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Phd Machine Learning Startup Jobs in California (NOW HIRING)

Tacit

Machine Learning Scientist

San Francisco, CA · On-site

$180K - $270K/yr

About Tacit We are an early-stage, deep tech startup based in San Francisco, developing innovative ... PhD in computer science, machine learning, computational neuroscience, or related fields (or ...

Poesis

Machine Learning Engineer

San Francisco, CA · On-site

$200K - $280K/yr

Prior experience at a frontier AI lab, agentic startup, leading hedge fund, big tech company, or ... Skill leveraging Claude Code, Codex, or other coding agents * BS/MS/PhD in Computer Science or a ...

Spotter

Machine Learning Scientist

Culver City, CA · Remote

You thrive in a fast-paced startup environment and are motivated by building models that don't just ... Who You Are * Master's degree or PhD in Computer Science, Statistics, Applied Mathematics ...

Pattern AI

Machine Learning Engineer

San Mateo, CA · On-site +1

Company Description PatternAI is an automated machine learning platform that reveals critical ... Additional Information About PatternAI PatternAI is an early stage startup that is growing rapidly ...

Spotter

Machine Learning Scientist

Culver City, CA · On-site

You thrive in a fast-paced startup environment and are motivated by building models that don't just ... Who You Are * Master's degree or PhD in Computer Science, Statistics, Applied Mathematics ...

Pattern AI

Machine Learning Engineer

San Mateo, CA

Company Description PatternAI is an automated machine learning platform that reveals critical ... Additional Information About PatternAI PatternAI is an early stage startup that is growing rapidly ...

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Phd Machine Learning Startup information

What are some common challenges faced by PhD-level professionals working in machine learning startups?

PhD-level professionals in machine learning startups often encounter challenges such as balancing research innovation with the need for rapid product development. Unlike academia, startups prioritize practical solutions that fit tight deadlines and resource constraints. Team members typically wear multiple hats and collaborate closely with engineers, product managers, and business stakeholders, requiring strong communication skills and adaptability. Additionally, translating cutting-edge research into scalable, real-world applications can be both intellectually rewarding and demanding.

What do PhD holders in Machine Learning do at startups?

PhD holders in Machine Learning at startups typically lead research and development efforts to create innovative algorithms and models that solve real-world problems. They often work on designing and implementing advanced machine learning solutions, analyzing large datasets, and collaborating with product and engineering teams to bring research ideas to production. Their expertise helps startups stay competitive by driving technological advancements and fostering a culture of innovation.

What are the key skills and qualifications needed to thrive as a PhD-level Machine Learning professional in a startup environment, and why are they important?

To excel as a PhD-level Machine Learning professional at a startup, you need advanced expertise in machine learning algorithms, statistical modeling, and a doctoral degree in a related field. Experience with Python, TensorFlow, PyTorch, and version control systems, along with a strong publication record, is typically expected. Initiative, adaptability, and excellent problem-solving and communication abilities are crucial soft skills in the fast-paced startup setting. These competencies enable rapid innovation, effective team collaboration, and successful deployment of machine learning solutions under resource constraints.
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Phd Machine Learning Startup jobs near you
Machine Learning Scientist

Machine Learning Scientist

Tacit

San Francisco, CA • On-site

$180K - $270K/yr

Full-time

Medical, Dental, Vision, Retirement, PTO

Posted 3 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-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

Apply