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Senior Machine Learning Engineer (Tech Lead), Robot Learning, Loco- Manipulation

Path Robotics -
Columbus, OH

Quick Apply

Job Details

Benefits

Paid parental leave
Health insurance
Dental insurance
401(k)
Paid time off
Parental leave
Vision insurance
Referral program
Food provided

Qualifications

AI models
Research design
Robot integration
Computer science
Systems integration
Software engineering
Computer Science
Full-stack development
5 years
Mechanical Engineering
Robotics engineering
System design
Integration Architecture Design (Architecture design skills)
Application deployment
Control system design
AI platforms (beyond public GPTs)
Research project design
Master's degree
Doctorate in mechanical engineering
Team development
Hiring
Production systems
Research project technical leadership
C++
Policy & process development
Machine intelligence
Managing engineering teams
Integration architecture
Doctor of Philosophy
Model deployment

Full Job Description

Build the Path Forward

At Path Robotics, we're building the future of embodied intelligence. Our AI-driven systems enable robots to adapt, learn, and perform in the real world closing the skilled labor gap and transforming industries. We go beyond traditional methods, combining perception, reasoning, and control to deliver field-ready AI that is risk-aware, reliable, and continuously improving through real-world use.

Big, hard problems are our everyday work, and our team of intelligent, humble, and driven people make the impossible possible together.

We are standing up a new Robot Learning team focused on whole-body loco-manipulation for precision tasks in heavy manufacturing.

We are seeking a Senior Machine Learning Engineer (Tech Lead) for this new team. You write code, set the technical direction, make the architectural decisions the team builds on, mentor junior and intermediate engineers, and help shape how the team works.

What You'll Do

Set the ML technical direction for the team, architectural choices on perception, reasoning, and action generation; training methodology; data strategy; the path from research bet to deployed capability. As one of the first senior hires, you are designing the approach, not extending it.
Own the architectural workstreams that define the team's research and engineering bets — multiple core build streams across action-policy learning, world-model-based supervision, and policy-orchestration interfaces.
Design hybrid physics-ML architectures for the integrated loco-manipulation stack. Manipulation, locomotion, and the whole-body control coupling between them are not separable on legged platforms; sub-millimetre continuous-trajectory precision at the tool requires the whole-body controller to compensate for base motion in real time. Today on fixed bases; tomorrow on mobile platforms. The integration is the hard problem; you own its design.
Own the cross-functional partnerships with hardware teams, domain experts, customer-facing assurance standards, and upstream / downstream teams. Drive a phased deployment strategy that builds production trust over time.
Mentor and shape the team, guide junior and intermediate ICs across software, ML, robotics, and perception backgrounds; establish code-quality standards, review practices, and engineering norms; help identify and attract next hires. You write code throughout — this is a tech-lead role, not a step away from the work.

Who You Are

Ph.D. or Master's degree in Robotics, Mechanical Engineering, Electrical Engineering, Computer Science, or a related field or equivalent experience.
5+ years of hands-on robot learning experience. You have shipped sim-to-real policies on real robots, across different tasks or platforms.
Demonstrated technical leadership and mentorship. You have made architectural decisions on robot learning systems that others built on, and you have meaningfully shaped the development of more-junior engineers as a tech lead. This can be in academia (leading a lab subgroup, advising students) or industry.
Deep sim-to-real expertise — domain randomisation, system identification, teacher-student distillation, sim-to-online fine-tuning. You can design a transfer strategy for a novel problem.
Full-stack robot learning — fluent across simulation construction, policy training, data collection, real-world deployment, and failure diagnosis.
Physics-informed ML or hybrid control experience — PINNs, neural ODEs, MPC with learned dynamics, process-model-conditioned generation, or similar.
A defensible view on visual-reasoning-centric substrates for grounded spatial / physical reasoning.
Push-back willingness — you can defend a non-obvious architectural commitment under pressure, and change your mind on evidence.
Strong programming skills in Python and C++; production-quality code with reproducibility, testing, and maintainability discipline.
Strong communication skills, able to convey complex technical concepts to a diverse audience.
Demonstrated independent technical authority — you have set technical direction in a tech-lead capacity, leading a research subgroup, owning an architecture across multiple ICs' work, or making the architectural call on a high-profile project.

Strongly Preferred:

Edge inference depth (TensorRT, ONNX, edge-class deployment).
Loco-manipulation experience — locomotion, whole-body control, and manipulation on legged platforms (quadrupeds, humanoids). The coupling between the three is the hard problem; direct hands-on experience or a defensible architectural view both count.
Precision manipulation or surgical robotics — sub-mm accuracy tasks.
Flow-matching action-head design at depth — direct experience with the architectural pattern.
Visual self-supervised representation learning experience on robot or 3D-vision tasks.
Multi-skill workflow or hierarchical policy design — skill sequencing, failure detection, control mode transitions.
Experience building ML capability from early stage — first or second ML engineer on a team, or built a research group's infrastructure from scratch.

Why You'll Love Working Here

Daily free lunch to keep you fueled and connected with the team
Flexible PTO so you can take the time you need, when you need it
Comprehensive medical, dental, and vision coverage
6 weeks fully paid parental leave, plus an additional 6–8 weeks for birthing parents (12–14 weeks total)
401(k) retirement plan through Empower
Generous employee referral bonuses—help us grow our team!

Who We Are

At Path Robotics we love coming to work to solve interesting and tough challenges but also because our ideas are welcomed and valued. We encourage unique thinking and are dedicated to creating a diverse and inclusive environment. All qualified applicants will receive consideration for employment without regard to race, color, religion, gender, gender identity or expression, sexual orientation, national origin, genetics, disability, age, or veteran status.

If you require a reasonable accommodation to participate in the application process or any part of the hiring process, please contact [email protected]. We are committed to providing equal access and will work with qualified individuals to ensure a fair and accessible hiring experience. We will respond to your request within 48 hours.

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Senior Machine Learning Engineer (Tech Lead), Robot Learning, Loco- Manipulation

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