Deployment Engineer
Menlo
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Job summary
Menlo is seeking a Deployment Engineer to own end-to-end deployment problems, work with real robotic systems in diverse environments, and turn field learnings into reusable research and product improvements. You will collaborate with core research and platform teams to scale deployable humanoid systems and contribute to the forward deployment practice.
You will join a hands-on, deployment-focused team that values rapid iteration, rigorous data collection, and clear communication across technical
Qualifications
- Deep expertise in at least one relevant technical domain: perception, navigation, manipulation, or teleoperation.
- Experience deploying AI, robotics, or embodied systems in real environments.
- Comfortable working outside your specialization when the deployment demands it.
- Experience collecting, cleaning, or curating deployment data for model improvement.
- Experience designing evaluations or benchmarks for systems that interact with real-world environments.
- Able to explain complex technical concepts clearly to both technical and non-technical audiences.
- Energized by ambiguity, motivated by ownership, and wants to see work survive contact with the real world.
Responsibilities
- Own deployment problems end to end, from understanding workflow to diagnosing field failures.
- Work with robotic systems in commercial and industrial environments with variability and constraints.
- Translate deployment friction into research questions and product improvements.
- Build and adapt systems across the deployment loop: data collection, fine-tuning, evaluation design.
- Collaborate with core research and platform teams to turn field learnings into reusable capabilities.
- Contribute domain expertise to a broader forward deployment practice across deployments.
- Help define the operating model for deploying humanoid systems.
Skills
Job description
Menlo Research is an Applied R&D lab building Asimov, an open-source humanoid robot platform, and the full software stack that powers it. Our mission is to make humanoid labor economically viable, turning software into physical labor at scale. We build across the full stack: hardware architecture, locomotion, autonomy, simulation, and infrastructure. We move fast, ship to real robots, and open-source everything we can. If you want your work to matter beyond a paper or a demo, this is the place.
The hard problem in robotics is not building a compelling prototype. It is making robotic systems deployable, repeatable, and economically useful in the real world. You will work directly on real customer problems, but you will not stop at integration or customization. You will use deployment pressure to uncover missing capabilities, design evaluations, collect data, adapt models, and turn one-off field learnings into reusable product and research improvements.
This is not a support role. It is not solutions engineering. It is a deployment-native R&D role for people who want to stay close to reality and build the abstractions that make the next deployment easier.
Own real customer and deployment problems end to end, from understanding the workflow to diagnosing failures in the field
Work directly with robotic systems in commercial and industrial environments where variability, ambiguity, and operational constraints are the norm
Translate deployment friction into research and product questions: what capability is missing, what data is needed, what evaluation should exist, what part of the stack must improve
Build and adapt systems across the deployment loop, including data collection, task-specific fine-tuning, and evaluation design
Collaborate closely with core research and platform teams so field learnings become reusable capabilities instead of one-off fixes
Contribute domain expertise to a broader forward deployment practice where knowledge is shared across deployments and specializations
Help define the operating model for how humanoid systems are deployed, improved, and scaled
Deep expertise in at least one relevant technical domain: perception, navigation, manipulation, or teleoperation
Experience deploying AI, robotics, or embodied systems in real environments rather than only in lab settings
Comfortable working outside your specialization when the deployment demands it
Experience collecting, cleaning, or curating deployment data for model improvement
Experience designing evaluations or benchmarks for systems that interact with real-world environments
Able to explain complex technical concepts clearly to both technical and non-technical audiences
Energized by ambiguity, motivated by ownership, and wants to see work survive contact with the real world
Comfort moving between software, research, operations, and user conversations as needed
Evidence of building trust with users while maintaining a strong technical point of view
Prior experience in a forward deployed or customer-facing technical role at a fast-moving company
You will sit at the point where all of Menlo’s core systems come together: the agent platform, simulation infrastructure, motor control and policy training, the Asimov reference platform, and the data engine. If you join as a Deployment Engineer, you will not just fill a role. You will help define a category. If you want to do serious technical work, stay close to real deployments, and invent the operating model for deployable robotic systems, we would love to talk.
Menlo Research is an Applied R&D lab building Asimov, an open‑source humanoid robot platform, and the full software stack that powers it. Our mission is to make humanoid labor economically viable -- turning software into physical labor at scale. We build across the full stack: hardware architecture, locomotion, autonomy, simulation, and infrastructure. We move fast, ship to real robots, and open-source everything we can. If you want your work to matter beyond a paper or a demo, this is the place.
Menlo Research is an Applied R&D lab building Asimov, an open‑source humanoid robot platform, and the full software stack that powers it. Our mission is to make humanoid labor economically viable -- turning software into physical labor at scale. We build across the full stack: hardware architecture, locomotion, autonomy, simulation, and infrastructure. We move fast, ship to real robots, and open-source everything we can. If you want your work to matter beyond a paper or a demo, this is the place.
