7jXuFpCj Q9Mp9E KsZKGB7P – 3D / Robotics — Philadelphia, PA

𝗬𝗼𝘂𝗿 𝘀𝗶𝗺𝘂𝗹𝘁𝗮𝗻𝗲𝗼𝘂𝘀 𝗹𝗼𝗰𝗮𝗹𝗶𝘇𝗮𝘁𝗶𝗼𝗻 𝗮𝗻𝗱 𝗺𝗮𝗽𝗽𝗶𝗻𝗴 (𝗦𝗟𝗔𝗠) 𝘀𝘁𝗮𝗰𝗸 𝘄𝗶𝗹𝗹 𝗻𝗼𝘁 𝗯𝗲 𝗲𝘃𝗮𝗹𝘂𝗮𝘁𝗲𝗱 𝗼𝗻 𝗮 𝗯𝗲𝗻𝗰𝗵𝗺𝗮𝗿𝗸 𝗱𝗮𝘁𝗮𝘀𝗲𝘁. 𝗜𝘁 𝘄𝗶𝗹𝗹 𝗯𝗲 𝗲𝘃𝗮𝗹𝘂𝗮𝘁𝗲𝗱 𝗼𝗻 𝘄𝗵𝗲𝘁𝗵𝗲𝗿 𝗮 𝗿𝗼𝗯𝗼𝘁 𝗰𝗹𝗶𝗺𝗯𝘀 𝗮 𝘀𝗲𝘁 𝗼𝗳 𝗴𝗿𝗮𝘁𝗲𝗱 𝘀𝘁𝗮𝗶𝗿𝘀 𝗮𝘁 𝗮 𝗰𝘂𝘀𝘁𝗼𝗺𝗲𝗿 𝘀𝗶𝘁𝗲 𝘄𝗶𝘁𝗵𝗼𝘂𝘁 𝗴𝗲𝘁𝘁𝗶𝗻𝗴 𝗹𝗼𝘀𝘁.

One role, three levels. You do not pick yours before applying; the team places you at the level your skills and scope earn during the process, and the offer matches it.

𝗧𝗛𝗘 𝗖𝗢𝗠𝗣𝗔𝗡𝗬

fTDT qNkUS4 is hiring on behalf of our client, a venture-backed robotics company under 200 people building autonomous ground robots for industrial inspection, infrastructure, energy, and public safety customers. The robots run on customer sites, on unstructured terrain, in real weather, today. Perception is the constraint on what the platform does next, which is why this hire matters more than the headcount suggests.

𝗪𝗵𝗼 𝘁𝗵𝗿𝗶𝘃𝗲𝘀 𝗵𝗲𝗿𝗲

- Researchers who became engineers. You care about the algorithm and you care just as much about whether it runs at frame rate on the robot's compute

- People who ship. The team's hardest hiring problem is candidates who can explain a method beautifully and have never taken one to production

- Engineers who take direct feedback in a code review, change the approach, and move on

- Anyone who wants their work measured against physical terrain instead of a leaderboard

This is not the role if you want to spend two years deepening one research thread, or if your only deployment target has ever been an offline dataset.

𝗖𝗔𝗥𝗘𝗘𝗥 𝗔𝗖𝗖𝗘𝗟𝗘𝗥𝗔𝗧𝗜𝗢𝗡

You own a piece of the perception stack outright, not a ticket queue inside someone else's. Your algorithm goes on a robot within weeks and field data comes back. Lead is open now if you want direct reports. Senior and Staff stay fully individual contributor if you do not.

𝗧𝗛𝗘 𝗢𝗣𝗣𝗢𝗥𝗧𝗨𝗡𝗜𝗧𝗬

The platform is being pushed into harder environments and perception unlocks them. The team is expanding what the robot handles across perception and manipulation: graded stairs, grating, and bridge structures, the geometry that breaks most localization stacks. You take perception from research to reliable production software alongside the robotics, controls, and systems engineers.

