Data Engineer, Machine Learning

Vizcom • San Francisco, CA

9d • $163,000 - $208,000 • Onsite

About The Position

Vizcom is hiring a Data Engineer, Machine Learning to build systems that turn professional design work into training-grade preference data and connect training results back to product improvements. The role works closely with researchers, Product, Engineering, and data infrastructure teams. It is a production-facing position involving systems used by professional design teams, including enterprise customers, where privacy, data protection, contractual obligations, and product performance are core considerations.

The engineer will help create a reliable path from product activity to research-ready training datasets. This includes designing data capture and instrumentation, maintaining pipelines from canvas to warehouse to training set, and ensuring datasets are clean, versioned, traceable, and reproducible. The role also develops safeguards for data privacy and enterprise boundaries, creates governed and sanitized datasets, supports explicit feedback collection, preserves ambiguity in behavioral data, and builds data quality checks and monitoring.

Vizcom views datasets as products with users, versions, provenance, and quality standards. The engineer will help establish practices for dataset fingerprints, lineage, validation, quality, and governance, working with the broader team to prioritize and expand ownership over time. The role is in person at Vizcom's San Francisco headquarters.

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 understanding when the absence of a signal differs meaningfully from a negative signal.
• Comfort operating in an environment where underlying systems and standards are still evolving.
• A collaborative engineering approach, including documenting decisions, incorporating feedback, and working across Product, Engineering, and Research.

Nice To Haves

• Experience training models and understanding what ML training pipelines need from their data.
• Experience with preference data, labeling systems, human-feedback pipelines, or evaluation operations.
• Experience building data infrastructure under meaningful privacy, security, or contractual constraints.
• Experience with large-scale event or behavioral datasets.
• Experience with data systems supporting generative AI or multimodal models.

Responsibilities

• Partner with Product and Engineering to improve product data capture and design and ship production instrumentation.
• Build and maintain pipelines from the product canvas through the warehouse to training datasets, ensuring data is clean, versioned, reliable, and reproducible.
• Implement dataset contracts and lineage systems that trace examples to their origins and make training datasets reproducible over time.
• Build systems that enforce data capture and usage rules under enterprise agreements, including consent, isolation, and appropriate safeguards.
• Create governed and sanitized datasets that allow researchers to experiment safely and effectively.
• Help build mechanisms for gathering explicit feedback from designers when historical signals are insufficient.
• Design data systems that preserve context and distinguish between ambiguous behaviors such as unpicked, disliked, abandoned, or rejected.
• Build data quality checks, monitoring, and tooling to detect gaps, inconsistencies, and unexpected changes before they affect research or training.
• Collaborate with researchers and the broader team to identify research questions the data can answer and prioritize improvements to the ML data stack.
• Document implementations and validate that resulting datasets behave as expected.
• Contribute to standards for dataset fingerprints, lineage, quality, and data boundaries.

Benefits

• 100% employer-sponsored medical coverage for employees, plus 25% coverage toward dependents.
• Dental and vision coverage, plus mental health benefits.
• Meaningful equity ownership.
• Flexible PTO.
• 401(k) with employer match.
• Generous annual Learning & Development allowance.
• Paid parental leave.
• Weekly catered lunch at the San Francisco headquarters.
• Monthly gym membership stipend.

What This Job Offers

• Job Type: Full-time
• Education Level: No Education Listed