Top Tech Jobs & Startup Jobs

Owns large-scale data projects for frontier AI labs from scoping through delivery. The role translates research needs into data specifications, quality requirements, metrics, and timelines; manages client relationships; coordinates partner companies and internal teams; removes blockers; and ensures high-quality execution. Success requires strong project ownership, analytical ability, communication skills, and comfort working with technical and research stakeholders in ambiguous, high-pressure environments.

Replay invites talented candidates who do not see a conventional role that fits to describe what they have built, what they would like to own, and how they could improve the company. The opportunity is open-ended and may involve creating a function or role aligned with Replay’s data-focused business and rapid growth.

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.

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 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.

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.

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.