Perception Engineer

Acceler8 Talent; San Francisco (CA); On-site; USD 150,000 - 210,000; Full time

Job summary

Acceler8 Talent is recruiting perception engineers for a stealth robotics company in the San Francisco Bay Area. You will work onsite across Menlo Park and San Jose, shaping core perception and sensor processing for autonomous manufacturing systems.

You will tackle challenging edge cases in sensors, lighting, and occlusions, collaborating with hardware, software, and ML teams to integrate perception, learning, and robotics across the stack.

Qualifications

• Experience in perception for autonomous systems.
• Ability to handle edge cases in sensors and lighting.
• Understanding interaction between learning, sensors and hardware.

Responsibilities

• Develop perception stacks for autonomous systems.
• Collaborate across hardware, software, and ML teams.
• Address sensor/environment edge cases in testing.

Skills

Perception engineering

Sensor fusion

Robotics

Machine learning

Tools

ROS

Python

C++

GPU programming

Job description

Hiring for a stealth robotics company building highly autonomous 3D factories designed to turn raw materials into finished goods with minimal human labour.

The company is building what it describes as modern "matter compilers" - deeply integrated robotic factories combining hardware, software, perception, robot learning, simulation and low-level compute into a single manufacturing system.

Rather than integrating standard robotics tooling, the team is building major parts of the stack from first principles, including perception, robot learning, simulation, rendering and GPU performance.

It is still a small, deeply technical team of around 11 people, with engineers given significant ownership over core systems and expected to work across disciplines rather than inside a narrow silo.

Hiring across Early, Mid & Late Career for perception engineers with experience in:

• Perception for self-driving or other autonomous systems
• Difficult sensor, lighting, occlusion & environmental edge cases
• Understanding how learning, sensors & hardware interact

Primarily onsite in the Bay Area, with R&D moving from Menlo Park to San Jose.