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Sunset is a rapidly scaling business process automation platform that partners with frontier AI labs by providing proprietary enterprise data for model training. The company has grown from $0 to multi-eight-figure run rate in months and is backed by top-tier investors including Floodgate and Afore.
You will own the backend systems that make data pipelines correct, replayable, operable, and safe to change. This role goes beyond conventional data-platform engineering: you'll build systems where silent failures (dropped records, duplicates, stale policies, lost lineage) are made visible, preventable, and recoverable.
Key responsibilities include:
- Design and ship backend systems for multi-stage, high-volume data processing
- Define authoritative, versioned contracts for manifests, artifacts, lineage, and state transitions
- Make retries, checkpoints, partial failures, replay, backfills, migrations, and rollbacks safe and understandable
- Build independent reconciliation and verification systems rather than treating job success as proof of correctness
- Turn escaped and recurring failures into fixtures, regression coverage, release gates, and durable recovery paths
- Expose trustworthy run state and safe controls to products used for data investigation and release
- Diagnose production behavior across code, queues, stores, artifacts, data formats, and deployed versions
- Improve correctness, throughput, and operating leverage without weakening privacy, security, or release confidence
- Use AI deeply in development and in bounded verification systems with explicit evaluation and independent checks
- Partner with ML, applied science, full-stack product, platform, security, and data engineering teams
Success means high-risk pipeline boundaries have explicit contracts and independent reconciliation; missing, duplicated, or stale work is detected before customer delivery; material pipeline state is backed by queryable provenance; and adjacent engineers can add stages through supported patterns rather than one-off scripts.
