ML Infrastructure Engineer
Sunday - Redwood City, CA, Redwood City, California, United States
Posted Feb 11, 2026
Benefits
- Parental leave
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- Non-birth-parent leave
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- Family-building benefits
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- Fertility benefits: Not verified
- Adoption assistance: Not verified
- Surrogacy assistance: Not verified
- Mental health support
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- Relocation assistance
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- Childcare support
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- Learning budget
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- Verification
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- Salary
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- 401(k) match
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Schedule
- Shift type
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- Weekend work
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Application
- Cover letter
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- Assessment
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- Deadline
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Where they hire
State eligibility is not yet verified.
About this role
ML Infrastructure Engineer Redwood City, CA, Redwood City, California, United States 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. The Role Sunday Robotics is building the future of home robotics. We're developing end-to-end ML models for robot manipulation, and you'll have the opportunity to build and shape foundational systems that directly accelerate our path to putting robots in homes. This is a broad role that can be tailored to your specific area of expertise: data pipelines, training infrastructure or inference. You'll build systems across the full robot learning pipeline: ingesting and processing multimodal data, scaling distributed training, optimizing inference for real-time control and building research tooling. What You'll Do Training and Inference Infrastructure - Maintain an effective research codebase with good ergonomics, optimizing for fast iteration and correctness - Own infrastructure for model training: job scheduling, checkpointing, metrics, and logging - Scale distributed training across GPU clusters with minimal researcher friction - Enable training of larger models through sharding, activation checkpointing and memory
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