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Principal Robotics Machine Learning Engineer
AI in this role
Principal Robotics ML Engineer to lead the end-to-end lifecycle of learned manipulation models for humanoid robots from simulation to deployment.
Apptronik is a human-centered robotics company developing AI-powered robots to support humanity in every facet of life. Our flagship humanoid robot, Apollo, is built to collaborate thoughtfully with people, starting with critical industries such as manufacturing and logistics, with future applications in healthcare, the home, and beyond.
We operate at the cutting edge of Applied AI, applying our expertise across the full robotics stack to solve some of society's most important problems. You will join a team dedicated to bringing Apollo to market at scale, tackling the complex challenges like safety, commercialization, and mass production to change the world for the better.
JOB SUMMARY
The Principal Manipulation Engineer is a core contributor to our robot’s ability to interact with the world with human-like world model context, dynamic perception, and interactive behavior. This role is responsible for the end-to-end lifecycle of learned manipulation, from data engine design through training to on-robot deployment and is expected to diagnose performance bottlenecks. This requires deep fluency in both classical computer vision and modern deep learning to unlock the full potential of state-of-the-art humanoid robot hardware.
This role will bridge the gap between cutting-edge research and scalable, reliable production software. We move at startup pace; intense and focused, but sustainable. We operate with high ownership, fast feedback, and low bureaucracy.
ESSENTIAL DUTIES AND RESPONSIBILITIES or KEY ACCOUNTABILITIES
- Implement and deploy state-of-the-art ML models/algorithms to achieve ambitious, world-class performance on open world object manipulation tasks with physical hardware.
- Drive the data engine for manipulation learning: data collection strategy, curation, annotation, and synthetic/real data mix, in close partnership with data infrastructure teams.
- Drive the entire development cycle, from prototyping in simulation to robustly transferring and fine-tuning models on the robot.
- Identify and prioritize performance bottlenecks, distinguishing between root causes traceable to classical vision (calibration, geometry, sensor fusion) vs those rooted in sub-optimal model/data, to prioritize fixes according to customer-expected levels of reliability.
- Act as a force multiplier across the organization. Beyond code reviews, foster a culture of technical rigor, setting the bar for architectural excellence and mentoring the next generation of robotics leaders.
SKILLS AND REQUIREMENTS
Technical Skills (Must-Have)
- Deployment Experience: 5+ years shipping machine learning models on robotic systems in production environments.
- AI Model Development Proficiency: Expertise in models including CNNs, transformers
- Software Engineering: Proficiency in Python, C++, and PyTorch, with experience building real-time robotic software stacks.
- AI Data Pipeline Experience: Experience with data collection, annotation, and synthetic data generation for ML.
- Computer Vision: Familiarity with 6D pose estimation, camera calibration/extrinsics, point cloud processing and visual-servoing.
- Hardware Bring-up: Experience with the initial calibration and tuning of high-DOF robotic manipulators.
Good to Have
- ML Ops: Familiarity with distributed training and MLOps principles.
- Teleoperation: Experience with VR/haptic interfaces and retargeting algorithms for human-in-the-loop control.
- Simulation Environments: Experience with physics engines such as IsaacSim, MuJoCo, or Drake for policy training and validation.
- Logistics Automation Background: Experience using manipulators to automate dynamic real-world material handling tasks.
EDUCATION and/or EXPERIENCE
- BS/MS/PhD in Robotics, Computer Science, Electrical Engineering, or a related field.
- 7+ years of relevant experience (or 5+ years with a PhD) specifically focused on robotic manipulation or complex motion control.
- A proven track record of taking complex algorithms from conception to successfully deploying them on physical hardware.
PHYSICAL REQUIREMENTS
- Prolonged periods of sitting at a desk and working on a computer
- Must be able to lift 15 pounds at times
- Vision to read printed materials and a computer screen
- Hearing and speech to communicate
*This is a direct hire. Please, no outside Agency solicitations.
Apptronik provides equal employment opportunities to all employees and applicants for employment and prohibits discrimination and harassment of any type without regard to race, color, religion, age, sex, national origin, disability status, genetics, protected veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by federal, state or local laws.
How we rate this
Principal Robotics Machine Learning Engineer at Apptronik rates 95 out of 100 for how much of the daily work is AI. That makes it Builds AI (AI Level 4 of 4). The level is about AI in the job, not seniority.
Builds AI. The job is building AI systems.
- ●●●● Builds AI80 to 100
- ●●●○ Works on AI60 to 79
- ●●○○ Uses AI40 to 59
- ●○○○ Little AI0 to 39
Levels come from how often the tools, models and workflows of the role are named in the posting itself. Read the description and count the AI tools, models and workflows it names.
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Built from this posting and from similar jobs on Level.
Skills and AI tools this role asks for
Also common in machine learning engineer jobs at this level
Questions you could be asked
- Walk me through fine-tuning a model: what data did you use, and how did you check the result?
- How do you monitor a model once it's live, and how do you know it needs retraining?
- Walk me through a computer vision problem you solved, from raw data to a deployed model.
- Tell me about a project where robotics was part of your work. What did you do?
- Tell me about a project where machine learning was part of your work. What did you do?
Adapt your resume
- List these exact terms on your resume: Fine-tuning, ML Ops, Computer vision, Robotics, and Machine learning. An applicant tracking system matches the wording, not the idea.
- Attach one line of real, concrete experience to at least one of them. A tool named with nothing behind it rarely survives a human read.
- Lead with what you built, trained or shipped. This role is judged on the AI system itself, not the tools around it.
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