Machine Learning Engineer
San Francisco /
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 a machine learning engineer at Wispr, your work closely with neuroscientists and machine learning engineers to design the research platform to process surface electromyography (EMG) and other neural signals. You will be developing cloud infrastructure for streaming and offline processing of data, designing visualization tools to rapidly ask and answer research questions, and developing machine learning training infrastructure for rapidly testing and deploying new models.
Come join us and make magic happen.
Core Job Responsibilities
- Collaborate with neuroscientists, machine learning experts and user researchers to unlock and perfect new capabilities to build interactions upon
- Build cloud infrastructure for streaming datasets and doing offline processing, including APIs etc for accessing the data
- Design research platform, including desktop application for interacting with streaming data and offline applications for visualizing and interpreting data
- Write clean and performant code to train ML models, focusing on throughput, stability, and ML metrics
- Build data warehousing and versioning infrastructure, ways to track ML model versions
- Setup testing, best engineering practices for the research engineering team
Required Knowledge/Skills, Education, And Experience
- MS or BS in computer science, machine learning or related engineering field
- 3+ years hands-on relevant engineering experience in data engineering or machine learning engineering
- Experience setting up data processing / ML pipelines and infrastructure, ideally for a research-based environment.
- Proficient in Python and machine learning libraries (SciKit-learn, SciPy, NumPy)
- Experience with cloud computing (AWS, Google Cloud)
- Experience with open-source and commercial products in the Data/MLOps and cloud infrastructure space. e.g. Docker, Kubernetes, AWS, GCP, Airflow.
- 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 working on data pipelines for speech recognition, audio data, generative models, or NLP
- Experience with deep learning frameworks (PyTorch, Tensorflow)
- 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.