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About the Role
As an ML Data Engineer, you'll build and improve systems that transform professional design work into training-grade preference data and training results back into a better product. You will work alongside researchers, within product systems, and across data infrastructure. This role focuses on building systems that feed ML models, not just training them, with an emphasis on data as a product, ensuring reproducibility, versioning, and quality.
Responsibilities
- Partner with Product and Engineering to improve what the product records and help design and ship instrumentation in production systems.
- Build and maintain the data pipeline from canvas to warehouse to training set, ensuring data is clean, versioned, reliable, and reproducible.
- Implement systems that make examples traceable to their origin and training datasets reproducible over time.
- Build systems that reflect what can be captured and used under different enterprise agreements, with consent, isolation, and appropriate safeguards.
- Create appropriately governed and sanitized datasets that allow researchers to experiment safely and effectively.
- Help build mechanisms for gathering explicit feedback that designers actually want to use.
- Design data systems that preserve context, reflecting nuances like 'unpicked' vs. 'disliked' and 'abandoned' vs. 'rejected'.
- Build checks, monitoring, and tooling to identify data gaps, inconsistencies, and unexpected changes.
Requirements
- 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.
- Ability to look at the exhaust of a complex product and see evidence.
Skills
- Data infrastructure
- ML data systems
- Production data pipelines
- Software engineering
- Data modeling
- Data pipeline design
- Reproducibility
- Observability
- Lineage
- Debugging complex data systems
- Experimental mindset
- Data quality assessment
- Collaboration
- Documentation
Location
- San Francisco, CA
Work Type
- In Person
- Full-Time
Experience Level
- Mid-level to Senior
Salary/Compensations
- $163,000 - $208,000
Benefits
- 100% employer-sponsored medical coverage for employees
- 25% medical coverage for dependents
- Dental and vision coverage
- Mental health benefits
- Meaningful equity ownership
- Flexible PTO
- 401(k) with employer match
- Generous annual Learning & Development allowance
- Paid parental leave
- Weekly catered lunch
- Monthly gym membership stipend
About the Company
- Vizcom is a Series B company that provides a platform for design teams at companies like Nike, GM, New Balance, and Hasbro to bring ideas from sketch to product. Designers use Vizcom to sketch, render, explore color and materials, work in 3D, and prepare concepts for production. The platform captures the 'pencil to product' journey, creating a valuable trail of design decisions. Vizcom has raised over $52M and has been used by more than 700,000 designers.