MVP Ventures
Talent Network.

Machine Learning Scientist

Software Engineering
San Francisco, CA, USA
Posted on Wednesday, February 23, 2022
Wispr is building a more natural way to interact with technology with neural interfaces. We're building a team of world-class scientists, engineers, and product designers to make that a reality.
So far, we've raised 14.6M from top-tier VCs like NEA and 8VC. Our angels and advisors include Chester Chipperfield (product lead for the first Apple Watch), Ben Jones (COO, CTRL-Labs), Dave Gilboa (CEO, Warby Parker), and Jose Carmena (Berkeley professor; co-CEO iota). Our founders are Stanford alums and have previously sold a company and run a team at a deep tech startup with over 100M in funding.
We're hiring in SF Bay Area / Remote.
As part of our ML research team at Wispr, you will work closely with neuroscientists and ML engineers to design algorithms to decode EMG and other neural signals. You will be designing experiments for large-scale data collection, investigating & understanding deep learning architectures, and inventing novel techniques for processing neural signals. These might include unsupervised pre-training, semi-supervised learning, synthetic data, and multi-modal feature extraction, among others.
Come join us and let's make magic happen.

Core Job Responsibilities

  • Design and improve deep learning architectures for neural signals from novel hardware
  • Scale up data collection and run experiments on large datasets
  • Setup best practices for ML research and set company-wide benchmarks to test models
  • Explore and invent novel ways to decode neural signals with low latency
  • Collaborate with neuroscientists, ML engineers, and user researchers to unlock powerful and new capabilities to build interactions upon

Required Knowledge/Skills, Education, And Experience

  • PhD in computer science, machine learning, or related engineering field
  • 3+ years of hands-on research experience in machine learning (in academia or industry)
  • Strong research/publication track record
  • Experience in Python and deep learning libraries (Pytorch, Numpy, Pandas, etc)
  • Experience with cloud computing (eg. AWS)
  • Experience working collaboratively with teams: we believe in working collectively towards a common goal

Nice to have Knowledge/Skills, Education, And Experience

  • Experience working with bio-signals (EMG, EEG, EKG, etc), medical devices, or real-world noisy data (eg. self-driving cars)
  • Experience with novel methods for increasing model performance - including leveraging synthetic data, unsupervised pre-training, etc.
  • Experience working on ASR systems, NLP
  • Research in a deep tech company or special projects group
Why Wispr?
• Design the next generation of personal computing in a creative and innovative environment.
• Headquarters in an open, green, and bright office in South San Francisco with water views
• Work closely with a world-class team.
• Flexible work arrangements to support you in working in the way that you work best.
For full-time employees:
• Generous health, dental, and vision coverage
• Generous parental leave, unlimited PTO (we encourage taking days off!)
• 401k match
• Commuter benefits
• Relocation assistance
• Total compensation for this position may also include stock options and other potential future incentives
At Wispr, diversity is important to us.
At Wispr, we believe that true innovation starts from people from diverse backgrounds coming together, bridging ideas, and collaborating. Wispr is proud to be an Equal Employment Opportunity employer and is committed to providing an environment of mutual respect where employment opportunities are available to all applicants and teammates without regard to race, color, religion, sex, pregnancy (including childbirth, lactation and related medical conditions), national origin, age, physical and mental disability, marital status, sexual orientation, gender identity, gender expression, genetic information (including characteristics and testing), military and veteran status, and any other characteristic protected by applicable law.