GPUs. - Implement strategies for parallelization, sharding, and efficient compute utilization. - Improve training efficiency through techniques such as attention optimizations, kernel fusion, and memory management. - Partner closely with modeling teams to accelerate iteration speed and reduce training costs. - Build internal tools for experiment tracking, monitoring, and debugging. - Implement systems for tracking training performance, failures, and resource utilization. - Debug and resolve bottlenecks across the training stack. - Provide lightweight infrastructure support for deploying and running models in production environments. - Optimize inference performance and reliability where needed. - Support core cloud infrastructure needs for training workloads (without heavy DevOps overhead). - Manage compute resources efficiently across training jobs. Requirements: - Strong experience building infrastructure for large-scale ML training. - Deep understanding of how modern LLM/VLM systems are trained and scaled. - Proven experience setting up and scaling distributed training across hundreds of GPUs. - Strong understanding of parallelization strategies (data, model, pipeline parallelism). - Strong proficiency in Python programming. - Expert-level proficiency in PyTorch and/or JAX. - Strong understanding of techniques like attention optimization, kernel fusion, and efficient memory usage. Nice to Have: - Experience supporting inference systems in production. - Familiarity with robotics or embodied AI workloads. - Experience building tools for experiment management and researcher productivity. ## About Mind Robotics ## Company Overview - **One-liner**: Mind Robotics builds AI-driven robotic systems for industrial deployment, focusing on creating general-purpose physical intelligence for the world's most demanding manufacturing challenges. - **Entity Type**: Private (Series A) - **Headquarters**: Palo Alto, California, United States - **Founded**: 2025 - **Founders**: Not publicly disclosed (founding technical team includes Sriramana Sankar, Ruijie He, Vishal J., and Ury Zhilinsky as Founding Technical Members) ## Core Business - **Primary industry/industries**: Robotics Engineering, Industrial Automation, Artificial Intelligence - **Target customers**: B2B, Enterprise (industrial manufacturing, factory floors) - **Mission or purpose statement**: "Building the generalized physical intelligence required to solve the world’s most demanding industrial challenges." The company believes the path to general-purpose robotics begins where the need is most acute and the environment is most structured: the factory floor. ## Products & Services - **AI-Enabled Industrial Robots**: Intelligent robotic systems designed for industrial deployment, focusing on creating collaborative platforms for manufacturing environments. The company builds "physical AI" for real-world industrial challenges, emphasizing hardware-in-the-loop development where researchers and engineers work hands-on with hardware. ## Market Standing - **Valuation/Market Cap**: Not disclosed - **Key Metric**: Total Funding of **$615M** across two rounds - **Notable Investors/Partners**: Andreessen Horowitz (co-lead Series A), Accel (co-lead Series A), Kleiner Perkins (led most recent round), Eclipse (seed), Prysm Capital, Menlo Ventures. Partners include Silicon Valley Robotics. - **Growth Signals**: - Raised a $115M seed round in late 2025, followed by a massive $500M Series A in March 2026 co-led by Accel and Andreessen Horowitz - Closed an additional funding round in May 2026 led by Kleiner Perkins - Headcount of ~21 employees with a monthly growth rate of +42.9% - LinkedIn following of 9,654 with +38.5% monthly growth - Talent sourced from top companies including Tesla, Rivian, Databricks, and Physical Intelligence ## Competitive Advantages - **Massive capital advantage**: With $615M in total funding raised within its first year, Mind Robotics is one of the best-capitalized early-stage robotics startups, enabling aggressive hiring and R&D investment. - **Top-tier investor backing**: Backed by Andreessen Horowitz, Accel, and Kleiner Perkins — some of the most prestigious venture firms in the world. - **Hardware-in-the-loop philosophy**: The company emphasizes that the hardest problems in AI are solved when researchers and engineers are hands-on with hardware, creating a unique engineering culture focused on real-world deployment. - **Lean, high-impact team**: With only ~21 employees and massive funding, each individual has outsized ownership and impact. ## Strategic Focus - **Industrial deployment first**: Targeting factory floors as the most structured environment for deploying general-purpose robotics, believing this is the fastest path to broadly capable robots. - **Physical AI development**: Building the intelligence layer for robots that can operate in messy, complex real-world environments. - **Aggressive scaling**: With $615M in funding, the company is rapidly expanding its team across hardware engineering, software, and data platform roles. ## Why Work Here - **Massive impact per person**: The company explicitly states it is "building a lean, collaborative team where every individual has a massive impact" — a rare promise for a company with over half a billion in funding. - **Hardware-in-the-loop culture**: Engineers and researchers work directly with physical hardware daily, solving real-world problems rather than just simulation. - **Top-tier team**: Colleagues include alumni from Tesla, Rivian, Databricks, and Physical Intelligence, creating a high-caliber peer group. - **Work policy**: Hybrid workspace (combination of remote and on-site work) with a strong in-office presence in Palo Alto, CA. Most roles appear to be on-site or hybrid. - **Open roles**: 22 active positions across hardware engineering (actuation, motor design, electrical design, tactile sensing), software engineering (robotics, firmware, controls, ML infrastructure), and data platform roles. - **Culture highlights**: The company looks for "builders who are passionate about robotics and live for the challenge of making systems work in messy, complex environments." They emphasize a lean, collaborative team where every individual has massive impact. ## Sources 1. [mindrobotics.com](https://www.mindrobotics.com/) 2. [mindrobotics.com/careers](https://www.mindrobotics.com/careers) 3. [LinkedIn - Mind Robotics](https://www.linkedin.com/company/mindrobotics) 4. [Built In - Mind Robotics](https://builtin.com/company/mind-robotics) 5. [Ashby Jobs - Mind Robotics](https://jobs.ashbyhq.com/mindrobotics) ## Other roles at Mind Robotics - [Field Technician, Data Collection](https://feeny.ai/job/field-technician-data-collection-mind-robotics-normal-15vnxcrxc24p) — Normal, IL - [Communications Lead](https://feeny.ai/job/communications-lead-mind-robotics-palo-alto-t7d5nwyggs2f) — Palo Alto, CA - [Software Engineer, Edge & Field Systems](https://feeny.ai/job/software-engineer-edge-field-systems-mind-robotics-normal-t75nwdgtchv5) — Normal, IL - [Mechanical Design Engineer, Plastics](https://feeny.ai/job/mechanical-design-engineer-plastics-mind-robotics-palo-alto-1ythfx9nb8x4) — Palo Alto, CA - [Software Engineer, Data Platform](https://feeny.ai/job/software-engineer-data-platform-mind-robotics-palo-alto-h9nnhq30rhnc) — Palo Alto, CA - [Technical Program Manager, Software](https://feeny.ai/job/technical-program-manager-software-mind-robotics-palo-alto-nzbtjpp2a6gy) — Palo Alto, CA - [Robot Operator, Data Collection](https://feeny.ai/job/robot-operator-data-collection-mind-robotics-palo-alto-8br5b7ksz571) — Palo Alto, CA - [Mechanical Design Engineer, Wearables](https://feeny.ai/job/mechanical-design-engineer-wearables-mind-robotics-palo-alto-kveqcemqyqm2) — Palo Alto, CA - [Tech Lead, Robotics Runtime & Middleware](https://feeny.ai/job/tech-lead-robotics-runtime-middleware-mind-robotics-palo-alto-0nmtyyhy7tad) — Palo Alto, CA - [IT Operations Lead](https://feeny.ai/job/it-operations-lead-mind-robotics-palo-alto-nnxhw00ffje8) — Palo Alto, CA