Vizcom Data Engineer, Machine Learning at Vizcom to build systems that convert design session data into training-grade datasets. Collaborate closely with researchers and product teams.
Responsibilities
As an ML Data Engineer, you'll build and improve the systems that turn professional design work into training-grade preference data and training results back into a better product.
You'll work alongside the researchers consuming what you build, within the product systems where these signals originate, and across the data infrastructure where they ultimately land. Your closest users are the researchers sitting beside you, and you'll see quickly when a dataset you've built allows them to ask a question they couldn't ask before.
This is not a support role, and it isn't traditional offline ETL.
The pipelines you work on will run through a live product used every day by professional design teams, including enterprise customers with rigorous expectations around privacy and data protection. Capturing better signals without compromising user trust, contractual obligations, or product performance is a core part of the work.
If you want to train models without building the systems that feed them, this probably isn't the role for you. If you believe the next advances in ML will increasingly be won through better data, it might be.
We think about a dataset as a product: it has users, versions, provenance, and a quality bar. A training result should be reproducible from a dataset fingerprint months later, and "Where did this example come from?" should always have an answer.
Here, helping build that standard is the job.
The capture surface: Partner with Product and Engineering to improve what the product records and help design and ship instrumentation in production systems.
The data pipeline: Build and maintain the path from canvas to warehouse to training set, ensuring data is clean, versioned, reliable, and reproducible.
Dataset contracts and lineage: Implement systems that make examples traceable to their origin and training datasets reproducible over time.
Privacy and data boundaries: Build systems that reflect what can be captured and used under different enterprise agreements, with consent, isolation, and appropriate safeguards designed in from the beginning.
Research-ready datasets: Create appropriately governed and sanitized datasets that allow researchers to experiment safely and effectively.
Qualification
Experience building data infrastructureGood judgment around data qualityA collaborative approach to engineeringAbove allA unique datasetResearchers as your usersMeaningful ownershipYou've worked with preference data
Required
Experience building data infrastructure, ML data systems, or production data pipelines used by other teams.
Strong software engineering skills and experience building reliable production systems.
Experience designing data models and pipelines with reproducibility, observability, and lineage in mind.
Comfort working with large or complex datasets and debugging issues across multiple parts of a data system.
An experimental mindset and comfort working closely with researchers to turn ambiguous questions into measurable datasets.
Good judgment around data quality, including an understanding of when the absence of a signal is meaningfully different from a negative signal.
Comfort operating in an environment where the underlying systems and standards are still evolving.
A collaborative approach to engineering, including documenting decisions, incorporating feedback, and working across Product, Engineering, and Research.
Above all, we're looking for someone who can look at the exhaust of a complex product and see evidence.
A unique dataset: The recorded decisions and workflows of more than 700,000 designers, with new signals generated every day.
Researchers as your users: You'll work directly alongside the people using the datasets you build, creating an unusually tight feedback loop between data engineering and research.
Meaningful ownership: You'll own substantive pieces of Vizcom's ML data infrastructure and have room to expand your scope as you build context and expertise.
Preferred
You've trained models yourself and understand what ML training pipelines actually need from their data.
You've worked with preference data, labeling systems, human-feedback pipelines, or evaluation operations.
You've built data infrastructure under meaningful privacy, security, or contractual constraints.
You've worked with large-scale event or behavioral datasets.
