AI Researcher

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About Reson8 At Reson8, we're on a mission to become a global leader in accurate, customisable speech-to-text for European languages, so every conversation is captured as it was meant to be. Unlike generic tools, Reson8 understands domain-specific jargon and adapts to the unique needs of each professional environment. From medical consultations to legal depositions to restaurant operations, Reson8 handles the jargon that generic tools get wrong. Available in 24 European languages so far, Reson8 is backed by world-class VCs and is committed to building a world where everyone is understood-no matter their language, accent, or jargon. What You'll Build Train, fine-tune, and improve Reson8's proprietary speech-to-text models across 24+ European languages. You'll work hands-on with our model architectures to push accuracy on domain-specific vocabulary (medical, legal, financial) beyond what generic alternatives can do. Build and maintain a stable, reproducible experimentation platform-so the team can run training experiments with confidence, track results systematically, and iterate fast without breaking things. Design and run experiments: formulate hypotheses around model architecture, training recipes, data mixes, and domain adaptation strategies. Analyse results rigorously, document findings, and ship what works into production. Work directly with the founders in a highly technical environment: all three have engineering backgrounds from IMC and Adyen. You'll be in the room where decisions happen and have real ownership over our model roadmap. Train and run inference on Reson8's own GPU cluster in NL datacenters-owning the full loop from experiment to deployment instead of relying on opaque cloud compute. Stay close to the frontier: evaluate new architectures, training techniques, and tooling (NeMo, vLLM, TensorRT) and bring what works into our stack. Own your work end-to-end: identify research opportunities, run the experiments, build the infrastructure to support them, and validate results in production. What You Bring Strong background in machine learning, with hands-on experience training and evaluating deep learning models (ideally in speech, NLP, or sequence-to-sequence domains). Proficiency in Python and PyTorch (strongly preferred). You've actually trained models, not just fine-tuned notebooks. Experience building or contributing to experimentation infrastructure-experiment tracking, reproducible training pipelines, evaluation frameworks, or similar tooling. Comfort reading research papers and translating ideas into working code. Bonus experience with: Speech recognition architectures (CTC, RNN-T, encoder-decoder ASR, Whisper-style models). NVIDIA tooling: NeMo, TensorRT. Distributed training or multi-GPU workflows. Rust (we use it in performance-critical parts of our stack). Kubernetes or container orchestration. Language-specific challenges in European languages (morphology, code-switching, diacritics). How We Work Small, highly technical team where everyone is a shareholder and behaves like an owner. No passengers: expectation of high accountability, curiosity, and bias to action. High bar for craftsmanship and attention to detail that competitors ignore.

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