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.

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

Compensation Range: $180K - $270K