• 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.

• part of an Agile team dedicated to productionizing machine learning applications and systems at scale • participate in the detailed technical design, development, and implementation of machine learning applications using existing and emerging technology platforms • focus on machine learning architectural design • develop and review model and application code • ensure high availability and performance of machine learning applications • continuously learn and apply the latest innovations and best practices in machine learning engineering • design, build, and deliver ML models and components that solve real-world business problems • inform ML infrastructure decisions using understanding of ML modeling techniques and issues • solve complex problems by writing and testing application code, developing and validating ML models • collaborate as part of a cross-functional Agile team • retrain, maintain, and monitor models in production • leverage or build cloud-based architectures, technologies, and platforms to deliver optimized ML models at scale • construct optimized data pipelines to feed ML models • leverage continuous integration and continuous deployment best practices to ensure successful deployment

Role Description A Senior Machine Learning Ops Engineer at Sheetz ensures that AI models move seamlessly from “working on a laptop” to running reliably across our stores, applications, and systems at scale. This role powers capabilities like smarter inventory management, enhanced customer experiences, and faster decision-making that keeps pace with the way Sheetz operates. The MLOps Engineer designs, builds, and maintains the pipelines, deployment processes, and monitoring systems that allow models to run continuously and perform consistently. Just as Sheetz kitchens operate around the clock to serve customers, this role keeps our AI systems running 24/7, using data as the ingredients and algorithms as the recipes that drive our technology. This role qualifies for a remote work arrangement within our 7 state footprint (PA, OH, MI, WV, VA, MD, NC). Responsibilities - Lead the end-to-end development and optimization of ML pipelines, including training, validation, deployment, monitoring, and retraining workflows at scale. - Guide the use of and implement infrastructure for tools such as ML flow, TensorFlow, PyTorch, Docker, and Kubernetes to support scalable production workflows for model deployment and lifecycle management. - Design and monitor tools for performance monitoring, drift detection, and automated alerting. - Develop CI/CD pipelines to enable safe, rapid model iteration, deployment, and retraining across environments. - Write, review, and maintain high-quality, production ready code, ensuring robust, reproducible, and secure ML systems. - Apply advanced software engineering and ML Ops best practices to operationalize machine learning solutions efficiently and reliably. - Collaborate with cross-functional teams to align ML solutions with business needs and system requirements and guide integration efforts to embed ML into production applications. - Maintain thorough documentation, version control, metadata tracking, and lineage to support reproducibility and compliance of ML models. - Recommend and implement improvements to ML infrastructure, frameworks, and operational standards, elevating the organization’s ML maturity and capabilities. - Mentor and coach junior engineers, providing guidance on technical challenges, workflow design, and career development. Qualifications - Bachelor’s degree in Computer Science, Management Information Systems, Computer Engineering, or related discipline is required. - Minimum 5 years hands-on experience in designing, developing, and operationalizing machine learning solutions, with a strong focus on ML Ops practices and infrastructure is required. - Previous experience working with large databases – both structured and unstructured – to build data pipelines and self-service dashboards for business users required. - Previous experience in managing machine learning pipelines, lifecycle management, and deployment at scale—including training, validation, serving, and monitoring required. - Previous experience with CI/CD pipelines for ML workflows and containerization tools such as Docker and Kubernetes preferred. - Previous experience with secure and scalable cloud environments (e.g., AWS, GCP, Azure) and infrastructure-as-code and platform-as-a-service (PaaS) offerings preferred. - Cloud Platforms (AWS, GCP, Azure) preferred. - MLOps tools and frameworks (e.g., ML Flow, Kubeflow, TFX) preferred. - DevOps certifications (e.g. Docker, Kubernetes, Terraform, CI/CD Tools) preferred. Company Description

• Design, implement, and evaluate deep learning models across biomedical data modalities • Develop multimodal AI architectures integrating H&E whole-slide imaging data with molecular and clinical data sources • Build scalable, production-quality ML workflows and pipelines using cloud infrastructure (AWS) • Apply modern ML techniques including CNNs, vision transformers (ViTs), sequence transformers, representation learning, and foundation model fine-tuning • Collaborate with technical and clinical teams to translate ML prototypes into validated tools • Analyze model outputs to generate reproducible biological and clinical insights • Document pipelines thoroughly and communicate data-driven findings to stakeholders