Engineer, Sensors

Who we are

Neros is a defense technology company rebuilding America’s drone industrial base. We design and manufacture high-performance unmanned systems that are tested in combat, iterated at startup speed, and built at massive scale. Our team culture is fast, hands-on, and obsessed with closing the gap between design and deployment.

As drones transform the character of warfare, Neros is delivering the systems the West needs to compete on the modern battlefield and deter the adversaries of democracy. We’re hiring engineers, operators, and builders who want to move fast, take on extreme ownership, and get capability into the hands of warfighters in months, not years.

What you will be doing

Be our in-house design expert for making the best camera modules possible within constraints. We should move the FPV industry forward by bringing knowledge and expertise from other industries making high quality, cost effective cameras.

Responsibilities

• Electro-Optical Selection & System Design: Own the technical selection of image sensors (imagers), ISPs, video transmission and supporting drive electronics based on application-specific requirements (e.g., latency, low-light performance, frame rates, and dynamic range)

• Optical & Lens Expertise: Serve as the in-house subject matter expert on lens technology; define critical optical parameters (FOV, aperture, MTF, distortion profiles), manage custom or off-the-shelf lens sourcing, and oversee precise lens-to-sensor alignment

• Supplier Quality & Design & Assembly Criteria: Establish and enforce rigorous design-in criteria for tier-1 supplier components, detailing exactly what is acceptable regarding sensor calibration, gluing, tilt, and environmental sealing. Know the end-to-end camera assembly process. Be able to challenge and guide the supply base to create manufacturing standards and testing protocols (e.g., active alignment, blemish testing) to ensure high yield and consistent image quality from the factory floor to the end user

• Camera Sample Testing & Characterization: Plan and run bench testing of camera samples and competitor units. Build repeatable test setups and acceptance criteria

• Performance Tooling & Code: Write and maintain software tools (e.g., Python, OpenCV) to acquire, process, and analyze test data and compute camera performance metrics, turning lab measurements into objective component-selection and supplier-acceptance decisions

You should have the following

• CAD familiarity (SolidWorks preferred)

• Experience releasing parts for mass production

• First principles understanding of optics, image sensors, and outdoor environment impact on product design

• Proficiency writing code (Python or similar) for test automation, data analysis, and performance-metric calculation

• Hands-on lab experience characterizing cameras/imagers. Using or building test rigs and interpreting MTF, SNR, dynamic range, latency, etc.

• Working knowledge of the imaging signal flow from imager through ISP to digital and analog (CVBS) video output

• Creative mindset to solve problems and come up with elegant solutions

Nice to have

• Some working level knowledge of embedded software in imager chips, ISP, CV tools and functionality

• Demonstrated experience of cradle to grave camera product for related product

• FPV drone passion and personal hobby

US Salary Range $111,000 - $155,500 USD

The salary range for this role is an estimate based on a wide range of compensation factors, inclusive of base salary only. Actual salary may vary based on (but not limited to) work experience, education and/or training, critical skills, and/or business considerations. Highly competitive equity grants are considered part of Neros' total compensation package.

We’re an equal opportunity employer. We welcome all applicants without attention to race, color, religion, sex, sexual orientation, gender identity, national origin, veteran or disability status.

Verified from the employer's public Greenhouse job collection. The provider-supplied title, description, location and application URL were normalized without inferring unstated facts.