Transforming behavioral health with technology
Machine Learning Engineer
Lyra is transforming mental health care using technology with a human touch. Using our proprietary platform and matching technology, Lyra helps companies and their employees find the highest quality mental health care. We work with innovative companies, such as Uber and eBay, among others, and we offer access to an elite network of mental health providers with the flexibility of in-person therapy, live video sessions, coaching programs, and digital self-care tools. Co-founded by David Ebersman, former CFO of Facebook and Genentech, Lyra has raised $83 million in funding to date from investors including Venrock, Greylock, Tenaya and Glynn Capital.
At Lyra we believe that data-driven technology and decision making is a critical part of solving the thorny, complex challenges of provider quality and accessibility in a broken system.
We are looking for an excellent machine learning engineer to join the Data and Machine Learning team, working on our ML algorithms such as search and relevance, diagnosis and treatment recommendations, provider network optimization, and predictive modeling for patient outcomes.
Lyra is for you if you:
- Want to work in a fast-paced environment with thoughtful people solving hard problems
- Have a passion for social impact and helping people when they are most vulnerable
- Like to collaborate across teams with physicians, therapists, data scientists, and product managers
We are looking for someone with:
- Excellent programming skills (Python or Java preferred)
- Expertise in ML and statistical learning
- Experience in building and deploying machine learning models in production
- Keen attention to detail and strong organizational skills
At Lyra we value inclusion and diversity and we work hard to ensure that people are treated respectfully and equitably and to create an environment where employees have the confidence to be their true selves. We are an equal opportunity employer and do not discriminate on the basis of race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status, disability status, or any other category protected by law.