Jobs at Sunset

Design and operate backend systems for high-volume, multi-stage data pipelines that de-identify sensitive enterprise data. Own versioned contracts, lineage, state transitions, retries, replay, reconciliation, verification, migrations, and safe recovery. Diagnose production behavior across services and storage, improve correctness and throughput, and build auditable release controls. Collaborate with ML, data, platform, security, product, and customer-facing teams while using AI tools with deterministic safeguards and human review.

Build end-to-end product features across frontend, backend, data, testing, and observability. Own ambiguous customer problems from discovery through reliable production, improve system reliability and measurements, and integrate AI responsibly. Work with product, design, and customers to make complex workflows understandable, recoverable, and verifiable.

Design and build production-quality frontend interfaces for complex, long-running enterprise workflows. Own interaction design from research and prototypes (Figma, sketches) through implementation in TypeScript and React, including responsive behavior, accessibility, tests, instrumentation, failure states, and reusable components. Work directly with customers, product, and engineering, and use AI tools throughout research and implementation.

Lead and grow an engineering team responsible for enterprise data ingestion, de-identification, ML/evaluation, orchestration, and reliable operations. Hire, coach, define ownership, drive technical direction, review designs and PRs, debug incidents, prototype selectively, and establish lightweight practices for planning, releases, incidents, and quality while keeping hands-on involvement to enable team autonomy.

Build and operate a shared platform to enable product, data, and AI teams to ship reliable, secure, observable, and cost-aware systems. Create IaC modules, runtime templates, deployment workflows, telemetry, recovery and incident processes, self-service developer tooling, and cloud cost and security controls. Drive measurable reliability and reduce toil while partnering with Security and engineering teams.

Build and productionize ML systems for de-identification: improve NER, entity resolution, extraction, and document understanding. Drive error analysis, dataset creation, experiments, evaluation, safe rollouts, inference optimization, and active-learning/LLM-assisted pipelines to improve precision, recall, and utility in live pipelines.

Establish and run evaluation practices for de-identified enterprise data: build corpora, design experiments, quantify uncertainty, detect failure slices, and produce decision-grade evidence using Python, SQL, synthetic data, replay, and human/model judgments to guide model, pipeline, and release decisions.

Ship and operate an LLM-backed dissolution support product: design retrieval, tool contracts, structured outputs, observability, and safe human handoffs; build versioned evaluations, privacy/tenant safeguards, rollout/canary plans, and iterate on production failures to improve product reliability and customer outcomes.

Own full lifecycle sourcing, pitching, negotiating, and closing proprietary data licensing deals for AI model training. Manage complex terms around pricing, data privacy, anonymization, and compliance. Engage C-suite stakeholders, collaborate with engineering and ops to identify valuable datasets, and meet monthly dataset and pipeline targets.

First-point-of-contact SDR responsible for high-volume personalized outreach, booking qualified calls for Account Executives, and maintaining quality in every interaction. Work closely with AEs on sourcing and handoffs, hit monthly qualified-call targets, iterate on approach, and be comfortable discussing data licensing and de-identification when needed.

Own top-of-funnel growth across paid, SEO/AEO/GEO, content, web, and experiments to generate MQLs for Wind-down and Data Partnerships. Build and ship landing pages, campaigns, and automations using AI tools, measure results, and scale successful channels.

Build and maintain GTM infrastructure: enrichment and list-building pipelines, multi-channel outbound systems, CRM integrations and data quality, live reporting and dashboards, and continual testing and measurement to enable AEs and SDRs to source and close efficiently.