Perception Robotics Engineer - MA - Start Up

$125,000–$200,000 + Equity

I'm partnering with an exciting robotics company that's building next-generation intelligent robotic systems, and they're looking for a Robotics Perception Engineer to own one of the most critical parts of their platform.

Before I spoke with the hiring manager, they made one thing very clear:

This is not a traditional Perception Engineer role.

If your background is primarily in computer vision research or training perception models, this probably isn't the right fit.

Instead, they're looking for an engineer who enjoys working closer to the hardware than the models, someone who loves building the software that makes an entire robotic sensing stack reliable, performant and production-ready.

If you've spent your career integrating cameras, LiDARs and sensors into real robotic systems, debugging problems on physical hardware and building software that simply cannot fail, this is exactly the type of role they're hoping to find.

You'll take ownership of the software that sits between the robot's sensors and the higher-level robotics applications.

Working across the full sensing pipeline, you'll ensure that data from multiple cameras and LiDARs is synchronised, processed accurately, delivered with low latency and stored for future training and analysis.

This is a genuine systems engineering role where you'll work directly with robots every day rather than spending your time in simulation.

You'll be trusted with ownership from the outset and will quickly become responsible for one of the company's core robotics systems.

This team is building robots that rely on large amounts of real-time sensor data to understand and interact with the world around them.

Some of the challenges include:

• Running multiple high-frame-rate RGB-D cameras alongside 3D LiDARs without dropped frames.
• Building reliable, low-latency perception pipelines capable of processing large amounts of sensor data in real time.
• Generating accurate point clouds for downstream robotics systems.
• Building tooling for calibration, diagnostics and monitoring.
• Debugging software directly on physical robotic hardware.
• Designing systems that remain reliable even when hardware inevitably misbehaves.
• Supporting both real-time robotics applications and large-scale dataset generation.

This is the type of engineering where performance, reliability and attention to detail matter just as much as writing clean code.

You'll have ownership across much of the sensing infrastructure, including:

• Integrating RGB, RGB-D and LiDAR sensors into production robotic systems.
• Developing low-level software in Python and/or C++.
• Building and improving perception pipelines.
• Working on sensor synchronisation and calibration.
• Improving runtime performance and reliability.
• Building internal tooling for diagnostics and monitoring.
• Debugging issues across hardware, firmware, operating systems and application software.
• Collaborating closely with robotics, controls and AI engineers to ensure downstream systems receive accurate, reliable sensor data.

The hiring manager repeatedly described wanting a generalist rather than a specialist.

They're looking for someone who's comfortable moving between software, sensors and hardware without worrying too much about where one discipline ends and another begins.

This role will probably suit you if you've found yourself asking questions like:

• Why is this camera dropping frames?
• Why have these sensors drifted out of sync?
• Why is this point cloud unstable?
• Where is this latency coming from?
• How do we debug this on the robot itself?
• How do we make this pipeline more reliable?

They're far more interested in engineers who've owned complete systems than candidates who've spent years optimising one small part of a much larger platform.

The company isn't hiring based on company names, they're hiring based on the problems you've solved.

Engineers from environments such as the following often have highly relevant experience:

• Robotics startups.
• Autonomous vehicles.
• Warehouse automation.
• Defence robotics.
• Drone or aerial robotics.

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