• Design, construct, and manage robust data pipelines for the training, validation, and continuous retraining of Large Quantitative Models (LQMs) and agentic frameworks • Develop, implement, and rigorously test novel ML models and algorithms, defining appropriate metrics to ensure model performance aligns with high-level product objectives • Lead the effort in cleaning, transforming, and engineering features from complex and large-scale datasets to optimize LQM performance and predictive accuracy • Conduct deep analysis of model behavior, performance, and failure modes, tuning hyper-parameters and optimizing model architecture for efficiency, speed, and accuracy in a production context • Collaborate closely with AI researchers, product managers, and SWEs to translate high-level business objectives into actionable ML development and deployment roadmaps • Champion and enforce exceptional engineering standards for code quality, system efficiency, and security in a prototyping environment • Drive technical execution with high autonomy, making critical design and implementation decisions independently

• Apply and adapt machine learning and statistical modeling approaches for biomarker discovery and longitudinal disease tracking. • Build scalable, production-ready analysis pipelines that meet clinical-grade performance standards. • Drive the adoption and refinement of machine learning best practices, including model interpretability, uncertainty estimation, and reproducibility. • Design computational strategies for ultrasensitive variant calling, error suppression, and signal extraction from sequencing data. • Collaborate cross-functionally with bioinformatics and data scientists, R&D and clinical teams to explore new ML approaches and evaluate their potential impact. • Actively participate in code and design reviews, with a focus on ML model quality, reproducibility, and integration into production pipelines.

• Support data science teams in building, deploying, and operating machine learning solutions at scale • Model Deployment: Ensuring that machine learning models are deployed efficiently and reliably • Model Monitoring: Continuously monitoring the performance of models to detect issues • Automation: Automating the machine learning pipeline, including tasks like data preprocessing, model training, and evaluation • Collaboration: Working closely with data scientists, software engineers, and IT operations • Version Control and Governance: Managing version control for models and ensuring compliance with governance policies • Optimization: Identifying and implementing ways to improve the performance and scalability of ML systems

• Architect, build, and scale the end-to-end ML Ops pipeline, including training, fine-tuning, evaluation, rollout, and monitoring. • Design reliable infrastructure for model deployment, versioning, reproducibility, and orchestration across cloud and on-prem GPU clusters. • Optimize compute usage across distributed systems (Kubernetes, autoscaling, caching, GPU allocation, checkpointing workflows). • Lead the implementation of observability for ML systems (monitor drift, performance, throughput, reliability, cost). • Build automated workflows for dataset curation, labeling, feature pipelines, evaluation, and CI/CD for ML models. • Collaborate with researchers to productionize models and accelerate training/inference pipelines. • Establish ML Ops best practices, internal standards, and cross-team tooling. • Mentor engineers and influence architectural direction across the entire AI platform.