Principal Research Scientist | Speech & Voice Foundation Models

Intelix.AI; Berlin; On-site; EUR 237,000 - 439,000; Full time

Job summary

Intelix.AI is seeking a Principal Research Scientist for Speech & Voice Foundation Models in Germany/Berlin. The role owns full-stack research, from framing questions to shipping results, with production deployment in a real-time, multilingual setting.

The candidate should have a PhD in ML or NLP (or equivalent) and a proven ability to publish or open-source work. The position is permanent and full-time, with a competitive USD base salary and equity/bonus options.

Qualifications

• PhD in ML or NLP, or equivalent practical experience.

Responsibilities

• Train and build foundation models for production use.
• Develop voice and speech models across TTS, STT, and speech-to-speech.
• Design evaluation pipelines and benchmarks to prove model quality.
• Lead research projects from framing questions to shipping measurable results.
• Publish and contribute to open-source or papers to demonstrate impact.

Job description

Principal Research Scientist | Speech & Voice Foundation Models

Germany of DACH region | UK and parts of Europe will be considered

Text-to-speech, voice cloning, speech synthesis, real-time conversational voice.

$270,000–$500,000 base, plus bonus, equity and benefits (US).

Permanent, full-time.

The role

Build the models that are the product. This is full-stack research ownership: you frame the question, run the experiments and ship the result. Research isn't finished until it's in production and measurable.

Responsibilities

• Train foundation models: pre-training, reinforcement learning, reward modelling, post-training, new architectures, scaling.
• Build and improve voice and speech models across TTS, STT and speech-to-speech.
• Design the evaluation that proves the work: benchmarks, eval loops, LLM-as-judge, failure analysis. Evaluation is treated as a research product in its own right, not a pre-launch checkbox.
• Work on frontier problems adjacent to the roadmap: multimodal, agents and tool use, test-time compute.
• Take models into production alongside the serving engineering team, inside a real-time latency budget and across a large multilingual footprint.
• Hands‑on foundation-model training: pre-training, RL, reward modelling, post‑training, scaling. Fine‑tuning or building on top of someone else's model is a different discipline and is not what this role is.
• Real voice or speech research: TTS, STT or speech-to-speech. Speech-to-speech is the strongest signal; TTS and ASR both count. Text‑only research does not transfer.
• Evidence we can point at: papers, shipped models, open-source contributions, or systems running in production.
• Evaluation depth: benchmarks, eval loops, quality measurement, failure analysis.
• Publications at ICML, ICLR, NeurIPS, EMNLP, ACL, AAAI, Interspeech or ICASSP.
• PhD in ML or NLP, or equivalent practical experience you can point to.
• Public work: side projects, open-source, technical write-ups.

Seniority level

Mid-Senior level

Artificial Intelligence · Software Development

Employment type

Full-time