AI role · Open offer

ML Engineer — GPU cloud

Lambda

This listing is optimized for AI-native talent: clear skills and tools so candidates know if they fit before they apply—and so you spend less time screening mismatches.

3 skills1 tool

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At a glance

How you'll work, where, and key listing dates.

Employment: Full-time
Workplace: Hybrid
Location: 1101 E 5th Street, Austin, TX 78702, United States · Hybrid
Compensation: $150k–$200k + equity
Experience level: Senior
Team: Cloud ML
Benefits & perks

About this role

What the team is looking for and how this position fits.

Lambda Cloud offers GPU infrastructure for training and inference-heavy workloads.

ML Engineer — GPU platform (Austin): scheduling, observability, and customer-facing performance tooling.

What you will do

Deliver production-grade GPU cloud ML platform with measurable quality, latency, and cost targets.
Build offline evals and online monitoring before scaling to full traffic.
Collaborate with product, design, legal, or domain experts on safe rollout.
Write clear RFCs, runbooks, and postmortems for cross-team learning.

What we look for

Multiple shipped ML/LLM features—not coursework-only portfolios.
Strong Python, SQL, and experimentation discipline.
Clear communication for remote or hybrid stakeholders.
Work authorization for the listed country.

Skills we're hiring for

Core capabilities—LLMs, automation, analytics, product, or domain expertise.

PythonKubernetesAWS

Tools & stack

Platforms and products mentioned for this role (APIs, IDEs, vector DBs, CRMs, design tools, etc.).

Docker

You're a strong fit if…

You've shipped real work with AI—not only experimented in isolation.
Several listed skills or tools show up in your recent projects.
You can explain trade-offs (quality, cost, latency, safety) in plain language.

Apply

Candidate accounts only: add your headline and bio on , and optionally a CV link or PDF so recruiters see your full résumé. Then write a short pitch below—applications use your login email.

What the employer asked for

GPU scheduling or inference perf project.

Register, add a headline and bio, then you can attach a CV link or PDF on My account before you apply. That keeps applications tied to your profile and makes it easier for employers to review your background.

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