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Data Engineer, Machine Learning

VizcomSan FranciscoEPD (Engineering, Product, Design)
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Salary
$163K - $208K
Job type
Full-time
Work mode
Remote
Posted
22 Sept 2026
Status
Open · checked 7d ago

About this role

The Machine Learning Data Engineer position at Vizcom in San Francisco is an in-person, full-time role suited for an experienced engineer passionate about building robust production data infrastructure. This opportunity allows the right professional to transform professional design activity into high-quality training data for cutting-edge machine learning research.

Role Overview

As a Machine Learning Data Engineer, you will build and improve the systems that convert professional design sessions into training-grade preference data, while also feeding training outcomes back into the core platform. Working directly alongside researchers and engineering teams, you will design the data pipelines, capture surfaces, and governance frameworks that sit at the intersection of a live production environment and advanced AI research. This role goes beyond traditional offline ETL by operating within a real-time product utilized by major enterprise design teams.

Key Responsibilities

Partner with Product and Engineering to capture design signals and ship telemetry instrumentation in production systems.
Build and maintain reliable, clean, versioned, and reproducible data pathways from canvas to data warehouse to training sets.
Implement data lineage and dataset contracts that ensure every training example is fully traceable to its source.
Establish privacy boundaries and safeguards that respect enterprise agreements, user consent, and data isolation from day one.
Generate sanitized and governed datasets that empower researchers to experiment safely.
Create new feedback-gathering mechanisms when historical product signals require enhancement.
Design data architectures that capture context and ambiguity accurately, distinguishing between abandoned directions and unselected options.
Develop quality checks, monitoring systems, and observability tooling to detect inconsistencies before training impacts occur.

Requirements

Experience building production data pipelines, machine learning data systems, or data infrastructure utilized by cross-functional teams.
Strong software engineering capabilities focused on reliable production systems.
Demonstrated experience in designing data models with a strong emphasis on lineage, reproducibility, and observability.
Comfort in working with complex or large-scale datasets while debugging across multiple system layers.
An experimental mindset with the ability to collaborate closely with researchers to convert ambiguous challenges into measurable datasets.
Excellent judgment regarding data quality and signal interpretation.
Adaptability to operate smoothly within an evolving technical environment.
Collaborative engineering approach centered on documentation, constructive feedback, and cross-functional execution.

Nice to Have

Personal experience training machine learning models and understanding downstream data dependencies.
Background working with human-feedback pipelines, preference data, evaluation operations, or labeling systems.
History of constructing data infrastructure under strict contractual, privacy, or security constraints.
Familiarity with large-scale behavioral or event-based datasets.
Previous exposure to data systems supporting multimodal models or generative artificial intelligence.

Skills and Tools

Production data pipelines and ETL design
Data warehousing, versioning, and lineage systems
Data governance, privacy frameworks, and consent management
Event instrumentation and telemetry capture
Monitoring, observability, and data quality assurance tooling

Salary and Benefits

The base salary for this position ranges from $163,000 to $208,000, accompanied by meaningful equity ownership. Total compensation packages are benchmarked against industry data for Tier 1 markets such as San Francisco. Comprehensive benefits include 100% employer-sponsored medical coverage for employees, dental and vision care, mental health benefits, a 401(k) plan with employer match, flexible PTO, paid parental leave, an annual Learning & Development allowance, monthly gym stipends, and weekly catered lunches at the San Francisco headquarters.

About the Company

Vizcom is a Series B company with over $52 million raised, empowering design teams at renowned organizations like Nike, GM, New Balance, and Hasbro to take ideas from initial sketch to physical product. Over 700,000 designers use the platform to render, sketch, explore materials, and prepare concepts for real-world production. The company values documentation, reproducible research results, and practical problem-solving over traditional algorithmic interviews.

How to Apply

Apply directly through the official company careers page on the Vizcom website to ensure your application is reviewed fairly and promptly.

Skills and tech stack

Machine LearningGenerative AI

Questions candidates ask

Is this position remote or located in an office?

This is an in-person role based at Vizcom's headquarters in San Francisco, California.

What is the salary range for the Machine Learning Data Engineer role?

The base salary ranges from $163,000 to $208,000, plus equity and a comprehensive benefits package.

What kind of data will I be working with?

You will work with behavioral and preference data generated by professional designers using the Vizcom platform, turning real-world design sessions into training-grade datasets for researchers.

Who will I work with most closely in this role?

Your closest users and colleagues will be the research team sitting beside you, alongside Product and Engineering partners.

What are the primary benefits provided by Vizcom?

Benefits include 100% employer-sponsored medical coverage for employees, dental and vision benefits, a 401(k) with employer match, flexible PTO, paid parental leave, mental health support, learning allowances, and weekly catered lunches.

Ready to apply?

The application is on the official Vizcom careers page.

Apply on company site