Applied Machine Learning Researcher (Speech/NLP)

San Francisco /
Engineering /
Full Time
Wispr is building the future of human-computer interfaces controlled by the peripheral nervous system. We're building a team of world class scientists, engineers, and product designers to make that a reality. We're funded by top tier Silicon Valley investors (NEA and 8VC) and backed by angels including Ben Jones (COO, CTRL-Labs), Jose Carmena (Berkeley professor; co-CEO iota), David Gilboa (CEO, Warby Parker), and Will Ahmed (CEO, Whoop). 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 / DC Area / Remote.

As an applied ML researcher at Wispr, your work closely with neuroscientists and machine learning engineers to design algorithms to decode surface electromyography (EMG) and other neural signals to speech. You will be responsible for designing experiments for scaled data collection, investigating deep learning architectures for speech processing, and inventing novel techniques for extracting signal from EMG (unsupervised pretraining, semi-supervised learning, synthetic data, multi-modal feature extraction, among others).

Come join us and make magic happen.

Core Job Responsibilities

    • Collaborate with neuroscientists, machine learning engineers, and software engineers to build speech decoding algorithms from neural signals
    • Determine best ways to scale data collection and leverage large datasets
    • Design and iterate on deep learning architectures for speech recognition on novel sensors, trade off between different designs for transcription models
    • Invent and adapt novel techniques for extracting signal from EMG (unsupervised pretraining, semi-supervised learning, synthetic data, multi-modal feature extraction, among others).
    • Setup best practices for ML research model versioning

Required Knowledge/Skills, Education, And Experience

    • PhD in computer science, machine learning or related engineering field, or equivalent research experience
    • 3+ years hands-on relevant research experience in speech processing (ASR) or natural language processing
    • 2+ years industry experience building speech recognition systems from scratch, including exposure to best practices for model and dataset versioning
    • Strong research/publication track record
    • Experience in Python and machine learning and deep learning libraries (Pytorch, NumPy, Pandas, etc)
    • Experience with cloud computing (AWS, Google Cloud)
    • Organized, self-directed, efficient and able to manage priorities and expectations
    • 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
    • Experience with novel methods for increasing performance of speech recognition systems, including leveraging synthetic data, unsupervised pretraining, etc.


Why Wispr?
- Develop cutting edge technologies in a creative and innovative environment.
- Imagine new opportunities in areas that matter and will impact the world you live in.
- Headquarters in an open, green, and bright office in South San Francisco - with a wide view of the bay and proximity to the Caltrain.
- Be a part of a high performing team of the world’s best innovators and executors.
- Receive excellent medical, dental and vision benefits, equity ownership in Wispr and competitive salaries.
- Flexible work arrangements to support you in working in the way that you work best.
- Grow your career and do the best work of your life.

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.