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Nidus Technologies

Staff Hardware Engineer, Robotics Systems & Deployments

New York CityOnsite · United States
Staff · FullTime · Engineering
POSTED 22 SEPTVERIFIED 9H AGO
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Role
48 days opentrue age
Posted 22 Sept
Pay
$200k–$270kno pay posted
Market p25–p75
$200k–$270k
New York · n=273 · 365d to 6 Nov
CompanyNidus Technologies →
3open roles
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Closed · 30d
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9,266open · Staff Software engineering
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All tracked boards · live 10 Nov · n=9,266
Distributed systems

About Us

Nidus is building autonomous manufacturing systems powered by AI and robotics. U.S. manufacturers are under growing pressure to increase output while navigating workforce constraints, underutilized equipment, and increasingly complex supply chains. We believe the next generation of manufacturing will be enabled by more intelligent, flexible automation—systems that help people and machines work together to expand capacity and improve productivity.

We're building the intelligence layer that makes that possible.

Our team comes from the intersection of defense technology and AI. We've built and shipped products to DoD customers, and we bring deep experience across ML, robotics, product development, and defense.

We're a small, in-person team in New York City working on hard problems that matter. If you want to build the future of American manufacturing, we'd love to hear from you.

About the Role

We are building vision-language-action models that enable robots to perform complex manipulation tasks in real-world environments. The quality, reliability, and scalability of the hardware used to collect data and deploy these systems are fundamental to our success.

We are looking for a Staff Hardware Engineer to define and lead the technical architecture for our robotic data-collection platforms, end effectors, camera systems, and customer workcells. You will own major hardware programs from early requirements and system architecture through validation, production, and fleet deployment.

This is a senior individual-contributor role with broad technical influence and a clear path to leadership. You will establish engineering standards, resolve complex cross-disciplinary tradeoffs, lead high-risk design efforts, and create reusable hardware platforms that can support new manipulation tasks, robot embodiments, and customer environments.

You will remain deeply hands-on—designing hardware, building prototypes, reviewing test data, and troubleshooting systems in the field—while also raising the technical bar across the organization through design reviews, mentorship, and long-range technical planning.

You will work closely with robotics, controls, machine learning, data operations, manufacturing, safety, and customer deployment teams.

What You’ll Do

Hardware Architecture and Technical Leadership

Define the technical architecture and long-term roadmap for robotic data-collection systems and customer-deployed workcells.

Translate company, product, model-training, and customer requirements into scalable hardware platforms and subsystem specifications.

Own system-level tradeoffs across mechanical design, sensing, controls, compute, safety, reliability, manufacturability, serviceability, cost, and deployment speed.

Identify the highest-risk technical problems, establish validation strategies, and drive programs from ambiguous requirements to reliable deployed systems.

Establish design principles, interfaces, qualification standards, documentation practices, and configuration-management processes across the hardware organization.

Lead architecture reviews, design reviews, failure investigations, and technical readiness assessments for critical hardware programs.

Serve as a technical authority for complex decisions spanning mechanical, electrical, optical, software, and operational domains.

Mentor engineers and help raise the quality of system design, technical judgment, documentation, and root-cause analysis across the team.

Data-Collection Systems

Architect robotic data-collection platforms that produce consistent, high-quality training data across operators, tasks, sites, and robot configurations.

Partner with ML and data teams to determine how embodiment design, sensor placement, calibration, latency, repeatability, and hardware variation affect data quality and policy performance.

Define system requirements for payload, workspace, stiffness, compliance, precision, visibility, ergonomics, cycle life, safety, and operator throughput.

Develop modular architectures that allow new sensors, end effectors, robots, and task fixtures to be integrated without redesigning the entire platform.

Source · Nidus TechnologiesFirst seen 2 NovOriginal ↗