Machine Learning Scientist

Tacit

$180K — $270K *
Healthcare
Less than 5 years of experience
Job Overview by Ladders

Qualifications

  • PhD in computer science, machine learning, computational neuroscience, or equivalent industry experience.
  • Expertise in deep learning frameworks like PyTorch and TensorFlow, and fluency in Python.
  • Proven record of publishing or deploying machine learning models in real-world applications.
  • Strong independent work ethic, with flexibility and resourcefulness.
  • Excellent communication and collaboration skills.

Responsibilities

  • Design and implement advanced machine learning algorithms for multimodal biosignal processing.
  • Build and optimize neural network architectures for real-time applications.
  • Develop multimodal learning techniques to integrate data from various sensor modalities.
  • Rapidly prototype models for real-time inference on custom hardware.
  • Establish a robust evaluation framework for benchmarking model performance across diverse datasets.
  • Collaborate with a multidisciplinary team including hardware engineers and neuroscientists to tailor models to user needs.

Benefits

  • Competitive equity package
  • Comprehensive medical, dental, and vision insurance
  • Unlimited PTO
  • Visa sponsorship
  • 4% 401k matching
Full Job 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. 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

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