The posting
STAFF MACHINE LEARNING ENGINEER
IN BRIEF
- We’re an early-stage startup on a mission to make healthcare proactive by empowering physicians, nurses, and care team members with real-time data to save lives.
- Part Data Scientist (building models), part Applied Scientist (productionizing models), and part MLE (deploying, maintaining), also known as “Full Stack Data Scientist” – someone who wants to own the end-to-end effectiveness of their real-time models in a live, clinical AI product.
WHO WE ARE
Bayesian Health’s mission is to improve patient outcomes by empowering clinicians with the insights they need to make the right decision for the right patient at the point-of-care. We’re a diverse team of clinicians, engineers, machine learning experts, product designers, and performance improvement leaders committed to enabling smarter, patient-specific care delivery through unlocking the power of data.
We’re funded by top tier tech and biotech investors: Andreessen Horowitz, American Medical Association’s venture arm, Catalio Partners, and LifeForce Capital. Our company has won many awards; most recent recognitions include: Forbes AI Top 50, World Economic Forum Tech Pioneer, Time Best Inventions, BioTech AI Company of the Year.
Read more about our recent publication in Nature Medicine that associates our products with lives saved. https://www.bayesianhealth.com/sepsis-research/?hstc=251652889.1e7598f5de0ed9b513da684474e8a044.1710861879595.1710945070565.1710971833587.3&hssc=251652889.1.1710971833587&__hsfp=1890770568
WHAT YOU’LL DO
As a Staff Machine Learning Engineer, you are not satisfied with training and tuning ML models that predict clinical conditions in patients; you also want to own the effectiveness of your model in the real world. In practice, that means you aren’t afraid to get your hands dirty by writing data mapping code, debugging a specific patient case by following patient data as it moves through our AWS services, or improving the timeliness of your model’s predictions by reading and writing production-grade Python and SQL code.
RESPONSIBILITIES
- Model Prototyping: Develop and tune innovative, new ML models and labeler systems based on deep understanding of clinical use cases and state-of-the-art ML methods.
- Productionizing: The same models that you develop with production-grade python.
- Deploying: Identify strategies for improving our production ML-based systems, and write, debug, and deploying production-grade Python code to implement those strategies.
- MLOps: Build infrastructure that enables ML model development and deployment in production systems.
MINIMUM QUALIFICATIONS
- Ph.D. in a relevant field plus 3+ years relevant experience, or a relevant Master’s degree and 5+ years experience shipping ML based software products.
- Experience owning your ML models from prototyping to production.
- Experience writing production-grade Python and SQL code to implement and evaluate ML models in production systems.
- Experience using MLOps tools such as SageMaker and MLFlow.
PREFERRED QUALIFICATIONS
- Experience going 0-1 and shipping high impact AI/ML products.
- Experience building solutions within healthcare and/or familiarity working with messy health data.
- Experience working with enterprise customers, and the agility and responsiveness they require.
- Comfortable interpreting / leveraging state-of-the-art peer-reviewed methods or tools in designing your approach.
- Excitement for Bayesian’s mission and being a bar raiser so we can accelerate the pace at which we create value.
Bayesian Health provides equal employment opportunities to all employees and applicants for employment and prohibits discrimination and harassment of any type without regard to race, color, religion, age, sex, national origin, disability status, genetics, protected veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by federal, state or local laws.
This policy applies to all terms and conditions of employment, including recruiting, hiring, placement, promotion, termination, layoff, recall, transfer, leaves of absence, compensation and training.
