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
Compensation Range: $180K - $270K