Machine Learning Research Engineer/Scientist

TLDR

Engage in developing advanced robot learning algorithms and collaborate within a cross-functional team to enhance home robotics technology that transforms everyday tasks.

Join Us in Building the Future of Home Robotics

At Sunday, we're developing personal robots to reclaim the hours lost to repetitive tasks. We're focused on an ambitious goal to make generalized robots broadly accessible, enabling households to take back quality time.

We have spent the last 18 months building a talented team, securing capital, and validating our technology. We are now seeking passionate individuals to join us in the next phase of our growth. If you are ready to apply your skills to the forefront of robotics innovation, we’d love to hear from you.

What to Expect

In building robots for the home, we are tackling a true grand challenge in robotics: dexterous and safe mobile manipulation in unstructured environments. To make this possible, we are building across the entire robotics stack. We’re training state-of-the-art AI models that leverage our large-scale, high-quality, real-world data collection system. At the same time, we’re building a new kind of consumer hardware product, which will be deployed into homes and delivering real value for real customers. As an early member of a small, cross-functional team, you’ll play a key role in pushing both our technology and product forward: advancing the frontier of embodied AI, and soon giving countless hours back to our customers so they can spend more time on the things they value most.

As a Machine Learning Research Engineer, you will work on the software and algorithms that enable our robots to complete dexterous manipulation tasks in home environments. You will leverage our unique large-scale in-the-wild data collection operation and our growing robot fleet to continuously add new behaviors and improve robustness across tasks and environments.

What You'll Do

  • Design and develop state-of-the-art robot learning algorithms for manipulation and controls

  • Leverage large-scale in-the-wild data collection to develop generalizable robot behaviors

  • Own the end-to-end loop from task definition and data curation to model training, evaluation, and on-robot deployment

  • Collaborate closely with a full-stack robotics team to develop a consumer personal robot

  • Write maintainable, production quality code for research and deployment

What You'll Bring

  • 3+ years (or equivalent) working on machine learning for robotics, controls, or perception

  • Proficiency with Python and any deep learning framework (PyTorch preferred)

  • Hands-on experience with robot learning for real-world tasks, including offline/online training and evaluation

  • Implement tooling for visualization, debugging, and evaluation of data and learning pipelines

  • Communicates clearly and collaborates effectively, thriving in fast-moving, cross-functional teams

Nice to Have

  • Familiarity with classical robotics fundamentals (e.g. controls, planning, state estimation)

  • Comfortable profiling and optimizing for latency, memory, and compute on edge devices

  • Experience with large-scale model training pipelines (LLM/VLM/VLA)

At Sunday Robotics, we’re building technology shaped by real people — curious, creative, and diverse. We’re proud to be an equal opportunity employer and consider all qualified applicants regardless of race, color, religion, gender, gender identity or expression, sexual orientation, national origin, genetics, disability, age, or veteran status.

Even if you don’t meet every single requirement, we encourage you to apply. Studies show that women and underrepresented groups often hold back unless they meet 100% of the criteria — we don’t want that to be the reason we miss out on great talent.

Sunday is building personal robots that take care of repetitive tasks, allowing busy households to reclaim their valuable time. Our focus is on making these smart, generalized robots accessible to everyone, freeing families to spend more time on what truly matters.

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