𝗪𝗛𝗔𝗧 𝗬𝗢𝗨'𝗟𝗟 𝗕𝗨𝗜𝗟𝗗

- Design and implement SLAM and localization systems, visual, visual-inertial, lidar, or multi-sensor

- Build computer vision pipelines for feature extraction, tracking, mapping, and scene understanding

- Implement and optimize estimation algorithms, both filtering and optimization-based

- Fuse data from cameras, inertial measurement units (IMUs), lidars, and depth sensors

- Evaluate perception performance against real-world field data and hard metrics

- Optimize for real-time performance and robustness on the robot's onboard compute

- Work with the controls and planning teams to support downstream autonomy

- Build out the tooling, datasets, and evaluation frameworks the team runs on

𝗧𝗛𝗘 𝗥𝗘𝗔𝗟 𝗧𝗔𝗟𝗞

This team has interviewed roughly 100 people across their open software roles and has not hired. The failures cluster: live C++ under observation, explaining a technical decision clearly, absorbing feedback in the room, and turning a concept into a finished product rather than an interesting prototype. The other consistent gap is candidates who cannot pivot off a long-horizon lab project into product cadence. Research background is welcome here; the hiring managers actively want lab experience. What they need with it is someone who can close.

𝗤𝗨𝗔𝗟𝗜𝗙𝗜𝗖𝗔𝗧𝗜𝗢𝗡𝗦

𝗥𝗲𝗾𝘂𝗶𝗿𝗲𝗱:

- A master's in robotics, computer science, electrical engineering, or a related field plus 2+ years of relevant experience, or a PhD. Equivalent industry experience counts; the team weighs what you built and shipped over where your degree is from

- Hands-on experience implementing SLAM or localization on a real system, not just in simulation

- Solid command of 3D geometry and coordinate transformations, camera models and calibration, feature-based or direct visual methods, and probabilistic state estimation

- Proficiency in C++ and/or Python. Expect a live C++ exercise in the process

- Linux environments and a robotics software stack such as Robot Operating System (ROS / ROS 2)

- At least 2 years in a complex physical product domain, robotics, automation, or aerospace, or equivalent research lab work on real hardware

- Permanent U.S. residency, and the ability to clear standard background checks before and during employment

- On-site in Philadelphia five days a week. There is no remote option for this role

𝗕𝗼𝗻𝘂𝘀 𝗣𝗼𝗶𝗻𝘁𝘀:

- SLAM frameworks: ORB-SLAM, VINS, Cartographer, or GTSAM

- Lidar-based perception and mapping

- Deep learning perception models

- Perception deployed on real robots in the field

- Graphics processing unit (GPU) acceleration with CUDA or OpenCL

- Dataset curation and annotation

- Publications in robotics or computer vision, paired with industry delivery experience

𝗧𝗛𝗘 𝗜𝗡𝗧𝗘𝗥𝗩𝗜𝗘𝗪 𝗣𝗥𝗢𝗖𝗘𝗦𝗦

Five steps, and you will know where you stand at each one.

- A 30-minute technical screen with us at fTDT qNkUS4 before anything goes to the client

- An initial phone screen with their people team

- A 45-minute technical conversation

- A live CoderPad coding interview with team members

- An on-site final focused on collaboration and fit

𝗖𝗢𝗠𝗣𝗘𝗡𝗦𝗔𝗧𝗜𝗢𝗡 𝗔𝗡𝗗 𝗕𝗘𝗡𝗘𝗙𝗜𝗧𝗦

𝗕𝗮𝘀𝗲 𝗦𝗮𝗹𝗮𝗿𝘆: $140,000 to $185,000, placed by level as described above

𝗔𝗻𝗻𝘂𝗮𝗹 𝗕𝗼𝗻𝘂𝘀: variable compensation that has averaged at least 15% of base, prorated in year one. This is the actual historical average, not a stretch target

𝗘𝗾𝘂𝗶𝘁𝘆: every engineer is on the equity plan

𝗥𝗲𝗹𝗼𝗰𝗮𝘁𝗶𝗼𝗻: assessed individually, scaled to your distance from Philadelphia and family size

𝗧𝗶𝗺𝗲 𝗢𝗳𝗳: flexible time off policy

𝗕𝗲𝗻𝗲𝗳𝗶𝘁𝘀: full medical package. Ask us for the current plan details and we will send them

𝗟𝗼𝗰𝗮𝘁𝗶𝗼𝗻: Philadelphia, PA, on-site, full-time

𝗧𝗿𝗮𝘃𝗲𝗹: none required, though domestic and international field opportunities exist if you want them

𝗔𝗣𝗣𝗟𝗬

Apply via LinkedIn and we'll reach out to schedule a conversation. Confidential search; your application is fully private.

𝗣𝗮𝘃𝗲 𝗧𝗮𝗹𝗲𝗻𝘁 | 𝗛𝗶𝗿𝗶𝗻𝗴 𝗥𝗲𝗶𝗺𝗮𝗴𝗶𝗻𝗲𝗱