Lead research and evaluation of clinical AI systems, providing technical guidance to regulatory affairs and driving reusable evaluation assets.
Compensation
The annual base pay for this position ranges from $ 124 713,60 - 187 070,40 USD Annual. Our positions offer eligibility for various incentives—an opportunity to receive short-term incentive bonuses, equity-based awards for salaried roles and commissions for sales roles.
Responsibilities
Design and run evaluations of LLM-based and agentic clinical systems
Build reusable evaluation assets such as test sets, scoring rubrics, annotation protocols
Study AI output impact on clinical judgment through expert-agreement and user studies
Investigate failures using traces, logs, qualitative review, and quantitative analysis
Publish and communicate findings internally and externally
Requirements
PhD in Machine Learning, Computer Science, NLP, Information Science, Biomedical Informatics, Statistics, or a related quantitative discipline
Develop machine‑learning and AI solutions to improve safety, reliability, and operational efficiency of Microsoft 365 services, focusing on scenario health gating.
Compensation
Applied Sciences IC4 - The typical base pay range for this role across the U.S. is USD $119,800 - $234,700 per year. There is a different range applicable to specific work locations, within the San Francisco Bay area and New York City metropolitan area, and the base pay range for this role in those locations is USD $160,200 - $261,000 per year.
Responsibilities
Scientific problem formulation and algorithm development
Translate service-health and change-safety problems into scientific objectives, hypotheses, datasets, baselines, and success metrics
Develop and evaluate machine‑learning, statistical, causal, time‑series, or agent‑based methods for regression detection, risk estimation, failure attribution, and gating decisions
Compare technical alternatives and explain tradeoffs involving accuracy, interpretability, latency, scale, cost, reliability, and customer risk
Address noisy, sparse, delayed, imbalanced, and distribution‑shifted production data
Requirements
Bachelor's Degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field
Master's Degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field
Doctorate in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field
Senior Scientist, Clinical AI Research & Evaluation (Regulatory & Responsible AI)
Lead AI research and evaluation for clinical AI systems, providing technical guidance to regulatory affairs and building reusable evaluation assets.
Compensation
The annual base pay for this position ranges from $ 124 713,60 - 187 070,40 USD Annual. Our positions offer eligibility for various incentives—an opportunity to receive short-term incentive bonuses, equity-based awards for salaried roles and commissions for sales roles.
Responsibilities
Design and run evaluations of LLM-based and agentic clinical systems
Build reusable evaluation assets
Study AI output impact on clinical judgment
Investigate failures and translate findings into improvements
Publish and communicate findings
Requirements
PhD in Machine Learning, Computer Science, NLP, Information Science, Biomedical Informatics, Statistics, or a related quantitative discipline
Lead the development and automation of biophysical and biochemical assays to support Design‑Make‑Test‑Learn workflows in discovery.
Compensation
The anticipated base pay range for this position is:
€63 700,00 - €109 480,00
Responsibilities
Design, develop, and execute SPR and biochemical assays to support hit validation and lead optimization
Generate and interpret kinetic, affinity, thermodynamic, and biochemistry data to guide SAR and critical project decisions
Deliver high‑quality biophysical and biochemistry data that accelerates DMTL cycles and enables rapid, data‑driven discovery decisions
Develop robust and scalable experimental workflows that expand throughput and reproducibility across the portfolio
Drive the development and implementation of automated biophysical and biochemical lab design and workflows that increase experimental scale, consistency, and scientific impact
Requirements
Ph.D. in Biophysics, Biochemistry, Structural Biology, or a related scientific discipline
