• Design, build, and maintain robust AI pipelines for data processing, model training, and deployment. • Collaborate with data scientists and engineers to implement cutting-edge AI solutions. • Optimize existing AI infrastructure for performance, scalability, and reliability. • Troubleshoot and resolve issues in the AI pipeline environment. • Document and maintain best practices for AI pipeline development and deployment • Develop and implement Minimum Viable Product (MVP) code for AI initiatives. • Collaborate with other engineers to transition MVP code to production-ready systems.

Role Description As an intern you will get a front row seat in a fast growing company which will undoubtedly advance your career and give the right candidate an accelerated career path. In this role, you’ll work with our growing team of world-class engineers, statisticians, and clinical experts to develop and deploy machine learning algorithms that help automate burdensome administrative clinical practices. This is a unique opportunity to join a new engineering team with great ambition and building on modern technology with zero legacy technical debt. Last but not least: People who succeed here are empathetic teammates who are candid, kind, caring, and embody our core values and principles. We believe that diverse, inclusive teams make the most impactful work. Cohere is deeply invested in ensuring that we have a supportive, growth-oriented environment that works for everyone. What you will do: - Work on reliable and scalable production machine learning systems - Contribute to feature design, development, testing, and delivery of our machine learning models - Work cross-functionally across diverse stakeholders, including product managers, statisticians, EHR data specialists and physicians - Actively participate in development of machine learning models Qualifications - You are passionate about building quality products and have end-to-end machine learning experience, leading with the right design and development principles - Experience developing in Python, required (NLP/PyTorch experience preferred) - You have familiarity with common software development practices such as version control, unit testing, and CI/CD - You are a team player and are interested in working at a fast-paced startup environment - You are enrolled in a Bachelor’s or higher (MS/PhD) degree program in computer science, machine learning, computational linguistics, statistics, mathematics or similar field - Prior experience in healthcare and life sciences is a plus, but is not required Equal Opportunity Statement Cohere Health is an Equal Opportunity Employer. We are committed to fostering an environment of mutual respect where equal employment opportunities are available to all. To us, it’s personal.

• Partner with the Data Platform team in a two-way exchange of best practices • Adopt common patterns and build effective abstractions across different machine learning pipelines that simplify existing machine learning processes and accelerate the modelling process from the business problem’s inception to deploying a model solution into production • Develop horizontal solutions to robustly scale the team’s machine learning models and processes • Build software with Object-oriented Design Patterns and Analysis (OOA and OOD) with an eye toward reducing technical debt and maintaining services at high availability • Participate in requirements reviews, design reviews, and code reviews • Research and prototype new technologies to support the rapid growth of the business • Interact cross-functionally with a wide variety of technical teams and work closely with data and applied scientists to identify opportunities to improve on iHerb’s platform

• Own ML production reliability strategy • Define and lead the operational strategy for production ML systems, including monitoring, traceability, deployment safety, incident response, and post-deployment validation. • Set the standards ML teams use to assess model health, performance, and trustworthiness in production. • Own model traceability and governance • Ensure every production model has clear lineage (data, features, code, artifacts, validation, deployment history) and drive adoption of model registry and metadata tooling across ML teams. • Build end-to-end ML observability • Design and implement monitoring across the full ML signal path: data arrival, feature freshness, distribution stability, candidate generation, ranking behavior, model metrics, serving latency, and SLA performance. • Define production health metrics • Partner with ML, data, product, and business stakeholders to define post-deployment metrics covering model quality, system reliability, business guardrails, and degradation indicators. • Detect drift and degradation proactively • Detect data drift, feature drift, model behavior changes, and silent failures before they impact customers via thresholding, alerting, anomaly detection, and release-over-release monitoring. • Lead diagnostic tooling and root-cause analysis • Build dashboards, logs, and diagnostic workflows that progress quickly from 'recommendations look off' to root cause, with context captured across candidates, features, scores, ranking decisions, and downstream outcomes. • Own ML deployment safety • Define and operate automated gates that prevent bad models or bad data from being promoted to production. • Partner with MLEs to establish validation checks, rollback criteria, canary strategies, shadow testing, and release health reviews. • Lead ML incident response • Own incident response practices for ML systems, including rollback playbooks, hotfix strategies, severity definitions, tradeoff frameworks, communications, and post-mortems. • Drive closure of systemic gaps after incidents rather than only resolving the immediate issue. • Partner across ML Platform, Data, and ML Partner with DevOps/Platform on infrastructure and observability needs; with Data Engineering on data quality, drift, and freshness; and with ML Engineering to embed operational requirements into development and deployment workflows. • Set standards and mentor others Act as the technical lead for ML operations: establish reusable patterns, playbooks, and standards, and mentor engineers on reliability, observability, and operational rigor.