Jobs / United States / Anysphere UK Ltd
Software Engineer, ML Platform
Anysphere UK Ltd · 🇺🇸 San Francisco; New York
Sponsorship verdict
No sponsorship evidence yet
No government record and no wording either way. Not a refusal — ask the recruiter.
- No government sponsor record hereThis employer does not appear with approvals in the government data SponsorApply holds.
- 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.
- 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).
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Sponsor Radar — Anysphere UK Ltd
This employer does not appear with approvals in the government data SponsorApply holds.
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About the role
Our mission is to automate coding. The first step in our journey is to build the best tool for professional programmers, using a combination of inventive research, design, and engineering. Our organization is very flat, and our team is small and talent dense. We particularly like people who are truth-seeking, passionate, and creative. We enjoy spirited debate, crazy ideas, and shipping code. ABOUT THE ROLE As a Software Engineer on ML Platform at SpaceXAI, you'll build the infrastructure that turns real product usage into better models — and keeps research moving fast on large GPU fleets. ML Platform is organized into four teams. Depending on your background, you may join any of them: - Telemetry — Own the collection and serving path that turns real product use into a record research can trust; without slowing the product, and under a small, explicit policy. Client-side or high-volume ingestion experience is a plus. - ML Data Platform — Build the shared environments and pipeline substrate researchers extend, so new experiments don’t fork their own stack. - Observability — Make it easy for researchers to start, watch, and debug their own runs. - ML DevX and Systems — Shorten the path from idea to a trusted run on the research fleet. We're looking for strong distributed-systems and infrastructure engineers who want to sit next to research and ship platform primitives that move the product. We're in-person with cozy offices in North Beach, San Francisco, Palo Alto, and Manhattan, New York, complete with well-stocked libraries. WHAT YOU’LL DO - Design, build, and operate core platform systems used daily by ML researchers and product engineers - Partner closely with research to turn recurring pain into durable infrastructure - Own reliability, performance, and developer experience for the systems in your lane - Ship iteratively in a flat, high-ownership environment. Measure impact, then raise the bar YOU MAY BE A FIT IF - You have a strong background in systems / infrastructure software engineering and enjoy building platforms other engineers depend on - You've owned production distributed systems at meaningful scale (ingestion, data pipelines, scheduling/orchestration, or similar) - You're comfortable across Linux, cloud and/or bare metal, and modern orchestration (Kubernetes, Ray, or equivalent) - You like working closely with ML researchers and product engineers - You thrive where ownership is high and the feedback loop is short ESPECIALLY STRONG BACKGROUNDS BY TEAM - Telemetry: event ingestion, product analytics pipelines, OpenTelemetry / tracing, reliable data APIs - Product Data Platform: data frameworks, Spark / Flink / Ray, ML dataset and training-data infrastructure - Observability: experiment / run monitoring, debug and eval tooling, agent-friendly observability UX - ML DevX and Systems: GPU / cluster scheduling, job queues, node health, research compute developer experience APPLYING If there appears to be a fit, we'll reach out to schedule 2-3 short technicals. After, we'll schedule an onsite in our office, where you'll work on a small project, discuss ideas, and meet the team.