Staff / Senior Staff Machine Learning Engineer, Inference - UK

Inworld AI; United States; Remote; USD 188,000 - 269,000; Full time

Benefits offered by this job

Equity

Benefits

Job summary

Inworld is seeking engineers to advance real-time AI inference for consumer apps. You will contribute to scalable serving stacks, optimize latency and throughput, and own end-to-end model deployment from research to production.

Ideal candidates have PhDs or equivalent hands-on expertise in backend/ML systems, with experience in Kubernetes, Ray, CUDA and related tools. Relocation support may be available for global candidates.

Qualifications

• Experience delivering reliable, scalable inference systems.
• Knowledge of modern serving frameworks and acceleration techniques.
• Proven ability to take models from research to production.
• PhD or comparable practical experience in backend/ML systems.

Responsibilities

• Own deployment of model serving stacks from research to production.
• Optimize latency, throughput and reliability for real-time workloads.
• Collaborate on distributed, multi-GPU/multi-node inference platforms.

Skills

Inference optimization

Model acceleration

High-performance systems

Distributed systems & scaling

Open-source contributions

Full-cycle ownership

Backend systems

Education

PhD in CS, Physics, Math or equivalent

Tools

Kubernetes

Ray

vLLM

CUDA

Job description

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.

• 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.

• You believe engineering isn’t finished until it’s shipped and stable. You have a bias for impact over purely theoretical optimizations.

• 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.

• 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

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.

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 and 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.

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 to support the separate work authorization and the tim‑ some …....

Inworld Jobs Privacy