Senior Robotics Perception Engineer – SLAM & Fusion, Equity

Treeswift; Philadelphia (Philadelphia County); On-site; USD 160,000 - 210,000; Full time

Benefits offered by this job

Competitive salary

Equity

Medical coverage

Dental coverage

Vision coverage

Life insurance

Disability insurance

Parental leave 16 weeks

Unlimited paid time off

401(k)

OneMedical membership

Commuter benefits

Snacks and team lunches

Job summary

Treeswift is seeking a Senior Robotics Engineer to drive perception, mapping, sensor fusion and calibration, directly contributing to the expansion of our field robotics platform in demanding environments.

You will lead SLAM development with new sensor evolutions, accelerate delivery by refining data-to-model pipelines, improve GPS-degraded performance, and automate calibration for reliable, large-scale scenes.

Qualifications

• 5-10 years of experience in perception for robotics.
• Proficient in C++ and Python.
• Extensive experience developing SLAM solutions, ideally involving ground-based LiDAR.
• Experience with extrinsic and intrinsic sensor calibration, including automated data-driven calibration.
• Ultra-wide angle cameras experience is a nice-to-have.

Responsibilities

• Drive outcomes across perception, mapping, sensor fusion and calibration to grow the business.
• Lead SLAM development, incorporate new sensors, and improve GPS-degraded performance.
• Advance sensor fusion by fusing multi-user, multi-session maps and automate LiDAR-imagery alignment.
• Increase reliability by reducing need for extrinsic measurements and catching data issues at source.

Skills

C++

Python

SLAM development

LiDAR

Sensor calibration

Ultra-wide angle cameras

Perception for robotics

Job description

Treeswift is seeking a Senior Robotics Engineer to drive perception, mapping, sensor fusion and calibration, directly contributing to the expansion of our field robotics platform in demanding environments.

You will lead SLAM development with new sensor evolutions, accelerate delivery by refining data-to-model pipelines, improve GPS-degraded performance, and automate calibration for reliable, large-scale scenes.