1

Computational Neuroscience Deep Learning Postdoc Jobs

Machine Learning Scientist

San Francisco, CA ยท On-site

$200 - $250/hr

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 ...

Machine Learning Scientist

San Francisco, CA ยท On-site

$180K - $270K/yr

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 ...

TSC's Intelligent Systems Laboratory in North Carolina is a state-of-the-art research and development research facility, dedicated to R&D in the areas of computational neuroscience, deep learning ...

TSC's Intelligent Systems Laboratory in North Carolina is a state-of-the-art research and development research facility, dedicated to R&D in the areas of computational neuroscience, deep learning ...

Showing results 41-60

Computational Neuroscience Deep Learning Postdoc information

See salary details

$18

$30

$57

How much do computational neuroscience deep learning postdoc jobs pay per hour?

As of Sep 9, 2026, the average hourly pay for computational neuroscience deep learning postdoc in the United States is $30.73, according to ZipRecruiter salary data. Most workers in this role earn between $23.56 and $38.70 per hour, depending on experience, location, and employer.

What are popular job titles related to Computational Neuroscience Deep Learning Postdoc jobs?

For Computational Neuroscience Deep Learning Postdoc jobs, the most frequently searched job titles are:

Machine Learning Scientist

San Francisco, CA โ€ข On-site

$200 - $250/hr

Other

Medical, Dental, Vision, Retirement, PTO

Re-posted 18 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 canโ€™t reveal too much just yet, our team is tackling cuttingโ€‘edge engineering challenges to bring revolutionary products to life.

As a Machine Learning Scientist, you will develop cuttingโ€‘edge AI models to integrate and decode complex, multimodal data streams from our custom sensing hardware. Youโ€™ll play a pivotal role in advancing our technology stack by building and optimizing models for realโ€‘time applications. This position spans foundational research in deep learning, handsโ€‘on model development, and applying algorithms to scale across diverse data sources and users.

Responsibilities:

  • Design and implement stateโ€‘ofโ€‘theโ€‘?? 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 realโ€‘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 realโ€‘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 humanโ€‘machine interaction systems such as automatic speech recognition or neural interfaces.

  • Handsโ€‘on experience with consumer wearables or custom hardware.

  • Knowledge of lowโ€‘latency inference techniques and model optimization for edge devices.

Details:

  • This position is full time, onsite in San Francisco (SOMA)

  • Company size: 30โ€“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

#J-18808-Ljbffr