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# Machine Learning Scientist

- **Company:** TACIT, Inc.
- **Location:** San Francisco, CA, United States
- **Salary:** $180,000.0 - $270,000.0
- **Contract:** Permanent contract
- **Skills:** Artificial Neural Networks, Program Optimization, Python (Programming Language), Machine Learning, Tensorflow, Smart Devices, Data Streaming, Speech Recognition, Pytorch, Deep Learning, Information Technology
- **Published:** August 9, 2026
- **Apply:** https://www.adzuna.com/details/5832946625

## About the Role

* 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, * 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.

## Description

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., * 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.


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