Staff / Senior Staff Machine Learning Engineer, Inference - UK
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Before you apply
Salary£140k – £200k/mo
LocationUK
Work moderemote · United Kingdom
Typefull-time
SeniorityLead
DepartmentML Engineering
Company size201–500 people
PostedApr 7, 2026 · 7mo ago
Verified live16h ago
May no longer be actively hiring
Posted by the company on Apr 7, 202671% of this company's open roles have been listed for 90+ days
At a glanceSummarised by Seekless from the posting.
PhD in CS, Physics, Math, or equivalent practical experience building backend or ML systems
Inference Optimization - Deep understanding of modern serving frameworks and techniques like vLLM or TRT-LLM
Model Acceleration - Hands-on experience with quantization, distillation, caching strategies, continuous batching, paged attention, and speculative decoding
High-Performance Systems - Proficiency in C++, CUDA, Rust, or highly optimized Python
Distributed Systems & Scaling - Experience with Kubernetes, Ray, custom load balancing, multi-GPU/multi-node inference, and reliably handling thousands of concurrent connections
Public work - Non-trivial systems programming projects, open-source contributions to major inference engines, or deep-dive technical write-ups
Full-cycle ownership - Take a model from research to containerization, optimize its serving, and ensure reliable production deployment
Legal right to work in the United Kingdom; visa sponsorship not available
Skills
vLLM
TRT-LLM
quantization
distillation
continuous batching
paged attention
speculative decoding
C++
CUDA
Rust
Python
Kubernetes
Ray
NVIDIA GPUs
Benefits
Equity
About Inworld
Inworld is a research lab and inference provider focused on realtime AI for consumer-facing applications. We build first-party speech models, serve LLMs, and run the inference behind modular APIs designed for high-volume, realtime workloads.
Hundreds of millions of users interact with Inworld powered apps every day and we serve over 10 trillion LLM tokens per month. Our models and infrastructure support consumer applications across companions, healthcare, fitness, education, media, and more. Our work spans model research, realtime inference, large-scale serving infrastructure, and the APIs developers use to bring these capabilities into production.
We’ve raised more than $125M from Lightspeed Venture Partners, Section 32, Kleiner Perkins, Microsoft’s M12 venture fund, Founders Fund, Meta, Stanford, and others. Our technology has powered experiences from companies including NVIDIA, Microsoft Xbox, Niantic, Logitech Streamlabs, Wishroll, Little Umbrella, and Bible Chat. Inworld has also been recognized by CB Insights as one of the 100 most promising AI companies globally and named one of LinkedIn’s Top 10 Startups in the USA.
Who We’re Looking For
A year ago, reliably working agentic systems and sub-second multimodal inference at scale barely existed. Nobody has a decade of experience here. So we’re not screening for a resume template — we’re looking for strong people from varied backgrounds who learn fast, thrive in ambiguity, and can show us what they’ve built, broken, and understood.
Experience We Find Useful
You don’t need all of this. But you need enough to make a case.
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Inference Optimization. Deep understanding of modern serving frameworks and techniques like vLLM or TRT-LLM.
•
Model Acceleration. Hands-on experience with quantization, distillation, caching strategies , continuous batching, paged attention, and speculative decoding.
•
High-Performance Systems. Proficiency in C++, CUDA, Rust, or highly optimized Python. You know how to profile code and squeeze every ounce of performance out of NVIDIA GPUs.
•
Distributed Systems & Scaling. Experience with Kubernetes, Ray, custom load balancing, multi-GPU/multi-node inference, and reliably handling thousands of concurrent connections.
•
Public work. Non-trivial systems programming projects, open-source contributions to major inference engines, or deep-dive technical write-ups.
•
Full-cycle ownership. You can take a model from the research team, containerize it, optimize its serving, and ensure it runs reliably in production.
•
Background. PhD in CS, Physics, Math, or equivalent practical experience building backend or ML systems.
Who Thrives Here
•
You don’t need a roadmap to start walking; you’re comfortable picking a direction and building the map as you go.
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You believe engineering isn’t finished until it’s shipped and stable. You have a bias for impact over purely theoretical optimizations.
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You don’t just ship code; you obsess over the why. You’re the first to question an architecture if you think there’s a better way to solve the core latency or throughput problem.
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You aren’t satisfied with “the PM said so.” You thrive on deep context and want to understand the fundamental logic behind every decision we make.
What Working Here Is Like
Hiring process
We hand you unclear problems and expect you to make them clear. We value engineers who say “I don’t know yet” and then design the benchmark or prototype that finds out. We treat performance, latency, and reliability as first-class product features, not a box to check before launch. Impact comes before everything else, though we support sharing work and open-source contributions that move the field forward. Your work should be visible. Flat structure, fast iterations, minimal process theater.
Benefits
The base salary range for this full-time position is £140,000 – £200,000. In addition to base pay, total compensation includes equity and benefits. Within the range, individual pay is determined by work location, level, and additional factors, including competencies, experience, and business needs. The base pay range is subject to change and may be modified in the future.
Legal
Candidates must already have the legal right to work in the United Kingdom, as visa sponsorship is not available for this role. For candidates interested in relocating to the San Francisco Bay Area in the future, full U.S. visa and relocation support may be available, subject to business needs and applicable legal and work authorization requirements.
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