Jobs / United States / Databricks
Sr. Engineering Manager, AI Runtime
Databricks · 🇺🇸 Mountain View, California; 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 roles4 certified filings for “Sr. Manager, Field Engineering” (Computer and Information Systems Managers) in TX: $209k–$218k, median $209k. Most were filed at wage level IV (75%) — 4 lottery entries, ≈61% 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
At Databricks, we are passionate about enabling data 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. Databricks' AI Runtime (AIR) product provides enterprises with an API for training and fine-tuning deep learning and LLM models with on-demand GPUs. Whether it's a transformer model for drug discovery or a fine-tuned foundation model, customers use this team's training infrastructure to build state-of-the-art frontier models. As a Senior Engineering Manager, you will lead the team owning both the product experience and the foundational infrastructure of AIR. You'll shape customer-facing capabilities while designing for scalability, extensibility, and performance of GPU training and adjacent areas, collaborating closely across the platform, product, infrastructure, and research organizations. The impact you will have: • Lead, mentor, and grow a high-performing engineering team responsible for the Custom Training product and its foundational infrastructure, including distributed training orchestration, cluster lifecycle, fault tolerance, and training efficiency. • Define and own the product and technical roadmap for AIR, balancing customer experience, functionality, and foundational investments. • Collaborate closely with product, research, platform, infrastructure teams, and customers to drive end-to-end delivery, from ideation and prioritization to launch and operation. • Drive architectural decisions and product design for managed GPU training at scale. • Advocate for customer needs through direct engagement, ensuring engineering decisions translate to clear product impact. • Build observability and reliability practices for long-running, multi-node training jobs, including checkpoint strategies, failure recovery, and operational runbooks. • Partner with recruiting to attract, hire, and develop top-tier engineering talent. What we look for: • 8+ years of software engineering experience, with 3+ years in engineering management. • Track record building and operating managed GPU training infrastructure at scale (100s/1000s GPUs). • Deep familiarity with distributed training frameworks (PyTorch, DeepSpeed, Composer, Megatron-LM) and parallelism strategies (FSDP, tensor/pipeline parallelism). • Experience with training resilience patterns: checkpointing, elastic training, and automated failure recovery for long-running jobs. • Understanding of GPU performance fundamentals including NCCL, interconnect topologies, and memory optimization. • Experience building platform products with clear SLAs where you've owned the customer experience, not just the backend. • Strong cross-functional leadership across platform, product, and research teams, with the ability to lead through ambiguity and deliver complex projects. • Excellent collaboration and communication skills across engineering, product, and research organizations. • BS/MS in Computer Science, Electrical Engineering, or related technical field. Pay Range Transparency Databricks is committed to fair and equitable compensation practices. The pay range(s) for this role is listed below and represents the expected salary range for non-commissionable roles or on-target earnings for commissionable roles. Actual compensation packages are based on several factors that are unique to each candidate, including but not limited to job-related skills, depth of experience, relevant certifications and training, and specific work location. Based on the factors above, Databricks anticipates utilizing the full width of