Jobs / United States / Natera INC
Sr. Software Engineer/Tech Lead, Data & AI Engineering
Natera INC · 🇺🇸 US Remote · Remote
Sponsorship verdict
Sponsorship possible
One solid signal, not two — worth applying, and worth asking about sponsorship early.
- Employer is on a government sponsor recordThe US Department of Labor certified 48 H-1B/E-3 labor condition applications for this employer between Oct 2025 and Jun 2026 (latest Jun 2026) — the step every H-1B hire needs first. USCIS also records 12 H-1B approvals in FY2023. Source: LCA disclosure data (US Department of Labor (OFLC)).
- The posting doesn’t mention sponsorshipSilence isn’t a refusal — ask the recruiter before investing much time.
- No salary bar for this routeH-1B has no fixed salary bar: the employer must pay at least the prevailing wage for the role and area. Cap-subject employers enter a lottery weighted by wage level. Source: https://www.federalregister.gov/documents/2025/12/29/2025-23853/weighted-selection-process-for-registrants-and-petitioners-seeking-to-file-cap-subject-h-1b, rules effective 2026-02-27.
- What Natera INC paid sponsored hires in similar roles4 certified filings for “Senior Data Science Production Engineer” (Data Scientists) in CA: $115k–$195k, median $144k. Most were filed at wage level III (50%) — 3 lottery entries, ≈46% projected selection odds for cap-subject employers. Source: US Department of Labor LCA disclosure data (Oct 2025 – Jun 2026).
- Confirmed live todayWhen a source last listed this job as open.
US H-1B: cap-subject employers enter a lottery weighted by wage level — Level I gets 1 entry, Level IV gets 4 (DHS projected selection odds ≈15% at Level I to ≈61% at Level IV). Universities and non-profit research employers are cap-exempt. The $100,000 fee for new petitions from abroad is currently blocked by a court order (appeal pending).
A verdict summarises public evidence; it is not legal advice and never a guarantee — the employer and the immigration authority decide. Sign in to factor in where you can already work.
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Sponsor Radar — Natera INC
The US Department of Labor certified 48 H-1B/E-3 labor condition applications for this employer between Oct 2025 and Jun 2026 (latest Jun 2026) — the step every H-1B hire needs first. USCIS also records 12 H-1B approvals in FY2023. Source: LCA disclosure data (US Department of Labor (OFLC)).
Past sponsorship or register membership never guarantees sponsorship for this vacancy or for you. Full Sponsor Radar for Natera INC →
About the role
About the Role Natera is a global leader in cell-free DNA testing, serving patients across oncology, women’s health, and organ health. Our Data & AI organization builds the enterprise data platform and AI systems that turn clinical, genomic, and operational data into products that improve patient care and accelerate the business. We are looking for a Data & AI Engineering Tech Lead to set the technical direction for a solutions team that builds analytics and AI solutions directly for business stakeholders: data and AI pipelines, data models, semantic layers, and the agents, dashboards, and data products that sit on top of them. This is a hands-on, senior individual contributor role: you will write production code, own architecture and design decisions for the solutions your team delivers, and raise the engineering bar through design leadership, code review, and mentorship. This team is AI-native by design. We expect engineers to use AI coding harnesses and agentic tooling as a core part of how they build, test, document, and operate solutions, and we expect the Tech Lead to model and scale those practices across the team. You will work in a regulated environment where data quality, lineage, and privacy are core requirements of every solution we ship. The ideal candidate is a strong engineer first, with a product mindset toward business outcomes, the judgment to make pragmatic architectural tradeoffs, and the communication skills to align engineers, product managers, and business stakeholders around them. Key Responsibilities Technical Leadership & Architecture • Own the technical design and architecture for the analytics and AI solutions your team delivers; drive design reviews and make build-vs-buy and technology decisions in partnership with the data platform and architecture teams. • Translate business stakeholder needs into well-scoped technical designs, breaking down complex analytics initiatives into deliverable milestones for the team. • Establish and enforce engineering standards for modeling, pipeline design, testing, CI/CD, and observability so solutions are built consistently and reusably across business domains. Hands-On Solutions Engineering • Design, build, and maintain scalable data pipelines and transformations on our cloud data platform, sourcing clinical, laboratory, operational, and commercial data to serve business analytics. • Develop governed data models and semantic layers that power self-service analytics, dashboards, and data products (e.g., Patient 360, Provider 360, Test 360) for business teams. • Develop AI solutions to enable business productivity and automation through use of agentic workflows, RAG/retrieval, and LLM pipelines (extraction/classification) • Write production-grade SQL and Python; contribute to shared frameworks, templates, and tooling that let the team deliver new analytics solutions faster. • Ensure the data layer supports performant, trustworthy analytics and LLM querying ; lead root-cause analysis on complex data issues and drive durable fixes. AI-Native Ways of Working & Mentorship • Use AI coding assistants and agentic tools daily across the development lifecycle—design, coding, testing, documentation, and operations—and measurably increase your own and the team’s throughput. • Define and scale the team’s AI-native engineering practices: prompt and context patterns, reusable agent workflows, AI-assisted testing and review, and the guardrails that keep quality and compliance intact. • Mentor and grow engineers through code review, pairing, and design coaching; partner with the engineering manager on technical hiring, onboarding, and skills development. Data Quality, Reliability & Compliance • Implement data quality checks, contracts, lineage, and monitoring so t