Jobs / United States / Databricks
Staff Backline Engineer – ML/AI
Databricks · 🇺🇸 Bellevue, Washington; San Francisco, California
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 445 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 78 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 Databricks paid sponsored hires in similar roles103 certified filings for “Software Engineer” (Software Developers) in CA: $158k–$191k, median $188k. Most were filed at wage level II (47%) — 2 lottery entries, ≈31% 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 — Databricks
The US Department of Labor certified 445 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 78 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 Databricks →
About the role
P - 1381 At Databricks, we are passionate about enabling Data & AI teams to solve the world's toughest problems — from making the next mode of transportation a reality to accelerating the development of medical breakthroughs. We do this by building and running the world's best data and AI infrastructure platform so our customers can use deep data insights to improve their business. Founded by engineers, we leap at every opportunity to tackle technical challenges, from designing next-gen UI/UX for data interaction to scaling our services and infrastructure across millions of virtual machines. And we're only getting started. About the Team The Backline Engineering Team serves as the critical bridge between Frontline Support and Engineering. We handle complex technical issues and escalations across the Data and AI ecosystem. With a strong focus on customer success, we are committed to delivering exceptional customer satisfaction by providing deep technical expertise, proactive issue resolution, and continuous platform improvements. We emphasise automation and tooling to enhance troubleshooting efficiency, reduce manual efforts, and improve the overall supportability of the platform and the health of our products. By developing smart solutions and streamlining workflows, we drive operational excellence and ensure a delightful experience for both customers and internal teams. As a Staff Backline Engineer, you will be a technical expert and escalation point for some of the most complex ML/AI issues. You will work across Support, Engineering, Product, and customers to troubleshoot difficult problems, reproduce issues, identify root causes, and drive them to resolution. What You'll Do • Serve as a senior escalation point for complex ML/AI issues involving model training, inference, Model Serving, MLflow, Feature Engineering, with knowledge of Spark, Delta Lake, and distributed workloads. • Perform deep technical investigations using logs, traces, metrics, profiling, configuration, source code, and customer workloads to identify root cause. • Reproduce customer issues through hands-on experimentation, Python/Spark development, workload construction, configuration changes, and performance analysis. • Troubleshoot model training and inference failures, performance degradation, resource utilization, memory/CPU/GPU issues, distributed execution problems, and deployment/runtime failures. • Partner closely with Engineering and Product teams to drive difficult issues to resolution and influence product improvements. • Identify recurring failure patterns and turn them into better diagnostics, documentation, tooling, automation, and Claude skill capabilities. • Mentor engineers and raise the technical troubleshooting capabilities of the broader Support organization. • Act as a technical SME for ML/AI platform areas and contribute to cross-functional initiatives with global impact. What We Look For • Deep troubleshooting experience with distributed ML/AI systems and the ability to debug problems across application code, frameworks, infrastructure, and the Databricks platform. • Strong hands-on Python experience and the ability to build, modify, and debug ML workloads using frameworks such as PyTorch, TensorFlow, or Scikit-Learn. • Strong understanding of Databricks ML/AI technologies, including MLflow, Model Serving, Feature Engineering, Spark MLlib, and model lifecycle management. • Strong Apache Spark knowledge, including DataFrames, query execution, distributed computing, memory management, shuffles, and performance optimisation. • Experience troubleshooting training and inference performance, including CPU/GPU utilisation, memory issues, data bottlenecks, concurrency, latency, and distributed execution. • Experience with M