Job description

We are looking for a talented and experienced applied ML researcher who can both research new capabilities and develop frontier models using cutting-edge methods. In this role, you will take on high-risk, high-reward challenges - researching, designing, and improving state-of-the-art deep learning models trained on unique data, implementing novel machine learning algorithms, and developing solutions to problems that don't have obvious answers. Your scope will encompass various ML subfields and modalities, combining ideas from different fields to solve unique challenges and deliver solutions adopted broadly across teams. BS in Computer Science, Electrical Engineering, or a related field - or equivalent practical experience. Experience in academic or industry research. Experience working with Pytorch. MS or PhD in Computer Science, Electrical Engineering, or a related field with 10+ years of relevant industry experience. Strong foundation in deep learning theory and hands-on experience training large-scale models. Deep knowledge in one or more of: self-supervised learning, synthetic data generation, large language model training, or automatic speech recognition. Experience working with multimodal data (e.g., images, audio, time-series, or sensor fusion). Strong analytical and problem-solving skills; ability to translate research ideas into production-quality code. Resilience and persistence - comfort with experimentation cycles where many attempts fail before one succeeds. Publication record at top-tier ML venues (NeurIPS, ICML, ICLR, Interspeech, ICASSP, etc.). Multidisciplinary background spanning fields such as neuroscience, physics, signal processing, or mathematics. Experience collaborating with distributed teams across time zones.

Requirements

Must-haves

BS in Computer Science, Electrical Engineering, or a related field - or equivalent practical experience.
Experience in academic or industry research.
Experience working with Pytorch.
MS or PhD in Computer Science, Electrical Engineering, or a related field with 10+ years of relevant industry experience.
Strong foundation in deep learning theory and hands-on experience training large-scale models.
Deep knowledge in one or more of: self-supervised learning, synthetic data generation, large language model training, or automatic speech recognition.
Experience working with multimodal data (e.g., images, audio, time-series, or sensor fusion).
Strong analytical and problem-solving skills; ability to translate research ideas into production-quality code.
Resilience and persistence - comfort with experimentation cycles where many attempts fail before one succeeds.
Publication record at top-tier ML venues (NeurIPS, ICML, ICLR, Interspeech, ICASSP, etc.).
Multidisciplinary background spanning fields such as neuroscience, physics, signal processing, or mathematics. Experience collaborating with distributed teams across time zones.

Nice-to-haves

None flagged.

Application prep

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Keywords to mirror

mlmachine learning

Platform: apple
Effort: medium

Benefits

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Vision