Data Visualization Engineer for Live AI Data Pipelines

Build AI; San Francisco (CA); On-site; USD 140,000 - 210,000; Full time

Benefits offered by this job

Competitive pay

Medical, dental, and vision packages

Housing subsidy

Relocation support

Wellness benefits

Daily lunch and dinner

Unlimited compute budget

Unlimited Codex and Claude credits

Travel

Job summary

Build AI is hiring a software engineer to build the visualization and analysis layer for the dataset and data engine. You’ll ship a UI/web or CLI to expose collection health, quality, evals, and pipeline status, making it fast to inspect data than dumping to notebooks.

You’ll work with the Head of Dataset & Quality, Evals Lead, research, and ops to decide what should be visible; expect to treat viz as a product with attention to latency, defaults, and shareability in a fully in-person SF team.

Qualifications

• Strong software engineer with a track record of data viz, internal tools, or product UI on heavy data.
• You care about making other people faster, not only about a pretty demo.
• Comfortable with large media — video, pose, or similar.
• Fine in a 0→1 environment where “everything” is still being defined.

Responsibilities

• Design and ship a visualization suite: data collection, processing, analysis, and UI (web and/or CLI) for the dataset and the data engine.
• Make looking at examples, batches, coverage, quality, and failures fast enough that research and ops actually do it.
• Build views for collection health, taxonomy coverage, and eval results, wired to live pipelines rather than one-off dashboards.
• Work with Head of Dataset & Quality, Evals Lead, research, and ops on what must be visible to make a decision.
• Treat viz as a product: latency, defaults, shareability, and a bar for how we look at video and pose.

Skills

Data viz

Internal tools

Performance minded

Large media handling

Tools

Python

TypeScript/React

Job description

Build AI is hiring a software engineer to build the visualization and analysis layer for the dataset and data engine. You’ll ship a UI/web or CLI to expose collection health, quality, evals, and pipeline status, making it fast to inspect data than dumping to notebooks.

You’ll work with the Head of Dataset & Quality, Evals Lead, research, and ops to decide what should be visible; expect to treat viz as a product with attention to latency, defaults, and shareability in a fully in-person SF team.