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Applied Scientist Machine Learning Scientist Jobs

Machine Learning Scientist

San Francisco, CA · On-site

$180K - $270K/yr

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 ...

Machine Learning Scientist

San Mateo, CA · On-site

$150K - $200K/yr

Machine Learning Scientist Department: DS/ML (Data Science/Machine Learning) Employment Type: Full Time Location: San Mateo, CA Description The role: We are seeking a creative, ambitious Machine ...

$150K - $200K/yr

Machine Learning Scientist Department: DS/ML (Data Science/Machine Learning) Employment Type: Full Time Location: San Mateo, CA Description The role: We are seeking a creative, ambitious Machine ...

Machine Learning Scientist

San Francisco, CA · On-site

$180K - $270K/yr

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 ...

Principal Applied Scientist

Chicago, IL · On-site

$214K - $350K/yr

Mentors data scientists, machine learning engineers, researchers, and technical leaders. Minimum Requirements: * Requires an MS or PhD in Computer Science, Machine Learning, Statistics, Applied ...

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Applied Scientist Machine Learning Scientist information

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Infographic showing various Applied Scientist Machine Learning Scientist job openings in the United States as of August 2026, with employment types broken down into 1% As Needed, 75% Full Time, 23% Part Time, and 1% Contract. Highlights an 84% Physical, 2% Hybrid, and 14% Remote job distribution.

Machine Learning Scientist

San Francisco, CA • On-site

$180K - $270K/yr

Other

Medical, Dental, Vision, Retirement, PTO

Re-posted 19 days ago


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.

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‑?? 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

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