Software Engineer, Inference
This is the employer's own posting, not a copy on a job board.
What we know
Is it still open?
Confirmed still open
Last checked 2d ago — checked against the employer's own applicant tracking system, which is the company answering directly.
We re-read the employer's own applicant tracking system and the posting was still there. That is the company answering directly.
How old is it?
Posted 2d ago
The date the source published, not the day we noticed it (2026-10-08). Last seen at its source 1h ago.
We have tracked this listing since 8 Oct 2026 (2 days). The employer's own board has carried it every time we have read it, most recently 1 hour ago.
Is it remote?
Remote, UK
That is the location the employer filed this posting under. Quoted as written — we do not re-word the source's own location.
Who may apply?
United Kingdom
The description states no restriction of its own. This is the source's own tag.
Pay not stated
Similar roles pay £70.6k–127.4k/yr
Middle 50% of 64 listings that do state pay — Engineering · all levels · United Kingdom · GBP/year. This employer has published no salary; this is what comparable listings we hold disclose, never converted between currencies or periods. How this is calculated.
Skills named in the ad
Recognised terms only, from a fixed vocabulary — this is what CV matching compares against.
Carried by 1 source
-
Ashby employer's own board first seen 2d ago · last seen 1h ago
The listing
You'll own how Luma's models get served — integrating new architectures into the inference engine, scaling deployments across thousands of machines, and keeping expensive GPU fleets busy while meeting internal SLOs.
This is large-scale inference systems work: scheduling, fleet management, deployment pipelines, and reliability across clusters and hardware providers. It fits a strong systems engineer comfortable with model serving and Kubernetes at scale. If you want pure modeling rather than the systems that run models, this is firmly the systems side.
What You'll Own
Ship new model architectures by integrating them into the inference engine.
Collaborate across research, engineering, and infrastructure to optimize model efficiency and deployments.
Build internal tooling to measure, profile, and track the lifetime of inference jobs and workflows.
Automate, test, and maintain inference services for maximum uptime and reliability.
Manage and optimize inference workloads across clusters and hardware providers, and scale deployments across thousands of machines.
Build scheduling systems that use expensive GPU resources optimally while meeting SLOs, and maintain CI/CD for model checkpoints and SDKs.
First 90 Days
One way the first 90 could unfold.
Days 1–30 — Immerse & Diagnose: Learn the inference stack, the fleets, and where reliability or utilization break.
Days 30–60 — Ship & Validate: Integrate a model or ship tooling/scheduling that improves uptime or GPU utilization.
Days 60–90 — Scale & Systemize: Harden deployment pipelines and scheduling across clusters and providers.
What You Bring
Strong Python and system-architecture skills.
Experience deploying models with PyTorch, Hugging Face, vLLM, SGLang, TensorRT-LLM, or similar.
Experience with queues, scheduling, traffic control, and fleet management at scale.
Experience with Linux, Docker, and Kubernetes, and with orchestration, deployment, and scheduling.
Familiarity with Redis and S3-compatible storage.
Nice to Have
Modern networking stacks including RDMA (RoCE, InfiniBand, NVLink).
High-performance large-scale ML systems (100+ GPUs).
CUDA, and FFmpeg or multimedia processing.
About Luma: Luma's mission is to build unified general intelligence that can generate, understand, and operate in the physical world. We believe multimodality is critical for intelligence — the next step beyond language models comes from vision. Luma is an equal opportunity employer.