You don’t need deep AI expertise for every role, but we do expect everyone at Menlo to be intellectually curious, drawn to tinkering and discovery, and excited to use AI as a real collaborator in their work. For some roles, AI fluency is a core requirement. When that’s the case, we’ll say so explicitly in the qualifications. People who thrive here don’t treat AI as a novelty. They use it to think better, and make their work easier for others to build on.
We hire talented people from a wide range of backgrounds. If you’re excited about a role but don’t meet every bullet, we still encourage you to apply. Menlo Research is an equal opportunity employer and does not discriminate on the basis of any legally protected characteristic. Menlo provides reasonable accommodations during the application process. If you need one, please let your recruiter know.
Menlo Research is an Applied R&D lab building Asimov, an open-source humanoid robot platform, and the full software stack that powers it. Our mission is to make humanoid labor economically viable, turning software into physical labor at scale. We build across the full stack: hardware architecture, locomotion, autonomy, simulation, and infrastructure. We move fast, ship to real robots, and open-source everything we can. If you want your work to matter beyond a paper or a demo, this is the place.
The hard problem in robotics is not building a compelling prototype. It is making robotic systems deployable, repeatable, and economically useful in the real world. You will work directly on real customer problems, but you will not stop at integration or customization. You will use deployment pressure to uncover missing capabilities, design evaluations, collect data, adapt models, and turn one-off field learnings into reusable product and research improvements.
This is not a support role. It is not solutions engineering. It is a deployment-native R&D role for people who want to stay close to reality and build the abstractions that make the next deployment easier.
Own real customer and deployment problems end to end, from understanding the workflow to diagnosing failures in the field
Work directly with robotic systems in commercial and industrial environments where variability, ambiguity, and operational constraints are the norm
Translate deployment friction into research and product questions: what capability is missing, what data is needed, what evaluation should exist, what part of the stack must improve
Build and adapt systems across the deployment loop, including data collection, task-specific fine-tuning, and evaluation design
Collaborate closely with core research and platform teams so field learnings become reusable capabilities instead of one-off fixes
Contribute domain expertise to a broader forward deployment practice where knowledge is shared across deployments and specializations
Help define the operating model for how humanoid systems are deployed, improved, and scaled
Deep expertise in at least one relevant technical domain: perception, navigation, manipulation, or teleoperation
Experience deploying AI, robotics, or embodied systems in real environments rather than only in lab settings
Comfortable working outside your specialization when the deployment demands it
Experience collecting, cleaning, or curating deployment data for model improvement
Experience designing evaluations or benchmarks for systems that interact with real-world environments
Able to explain complex technical concepts clearly to both technical and non-technical audiences
Energized by ambiguity, motivated by ownership, and wants to see work survive contact with the real world
Comfort moving between software, research, operations, and user conversations as needed
Evidence of building trust with users while maintaining a strong technical point of view
Prior experience in a forward deployed or customer-facing technical role at a fast-moving company
You will sit at the point where all of Menlo’s core systems come together: the agent platform, simulation infrastructure, motor control and policy training, the Asimov reference platform, and the data engine. If you join as a Deployment Engineer, you will not just fill a role. You will help define a category. If you want to do serious technical work, stay close to real deployments, and invent the operating model for deployable robotic systems, we would love to talk.
Menlo Research is an Applied R&D lab building Asimov, an open‑source humanoid robot platform, and the full software stack that powers it. Our mission is to make humanoid labor economically viable -- turning software into physical labor at scale. We build across the full stack: hardware architecture, locomotion, autonomy, simulation, and infrastructure. We move fast, ship to real robots, and open-source everything we can. If you want your work to matter beyond a paper or a demo, this is the place.
Menlo Research is an Applied R&D lab building Asimov, an open‑source humanoid robot platform, and the full software stack that powers it. Our mission is to make humanoid labor economically viable -- turning software into physical labor at scale. We build across the full stack: hardware architecture, locomotion, autonomy, simulation, and infrastructure. We move fast, ship to real robots, and open-source everything we can. If you want your work to matter beyond a paper or a demo, this is the place.
You don’t need deep AI expertise for every role, but we do expect everyone at Menlo to be intellectually curious, drawn to tinkering and discovery, and excited to use AI as a real collaborator in their work. For some roles, AI fluency is a core requirement. When that’s the case, we’ll say so explicitly in the qualifications. People who thrive here don’t treat AI as a novelty. They use it to think better, and make their work easier for others to build on.
We hire talented people from a wide range of backgrounds. If you’re excited about a role but don’t meet every bullet, we still encourage you to apply. Menlo Research is an equal opportunity employer and does not discriminate on the basis of any legally protected characteristic. Menlo provides reasonable accommodations during the application process. If you need one, please let your recruiter know.
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