• Design and build data pipelines for agent training: collection, filtering, deduplication, formatting, and versioning across text, image, and multimodal sources • Build and maintain infrastructure for efficient data loading, storage, and retrieval at scale (S3, distributed systems, streaming pipelines) • Collaborate with research scientists to translate research requirements into concrete data specifications, and iterate as experiments reveal new needs • Create evaluation datasets and benchmarks in collaboration with researchers—curating task distributions that surface real failure modes • Develop tooling for dataset construction—including human annotation workflows, synthetic data generation, and preference data collection for RLHF/DPO-style training • Own data quality: build validation frameworks, monitor for drift and contamination, and establish standards that make datasets trustworthy and reproducible • Document datasets thoroughly: provenance, known limitations, intended use cases, and versioning history • Implement comprehensive test coverage for data pipelines and ML workflows, ensuring reliability and catching regressions early • Elevate codebase quality through code reviews, refactoring, and establishing engineering best practices that help research velocity scale sustainably • Contribute to team roadmaps by identifying data bottlenecks and proposing solutions that unblock research velocity

• Lead research and development of pCTR and conversion prediction models, with a focus on improving calibration, reducing training data biases (selection bias, position bias, optimizer’s curse), and advancing model accuracy across Instacart’s ads surfaces. • Design and implement debiasing techniques such as Mixed Negative Sampling (MNS), Inverse Propensity Weighting (IPW), counterfactual risk minimization, and calibration methods (Platt scaling, isotonic regression) to address systematic prediction biases. • Contribute to the next-generation Multi-Domain Multi-Task (MDMT) model architecture, incorporating innovations like Mixture-of-Experts (MoE), Transformer layers for sequential user behavior, and LoRA adaptors for scalable domain fine-tuning. • Drive sequence modeling initiatives including the TIGER generative retrieval system and Semantic ID representation learning, expanding their application across ads surfaces such as Product Details, Search and other placements. • Collaborate with the broader ML community in the company on the path toward Foundation Models using autoregressive user behavior prediction. • Formulate and scope ambiguous modeling problems from first principles. Translate business observations (e.g., overcalibration patterns, cold-start underperformance) into well-defined ML research directions with clear evaluation criteria. • Publish and present findings internally. Contribute to the team’s culture of technical rigor through design reviews, paper sharing, and experiment retrospectives.

• Work on strategic use cases, translating business needs into data-driven analytical solutions • Conduct end-to-end analyses: exploration, understanding, data preparation, and delivery • Work with large volumes of data, ensuring quality, integrity, and statistical consistency • Develop, validate, and iterate statistical and Machine Learning models, ensuring performance, stability, and explainability • Monitor model performance metrics, proposing continuous improvements • Contribute to building and evolving Feature Stores, promoting standardization and governance • Create and maintain notebooks and pipelines in Databricks, following analytical engineering best practices • Collaborate with multidisciplinary teams (Engineering, Governance, BI, and Business) • Communicate results, hypotheses, and insights clearly and strategically, using data visualization and storytelling

Opportunity Overview: As a Staff Machine Learning Engineer, you will play a critical technical leadership role on Cohere Health’s Enterprise ML team, with a primary focus on powering and scaling machine learning capabilities within our Intake product. You will apply state-of-the-art machine learning, large language models, and agentic architectures to complex clinical and operational intake workflows, helping automate decision-making, improve data quality, and reduce administrative burden for clinical teams. In this role, you will partner closely with clinical operations, product, and engineering teams to uncover hidden drivers, inform strategic decisions, and deploy production-grade ML systems that directly impact how members and providers experience Cohere Health at the front door of care. This role blends deep hands-on technical work with strategic influence, mentorship, and ownership across multiple work streams, while contributing to broader Enterprise ML initiatives. What you’ll do: - Design, build, and deploy advanced machine learning systems for retrieval, classification, prediction, and generative use cases. - Apply advanced statistical and ML techniques to extract insights from large-scale structured and unstructured healthcare datasets. - Lead model development across the ML lifecycle, including experimentation, training, evaluation, deployment, monitoring, and iteration. - Develop and oversee scalable, reusable codebases and ML infrastructure to support production use cases. - Collaborate cross-functionally with product managers, clinicians, data engineers, BI engineers, and design teams to translate business and clinical needs into robust ML solutions. - Drive experimentation by defining problem statements, forming falsifiable hypotheses, and designing rigorous evaluation frameworks tied to business outcomes. - Review, communicate, and present ML insights and results to technical and non-technical stakeholders, including executive leadership. - Serve as a technical mentor and advisor to junior engineers, providing guidance on ML best practices, experimentation, and system design. - Contribute as an expert advisor across multiple initiatives, helping shape ML strategy and performance tracking across the organization. What you’ll need: - Master’s degree (PhD preferred) in Computer Science, Data Science, Machine Learning, or a closely related quantitative field. - 8+ years of professional experience in applied machine learning or data science,,including ownership of production ML systems. - Deep expertise in Python and modern deep learning frameworks (e.g., PyTorch). - Hands-on experience building and deploying deep learning models (e.g., transformers) for NLP tasks. - Strong understanding of experimental design, model evaluation, and optimization for real-world production environments. - Experience leveraging cloud platforms (AWS preferred) across the ML lifecycle (training, deployment, monitoring). - Proven ability to collaborate with product, business, and clinical partners to drive data- informed decision-making. - Excellent written and verbal communication skills, with experience presenting to both technical and non-technical audiences. Pay & Perks: 💻 Fully remote opportunity with about 5% travel 🩺 Medical, dental, vision, life, disability insurance, and Employee Assistance Program 📈 401K retirement plan with company match; flexible spending and health savings account 🏝️ Flexible Time Off + Company Holidays 👶 Up to 14 weeks of paid parental leave 🐶 Pet insurance  The salary range for this position is $225,000 to $240,000 annually; as part of a total benefits package which includes health insurance, 401k and bonus. In accordance with state applicable laws, Cohere is required to provide a reasonable estimate of the compensation range for this role. Individual pay decisions are ultimately based on a number of factors, including but not limited to qualifications for the role, experience level, skillset, and internal alignment. Interview Process*: - Connect with Talent Acquisition for a Preliminary Phone Screening - Meet your Hiring Manager! - Behavioral Interview(s) - Case Study *Subject to change About Cohere Health: Cohere Health is a fast-growing clinical intelligence company that’s improving lives at scale by promoting the best patient-specific care options, using cutting-edge AI combined with deep clinical expertise. In only four years our solutions have been adopted by health plans covering over 15 million lives, while our revenues and company size have quadrupled. That growth combined with capital raises totaling $106M positions us extremely well for continued success. Our awards include: 2023 and 2024 BuiltIn Best Place to Work; Top 5 LinkedIn™ Startup; TripleTree iAward; multiple KLAS Research Points of Light awards, along with recognition on Fierce Healthcare's Fierce 15 and CB Insights' Digital Health 150 lists. The Coherenauts, as we call ourselves, 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. We can’t wait to learn more about you and meet you at Cohere Health! 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. #LI-Remote #BI-Remote