Machine Learning Scientist

Job description

About Tacit We are an early-stage, deep tech startup based in San Francisco, developing innovative hardware that rethinks human-computer interaction. We are backed by General Catalyst, Khosla Ventures, and Greylock Partners, with a founding team from Stanford, BrainGate, Oculus, and Tesla. While we can’t reveal too much just yet, our team is tackling cutting-edge engineering challenges to bring revolutionary products to life. About the role As a 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 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 biosignals, including time series, spatial, and spectral data. - Build and optimize neural network architectures. - 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, 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 fast moving startup environment, excited to build independently Preferred Qualifications: - Familiarity with human-machine interaction systems such as automatic speech recognition or neural interfaces. - 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, onsite in San Francisco (SOMA) - Company size: 30-40 people Compensation Range $180,000 - $270,000/year BENEFITS - Competitive equity package - Comprehensive medical, dental, and vision insurance - Unlimited PTO - Visa sponsorship - 4% 401k matching