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About Lyra Health
Lyra is transforming mental health care through technology with a human touch to help people feel emotionally healthy at work and at home. We work with industry leaders, such as Morgan Stanley, Uber, Amgen, and other Fortune 500 companies, to improve access to effective, high-quality mental health care for their employees and their families. With our innovative digital care platform and global provider network, nearly 20 million people can receive the best care and feel better, faster. Founded by David Ebersman, former CFO of Facebook and Genentech, Lyra has raised more than $900 million.
We are looking for an experienced Machine Learning Engineer who cares about impact, ownership, cross-functional projects, and mentorship.
This role can be carried out from our Burlingame, CA headquarters, hybrid, or fully remote/virtually. Please note - remote candidates must be physically located within the United States.
Responsibilities
- Be part of a team of innovative ML engineers working on core pieces of our ML and AI models
- Train and evaluate machine learning models for search, recommendation, and clinical decision support
- Write production code to integrate ML models into our serving infrastructure
- Create data systems to collect, clean, label, and store data used for features
- Work with product and clinical on requirements and solutions for ML and AI solutions
- Work alongside data scientists to analyze online experiments and incorporate actionable insights into Lyra’s modeling approaches.
Qualifications
- 4+ years of industry experience
- Expertise in machine learning
- Ability to write high-quality production code in Python
- Experience delivering production-level machine learning models in an online setting
- Strong communication skills and ability to generate consensus and buy-in within the team
- A track record of making high return-on-investment decisions in fast-paced environments
Preferred Qualifications
- Experience using Kubernetes
- Experience with a JVM coding language (especially Kotlin)
- Knowledge of Bayesian statistics or causal inference
- Experience delivering production LLM-based applications a plus
- Experience working with highly sensitive data in a healthcare environment
Protect yourself from recruitment fraud
Lyra Health does not use text or chat applications to conduct interviews or make job offers. We have a thoughtful and interactive interview process that primarily includes video-based interviews with our hiring teams. Communications will always be conducted from lyrahealth(dot)com or lyraclinical(dot)com domain names. Please disregard any other communications that do not include these domain names but appear to be from Lyra Recruiting.
"We are an Equal Opportunity Employer. We do not discriminate on the basis of race, color, religion, sex (including pregnancy), national origin, age, disability, genetic information or any other category protected by law.
By applying for this position, you acknowledge that your personal information will be processed as per the Lyra Health Workforce Privacy Notice. Through this application, to the extent permitted by law, we will collect personal information from you including, but not limited to, your name, email address, gender identity, employment information, and phone number for the purposes of recruiting and assessing suitability, aptitude, skills, qualifications, and interests for employment with Lyra. We may also collect information about your race, ethnicity, and sexual orientation, which is considered sensitive personal information under the California Privacy Rights Act (CPRA) and special category data under the UK and EU GDPR. Providing this information is optional and completely voluntary, and if you provide it you consent to Lyra processing it for the purposes as described at the point of collection, for example for diversity and inclusion initiatives. If you are a California resident and would like to limit how we use this information, please use the Limit the Use of My Sensitive Personal Information form. This information will only be retained for as long as needed to fulfill the purposes for which it was collected, as described above. Please note that Lyra does not “sell” or “share” personal information as defined by the CPRA. Outside of the United States, for example in the EU, Switzerland and the UK, you may have the right to request access to, or a copy of, your personal information, including in a portable format; request that we delete your information from our systems; object to or restrict processing of your information; or correct inaccurate or outdated personal information in our systems. These rights may be subject to legal limitations. To exercise your data privacy rights outside of the United States, please contact [email protected]. For more information about how we use and retain your information, please see our Workforce Privacy Notice."
What you need to know about the Austin Tech Scene
Austin has a diverse and thriving tech ecosystem thanks to home-grown companies like Dell and major campuses for IBM, AMD and Apple. The state’s flagship university, the University of Texas at Austin, is known for its engineering school, and the city is known for its annual South by Southwest tech and media conference. Austin’s tech scene spans many verticals, but it’s particularly known for hardware, including semiconductors, as well as AI, biotechnology and cloud computing. And its food and music scene, low taxes and favorable climate has made the city a destination for tech workers from across the country.
Key Facts About Austin Tech
- Number of Tech Workers: 180,500; 13.7% of overall workforce (2024 CompTIA survey)
- Major Tech Employers: Dell, IBM, AMD, Apple, Alphabet
- Key Industries: Artificial intelligence, hardware, cloud computing, software, healthtech
- Funding Landscape: $4.5 billion in VC funding in 2024 (Pitchbook)
- Notable Investors: Live Oak Ventures, Austin Ventures, Hinge Capital, Gigafund, KdT Ventures, Next Coast Ventures, Silverton Partners
- Research Centers and Universities: University of Texas, Southwestern University, Texas State University, Center for Complex Quantum Systems, Oden Institute for Computational Engineering and Sciences, Texas Advanced Computing Center
