ML Scientist
Research-oriented Machine Learning Scientist developing multimodal ML models (NLP, Speech, Computer Vision) for lifelike voice agents. Requires published papers in large-scale deep learning and familiarity with SOTA AI.
Research Scientist advancing generative audio models (diffusion/flow matching) for music, focusing on vocal synthesis, post-training alignment (DPO/RLHF), or audio editing. Requires PhD, top publications, and PyTorch expertise to turn research into artist-first Spotify products.
Conduct groundbreaking research in generative audio using diffusion or flow matching models, with a focus on one or more of the following areas:
Vocal Synthesis — Research in vocal and speech synthesis, along with related areas such as ML-based audio processing and signal processing.
Post-Training — Research in post-training techniques for music generation, including preference alignment methods (such as DPO, RLHF, or KTO), reward model design and training, and reinforcement learning to improve output quality, controllability, and human preference adherence.
Editing — Research in iterative music generation and audio editing, including capabilities such as stem replacement, instrumentation change, mood changes (while preserving content), tempo changes, and structure changes.
You will also:
Research-oriented Machine Learning Scientist developing multimodal ML models (NLP, Speech, Computer Vision) for lifelike voice agents. Requires published papers in large-scale deep learning and familiarity with SOTA AI.
Conduct original research on LLM evaluation, routing optimization, and model behavior using billions of real-world generations. Design novel benchmarks, run large-scale empirical studies, and develop statistical foundations for intelligent routing. Requires MS/PhD, publication track record, deep stats/ML expertise, and Python/SQL skills.
Build high-quality, domain-specific benchmarks and infrastructure to rigorously evaluate frontier AI agents on realistic workflows. Requires strong Python/Docker/Linux skills, experience with evals or benchmarks, and a deep understanding of what makes a benchmark reliable and useful.
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