Machine Learning Scientist

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

• Competitive equity package

• Comprehensive medical, dental, and vision insurance

• Unlimited PTO

• Visa sponsorship

• 4% 401k matching