Jobs / United States / Modal
Inference Engineering and Product Lead
Modal · 🇺🇸 San Francisco
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 10 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. 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 Modal paid sponsored hires in similar roles1 certified filing for “ML Performance Engineering Manager” (Computer and Information Systems Managers) in NY: $300k–$300k, median $300k. Most were filed at wage level II (100%) — 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 — Modal
The US Department of Labor certified 10 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. 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 Modal →
About the role
ABOUT US: AI needs a new infrastructure layer. We're building it at Modal. Every era of computing brought new workloads that previous infrastructure couldn't support: mainframes, databases, and the cloud. Each time, the company that rebuilt the layer underneath defined the decade. AI is no different, except it touches everything instead of one slice, and the window to build the layer underneath it is open right now. Our customers include category-defining companies like Lovable https://modal.com/blog/lovable-case-study, Ramp https://modal.com/blog/how-ramp-built-a-full-context-background-coding-agent-on-modal, Cognition, DoorDash, and Suno. They rely on Modal for instant GPU access, sub-second container starts, and native storage, so it's simple to serve low-latency inference, fine-tune models, and access production-ready sandboxes at scale. We recently raised a $355M Series C https://modal.com/blog/modal-series-c at a $4.65B valuation, led by General Catalyst and Redpoint Ventures. We've crossed $300M+ ARR and grown fivefold since September. Our team includes creators of popular open-source projects (e.g.,Seaborn https://github.com/mwaskom/seaborn,Luigi https://github.com/spotify/luigi), academic researchers, international olympiad medalists, and experienced engineering and product leaders with decades of experience. The Role Modal's LLM inference platform delivers frontier performance https://modal.com/blog/kimi-k3-by-moonshot-now-available-on-modal for open-source models with best-in-class elasticity and developer experience, made in part possible by our custom runtime with GPU memory snapshots https://modal.com/blog/gpu-mem-snapshots and multi-cloud substrate https://modal.com/blog/resource-solver. We're looking for a leader to own the direction and execution of this platform to continue to establish us as the clear market leader, working closely with customers like Cognition, Doordash, Ramp, and many more. You'll be leading a group of highly talented engineers working on our market-leading LLM inference offering, spanning the serving stack, routing infrastructure, internal agentic optimization platform, and the user-facing product surface area. This is a hands-on leadership role — expect to split your time between technical contribution, product shaping and people management depending on what the team needs. You'll set direction, remove blockers, and build a strong engineering culture as your team tackles hard problems in distributed computing, frontier inference serving, and performance optimization. Responsibilities Team - Recruit, hire, and grow a high-performing team of engineers; run regular 1:1s focused on coaching, feedback, and career growth. - Set clear performance expectations, hold a high bar, and build an environment where engineers do their best work. - Foster a culture of ownership, accountability, customer obsession, and continuous improvement. Technical and Product Direction - Drive technical and product decisions through design reviews, code reviews, and architectural discussions. - Lead our efforts working with customers with novel or frontier workloads so they can be successful running on Modal. - Translate learnings from frontier customers into a roadmap for our internal optimization platform and user-facing product, so that gains from optimization and research efforts are accessible to all of our users. - Establish standards for reliability and product excellence; ensure the team owns projects end-to-end, from spec through production. Cross-Functional Leadership - Partner with business operations and compute strategy on continuing to develop our strategy for compute purchases across a variety of accelerators. - Collaborate with GTM on product launches, positioning, and i