Senior / Lead Research Scientist - Germany

Inworld AI; Deutschland; Vor Ort; EUR 70.000 - 90.000; Vollzeit

Zusammenfassung

A leading research lab in AI based in Germany seeks a talented researcher to tackle complex problems and contribute to impactful projects. The ideal candidate will have a PhD in ML/NLP or relevant experience, and a strong background in practical AI research and interdisciplinary work. Join a flat-structured environment that values innovation and fast iterations. Fluent English is required for effective collaboration with the US teams.

Qualifikationen

• Experience with evaluation and benchmarks in machine learning.
• Background in multimodal models or AI-related research.
• A strong history of practical AI projects and open-source contributions.

Aufgaben

• Develop and execute research experiments from conception to delivery.
• Collaborate with US-based teams to address complex problems.
• Contribute to the advancement of the field through publication and experimentation.

Kenntnisse

Foundation models

Quality measurement

Published research

Full-stack research ownership

Public work in AI

Ausbildung

PhD in ML/NLP or equivalent experience

Jobbeschreibung

About Inworld

Inworld is a product-oriented research lab of top AI researchers and engineers, developing best-in-class realtime multimodal models and the only realtime orchestration platform optimized for thousands of queries per second.

We’ve raised more than $125M from Lightspeed, Section 32, Kleiner Perkins, Microsoft’s M12 venture fund, Founders Fund, Meta and Stanford, among others. Our technology has powered experiences from companies such as NVIDIA, Microsoft Xbox, Niantic, Logitech Streamlabs, Wishroll, Little Umbrella and Bible Chat. We’ve also been recognized by CB Insights as one of the 100 most promising AI companies globally and have been 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

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

Who Thrives Here

• Pathfinders: You don’t need a roadmap to start walking; you’re comfortable picking a direction and building the map as you go.

• Full-Cycle Researchers: You believe research isn't finished until it’s shipped. You have a bias for impact over purely academic output.

• First-Principles Engineers: 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.

• Mission Owners: 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.

We don't need a cover letter. A link to something you've built tells us more.

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