[![Image]() Meet Jobicy Copilot — free AI autofill for job applications + remote job alerts ›](#) [All remote jobs](https://jobicy.com/jobs.md)Open role[![Bayesian Health logo](https://jobicy.com/data/server-nyc0409/galaxy/mercury/2025/08/f74593d8-221.png)](https://jobicy.com/company/bayesian-health.md)Remote opportunity at[Bayesian Health](https://jobicy.com/company/bayesian-health.md) # Staff Machine Learning Engineer Review the role, location requirements, compensation details, and application process before deciding whether this opportunity fits your next career move. [Apply for this job](#job-application)[View company](https://jobicy.com/company/bayesian-health.md)Share3 Sep 2026Published29Listing views0Application actions3 Oct 2026Apply before Opportunity details ## About this role. AI SummaryBayesian Health is seeking a Staff Machine Learning Engineer to own clinical machine-learning systems from model prototyping through production deployment and real-world performance evaluation. The role combines data science, applied ML, production Python and SQL development, and MLOps for a live healthcare AI product. Key work includes developing predictive models and labeler systems, debugging data flows across AWS services, and improving prediction timeliness and reliability. Candidates need advanced academic or equivalent practical experience, a record of shipping ML products, and hands-on familiarity with tools such as SageMaker and MLflow. Healthcare-data experience, enterprise customer awareness, and the ability to translate peer-reviewed methods into production solutions are preferred. ## Role DNA A quick view of the complexity, pace, ownership and collaboration implied by the job description. ### Job Complexity 5/5EasyHard ### Pace & Pressure 5/5RelaxedFast-paced ### Autonomy Level 5/5GuidedFull ownership ### Communication Load 4/5IndependentCollaborative AI insightThis is a senior, end-to-end ML role in a high-stakes clinical setting, requiring both advanced modeling judgment and strong production engineering ability. The engineer must independently navigate messy health data, operational reliability, and the real-world impact of model outputs. ## Salary analysis Estimated compensation compared with the broader US market for similar roles. Estimated job medianHighly competitive$225,000US market range$190k–$260k0$286k AI insightNo actual salary, base-pay range, or other role compensation is disclosed in the posting. The figures are estimated annual USD base-salary market ranges for a US Staff Machine Learning Engineer with healthcare AI, production ML, and MLOps responsibilities; equity and benefits may be additional. ## Core skills Skills and capabilities most closely associated with this opportunity. [Machine Learning](https://jobicy.com/jobs?search_keywords=Machine%20Learning.md)[Applied Machine Learning](https://jobicy.com/jobs?search_keywords=Applied%20Machine%20Learning.md)[MLOps](https://jobicy.com/jobs?search_keywords=MLOps.md)[Python](https://jobicy.com/jobs?search_keywords=Python.md)[SQL](https://jobicy.com/jobs?search_keywords=SQL.md)[AWS](https://jobicy.com/jobs?search_keywords=AWS.md)[Amazon SageMaker](https://jobicy.com/jobs?search_keywords=Amazon%20SageMaker.md)[MLflow](https://jobicy.com/jobs?search_keywords=MLflow.md)[Healthcare Data](https://jobicy.com/jobs?search_keywords=Healthcare%20Data.md)[Model Deployment](https://jobicy.com/jobs?search_keywords=Model%20Deployment.md) Sample interview questionsDescribe an ML model you owned from initial prototype through production deployment. What decisions changed as you moved into production?I would explain the clinical or business objective, baseline approach, feature and label design, and validation results. I would then cover production considerations such as data availability, latency, reproducibility, monitoring, failure handling, and how those constraints altered the final model or serving architecture. How would you investigate a patient case where a clinical prediction was delayed or appeared incorrect? I would trace the record through each stage of the pipeline: source data arrival, mapping and transformation logic, feature generation, model-scoring events, and downstream delivery. I would compare timestamps and values against expected behavior, identify whether the issue is data quality, system latency, model logic, or presentation, and implement a tested corrective action with monitoring. What practices do you use to make ML systems reproducible and safe to deploy? I use versioning for code, data definitions, features, training configurations, model artifacts, and evaluation reports. I also establish automated tests, validation gates, deployment approvals, rollback procedures, drift monitoring, and clear documentation of intended use and known limitations. How do you approach modeling with messy healthcare data? I begin by understanding the clinical workflow and the meaning, provenance, and timing of each data element. I assess missingness, coding changes, leakage risk, cohort definitions, and label reliability, then build robust preprocessing and evaluate performance across clinically meaningful patient subgroups and care settings. How would you decide whether a state-of-the-art method from a research paper is appropriate for a production clinical product? I would assess whether the method addresses a meaningful clinical and operational need, can be reproduced on our data, and provides material improvement over simpler baselines. Beyond offline metrics, I would evaluate interpretability, latency, data requirements, robustness, maintenance cost, and prospective performance before recommending production adoption. ### 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. Show more [Apply now >](https://jobicy.com/jobs/152429-staff-machine-learning-engineer-2.md) > Annual salary information is not provided for this position. 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