One of our early-stage, deep tech startup investments based in San Francisco is developing innovative hardware that rethinks human-computer interaction. Founding team is from Stanford, BrainGate, Oculus, and Tesla.
Job Description:
As a Multimodal Machine Learning Scientist, you will develop cutting-edge AI models to integrate and decode complex, multimodal data streams from our custom sensing hardware. You’ll play a pivotal role in advancing our silent speech technology stack by building and optimizing models for real-time applications. This position spans foundational research in deep learning, hands-on model development, and applying algorithms to scale across diverse data sources and users.
Responsibilities:
• Design and implement state-of-the-art machine learning algorithms for processing multimodal bio-signals, including time series, spatial, and spectral data.
• Build and optimize neural network architectures ranging from transformers to state space models.
• Develop and evaluate multimodal learning techniques to fuse information from multiple sensor modalities.
• Iterate rapidly on model prototypes for real-time inference on custom hardware.
• Create and maintain a robust evaluation framework for benchmarking model performance across datasets and participants.
• Collaborate closely with a diverse team, including hardware engineers, neuroscientists, and product designers, to align models with user needs.
Requirements:
• PhD in computer science, machine learning, computational neuroscience, or related fields (or equivalent industry experience).
• Expertise in deep learning frameworks (e.g., PyTorch, TensorFlow) and fluency in Python.
• Track record of publishing or deploying machine learning models in real-world systems.
• Independent work ethic, flexibility, and resourcefulness.
• Effective communication and collaboration skills.
• Comfortable in a fast moving startup environment, excited to build independently.
Preferred Qualifications:
• Familiarity with human-machine interaction systems, such as ASR, multimodal interfaces, or neural decoding.
• Hands-on experience with consumer wearables or custom hardware.
• Knowledge of low-latency inference techniques and model optimization for edge devices.
Details:
• This position is full time, on-site in San Francisco (SOMA)
• Company size: 10-20 people
Other Keywords: deep learning, speech recognition, foundation models, real-time inference, data fusion, transformers, bio-signals, applied science
About Greylock:
Greylock is an early-stage investor in hundreds of remarkable companies including Airbnb, LinkedIn, Dropbox, Workday, Cloudera, Facebook, Instagram, Roblox, Coinbase, Palo Alto Networks, among others. More can be found about us here:
About the Greylock Recruiting Team:
As full-time, salaried employees of Greylock, we provide free candidate referrals/introductions to active and upcoming investments to help them grow/succeed (as one of the many services we provide). Our recruiting team, combined, has over 125 years of in-house recruiting experience at successful startups through FAANG’s and over 30 years of VC Talent.
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