Senior Annotation and Data Pipeline Manager

Genesis AI; San Carlos (CA); Presencial; USD 180 000 - 300 000; Tempo integral

Resumo da oferta

Genesis AI seeks a senior data/ML pipeline specialist to own the data engine and dataset ontology, turning raw robot demonstrations into training-ready data at scale. You will manage auto-labeling, language-grounding, and data synthesis to keep the model improving while coordinating across the team.

You will lead the labeling operation, scale internal and vendor annotation, and maintain a focus on quality with measurable metrics. Ambiguity is common; speed and rigorous execution are required.

Qualificações

• Four or more years in data or ML pipelines, including time leading the work; at frontier AI labs or top data operations, you have taken raw robot or embodied data to training-ready scale.
• Strong Python (Pandas, NumPy, PyTorch) and SQL; you write the automation that shrinks the pipeline.
• ML literacy: you understand training vs. test, accuracy metrics, and overfitting well enough to design an ontology that helps the model, not just label data.
• Hands-on technical leadership: you can run a labeling operation while staying hands-on as a contributor.
• Comfortable with ambiguity and speed in a research-paced environment; you bring order to complexity.

Responsabilidades

• Run the data engine from raw trajectory/video to training-ready datasets with validation steps that guarantee clean, correctly labeled data.
• Own datasets and ontology; decide what gets annotated and how, designing the ontology with the model team for training implications.
• Automate with vision-language models for automated trajectory annotation, language grounding, and data synthesis to scale the pipeline without linear headcount.
• Run the annotation operation; stand up and scale labeling, internal and vendor, against a clear quality bar and a delivery schedule the model team can plan around.
• Close the loop; turn real-robot eval failures into targeted collection and annotation jobs, and prove the new data improves the model.
• Own the metrics; track inter-annotator agreement, label error rate, and throughput per annotator-hour.

Conhecimentos

Python

Pandas

NumPy

PyTorch

Ferramentas

SQL

Descrição da oferta de emprego

About Genesis

We are a full-stack general-purpose robot company, born across San Francisco and Paris and bound by a single mission: to make general-purpose robots a reality, unlocking infinite physical labor for the world and freeing people for creativity, exploration, and the pursuits they love for their own sake.

We are French and American by birth and global by nature. We are backed by partners who share our vision, including Eclipse, Khosla Ventures, Bpifrance, and HSG, alongside Eric Schmidt, Xavier Niel, Daniela Rus, and Vladlen Koltun. Our people work across the Bay Area and Europe, building the engine that will teach robots to do real work in the real world. We recently unveiled Eno, our wheeled, dexterous robot powered by our GENE foundation model, and we are starting to put it to work with real customers.

The role

We have built a frontier model and put Eno in front of the world, fast. Behind that is a data engine: the machine that turns a raw human demonstration into data the model is measurably better for. This role owns that engine.

A worn glove and a camera produce a raw demonstration, not training data. You will build the pipeline and the annotation operation that turn raw demonstrations into clean, labeled, training-ready data, and make it scale with automation rather than headcount. You will own the datasets, what gets annotated, and the ontology, how it gets labeled, bring vision-language models to bear on trajectory labeling and language grounding, and close the loop so the engine keeps making the model better. This role serves the whole operation, our own floors and our partner-funded collection.

What you'll do

• Run the data engine. Own the loop from raw trajectory and video to training-ready datasets, with validation steps that guarantee clean, correctly labeled data.

• Own datasets and ontology. Decide what gets annotated and how, designing the ontology with the model team for its training implications.

• Automate with models. Use vision-language models for automated trajectory annotation, language grounding, and data synthesis, so the pipeline scales without linear headcount, while holding the quality bar.

• Run the annotation operation. Stand up and scale labeling, internal and vendor, against a clear quality bar and a delivery schedule the model team can plan around.

• Close the loop. Turn real-robot eval failures into targeted collection and annotation jobs, and prove the new data improves the model.

• Own the metrics. Track inter-annotator agreement, label error rate, and throughput per annotator-hour, and drive them the right way.

What we're looking for

• You have scaled an annotation or data pipeline at a serious operation. Four or more years in data or ML pipelines, including time leading the work. At a frontier AI lab or a top data operation, you have taken raw robot or embodied data to training-ready at volume and you know exactly where it breaks. The people who have done this are a small group. If you are one, we want to talk.

• You can build, not just manage. Strong Python (Pandas, NumPy, PyTorch) and SQL. You write the automation that shrinks the pipeline.

• ML literacy. You understand training versus test, precision and recall, and overfitting well enough to design an ontology that helps the model, not just labels data.

• Hands-on technical leadership. You can run a labeling operation and stay a hands-on contributor at the same time.

• Comfortable with ambiguity and speed. You move fast in a research-paced environment and bring order to it.

How we work

We are always looking for people who are driven to work on hard problems and want to shape what the world will look like in ten years. A few of the values that guide us:

• Limitless ambition. We hold ourselves to the highest bar in everything we build.

• Relentless urgency. Iterate faster, learn faster. Time is the denominator.

• Type-2 Fun. Take the work seriously, not yourself. Embrace the grind and find joy in the struggle.

• Radical transparency. Candid communication builds trust, and direct feedback drives growth.

• Multiculturalism. We come from across the world. Be curious. Meet people on their terms.

• Team over ego. Help others, share context, and win as a team.

• A joyful journey. We take pride in the craft, enjoy the ride, and make space to laugh. Happiness is part of how we get there.