About Us
Foundation is developing the future of general purpose robotics with the goal toaddress the labor shortage.https://foundation.bot
Our mission is to create advanced robots that can operate in complex environments, reducing human risk in conflict zones and enhancing efficiency in labor-intensive industries.
We are on the lookout for extraordinary engineers and scientists to join our team.
We expect that many of our team members will bring diverse perspectives from various industries and fields. We are looking for individuals with a proven record of exceptional ability and a history of creating things that work.
All positions are based in San Francisco
Our Culture
We like to be frank and honest about who we are, so that people can decide for themselves if this is a culture they resonate with. Please read more about our culture herehttps://foundation.bot/culture.
Who should join:
- You like working in person with a team in San Francisco You deeply believe that this is the most important mission for humanity and needs to happen yesterday.
- You are highly technical - regardless of the role you are in. We are building technology; you need to understand technology well.
- You care about aesthetics and design inside out. If it"s not the best product ever, it bothers you, and you need to “fix” it.
- You don"t need someone to motivate you nor give you tasks; you get things done.
Why are We Hiring for this Role
- Deploy and maintain ML models in production, with a focus on retraining pipelines and lifecycle management.
- Design and manage automated workflows using Apache Airflow, including one-time job DAGs for ad hoc processing and batch workloads.
- Work with unstructured data, particularly video, for model training, inference, and evaluation.
- Optimize model performance in distributed computing environments using tools like Ray, ClearML, or Polyaxon.
- Ensure scalable, resilient model serving via cloud-native platforms (AWS, GCP, or Azure).
- Maintain containerized ML workflows with Kubernetes, ensuring reproducibility, observability, and fault tolerance.
- Collaborate with MLOps and data engineering teams to ensure data availability, traceability, and governance in training pipelines.
What Kind of Person are We Looking for
- 5+ years of experience in ML engineering or MLOps roles.
- Proven experience deploying ML models to cloud platforms (AWS, GCP, or Azure), including monitoring and retraining workflows.
- Solid hands-on experience with Apache Airflow, including DAG authoring and orchestration for recurring and one-time jobs.
- Experience with video data and other unstructured data types in a production ML context.
- Familiarity with distributed training and inference frameworks such as Ray, ClearML, Polyaxon, or similar.
- Working knowledge of Kubernetes for model deployment and orchestration.
- Proficiency in Python and common ML/AI frameworks (e.g., TensorFlow, PyTorch, scikit-learn).
- Strong problem-solving skills and an ability to work collaboratively in cross-functional teams.
- 3+ years of experience in ML engineering or MLOps roles.
- Experience with data versioning tools (e.g., DVC, MLflow).
- Exposure to real-time inference pipelines or edge deployment of models.
- Prior experience in robotics, autonomy, or IoT is a plus.
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