Staff / Senior Staff Machine Learning Engineer, Inference - Switzerland

Inworld, Inc.; Schweiz; Remote; CHF 120.000 - 180.000; Vollzeit

Zusammenfassung

Inworld, Inc. is seeking engineers to push the frontier of real-time, agentic AI systems at scale.

You’ll optimize serving frameworks, accelerate models with CUDA and Python, and own full production delivery from research to deployment, working across Kubernetes and multi-GPU setups.

Qualifikationen

• Deep understanding of modern serving frameworks and techniques such as vLLM or TRT-LLM.
• Hands-on experience with quantization, distillation, caching strategies, continuous batching, paged attention, and speculative decoding.
• Proficiency in C++, CUDA, Rust, or highly optimized Python with profiling to maximize GPU performance.
• Experience with Kubernetes, Ray, custom load balancing, multi-GPU/multi-node inference, and handling thousands of concurrent connections.
• Non-trivial systems programming projects or open-source contributions to major inference engines.
• PhD or equivalent practical experience in backend or ML systems.
• Professional fluency in English, written and spoken.

Aufgaben

• Take models from research to containerized, optimized serving in production.
• Ensure reliability and performance of deployed AI systems at scale.
• Collaborate with US-based leadership and engineering teams.

Kenntnisse

C++

CUDA

Rust

Python performance

vLLM / TRT-LLM knowledge

Quantization / distillation

Profiling / optimization

Distributed systems

Kubernetes

Ray

Multi-GPU / multi-node inference

Open-source contributions

Ausbildung

PhD in CS, Physics, Math or equivalent

Tools

CUDA

Kubernetes

Ray

vLLM

TRT-LLM

Jobbeschreibung

Introducing Inworld Inference, built for consumer AI.

Nearly 8× faster time to first token than most other providers.

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.
• Professional fluency in English (written and spoken) is required, as you will be collaborating daily with our US-based leadership and engineering teams.

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.

Location & Employment

Location: remote within Switzerland

Hiring model: Employment via Employer of Record (EOR)

Candidates must already have the legal right to work in Switzerland, 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.