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 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.

• Foundation models: training, new architectures, RL, reward modeling, scaling

• Evaluation: benchmarks, eval loops, quality measurement, LLM-as-judge, failure analysis

• Frontier topics: multimodal models, agents, tool use, test-time compute, world models

• Published research at ICML, ICLR, NeurIPS, EMNLP, ACL, or AAAI

• PhD in ML/NLP — or equivalent practical experience you can point to

• Public work: non-trivial AI side projects, interdisciplinary experiments, open-source contributions

• Full-stack research ownership: you frame the question, run the experiments, write the paper, ship the result

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

If you learned through building, competitions, or collaborations outside academia, that counts. We care about evidence, not credentials.

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 research isn't finished until it’s shipped. You have a bias for impact over purely academic output

• You don't just ship code; you obsess over the why. You’re the first to question an approach if you think there’s a better way to solve the core 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 researchers who say "I don't know yet" and then design the experiment that finds out. We treat evaluation as a first-class research product, not a box to check before launch. Impact comes before publications though we support sharing work that moves the field forward. Your work should be visible. Flat structure, fast iterations, minimal process theater.

Location & Employment

• Location: remote within Switzerland

• Employment type: Full-time, permanent employment

• 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.

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