• 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.

• Design, build, and release AI products/features that solve real user problems • Design and implement streaming/batch data pipelines to support training and inference • Write production-ready code and move it to production with the help of cloud services and CI/CD techniques • Continuously monitor and improve the quality and performance of the systems • Learn by doing — while owning real problems and demonstrating end-to-end ownership throughout the system • Build resilient, performant pipelines and services in production • Own system architecture, performance, observability, and scalability • Collaborate across teams to turn ideas into impactful products • Mentor engineers and help set technical direction