Hybrid Senior Machine Learning Engineer

at Shipt

• Job title: Senior Machine Learning Engineer
• Job type: Full Time
• Experience level: Senior
• Salary: $109,000 - $219,000 per year
• Degree requirement: Postgraduate Degree
• Tech skills: Airflow, Kafka, Python, PyTorch, Scikit-Learn, Spark, Tensorflow
• Location requirements: Hybrid, Birmingham, United States

About the role

• Senior Machine Learning Engineer developing and deploying ML systems for fraud detection at Shipt. Collaborating with cross-functional teams to enhance fraud detection and risk assessment processes.

Responsibilities

• Design, implement and optimize scalable production ready ML systems for fraud detection, risk scoring, and anomaly detection using structured and unstructured data.
• Build and productionize end-to-end ML pipelines, including data ingestion, feature engineering, model training, deployment, and monitoring.
• Contribute to shape the ML roadmap enabling ML models as well as unfold Agentic AI capabilities for fraud detection and prevention.
• Collaborate with data scientists to productionize statistical and ML models with a focus on low-latency, high-throughput, and real-time fraud detection.
• Develop automated feedback loops for model retraining and continuous improvement as fraud patterns evolve.
• Leverage experimentation frameworks (e.g., A/B testing, causal inference) to evaluate the impact and lift of fraud prevention models and systems.
• Ensure model governance and compliance, including explainability, versioning, and audit readiness.
• Engage in continuous learning and development, staying up-to-date with the latest advances in machine learning, Gen AI and software engineering.

Requirements

• Master’s or Ph.D. in Computer Science, Machine Learning, Statistics, or a related quantitative discipline.
• 3+ years of experience in developing and deploying machine learning models in large-scale production environments, delivering measurable business impact.
• Experience in the development, training, productionization and monitoring of ML solutions at scale, ranging from offline pipelines to online serving and MLOps.
• Proficiency in Python and ML libraries (e.g., TensorFlow, PyTorch, scikit-learn)
• Experience with data pipeline tools and frameworks (e.g., Airflow, Spark, Kafka, or similar).
• Strong understanding of feature engineering, model evaluation, monitoring, and drift management.
• Experience applying graph ML techniques to detect relational or network-based fraud patterns (e.g., NetworkX, PyTorch Geometric, etc) is highly desirable.
• Familiarity with GenAI/LLM ops, real-time personalization, or fraud detection is a plus.
• Excellent problem-solving, communication, and cross-functional collaboration skills.

Benefits

• Employees (and eligible family members) are covered by medical, dental, vision and more.
• Employees may enroll in our company’s 401k plan.
• Employees will also be eligible to receive discretionary vacation for exempt team members, paid holidays throughout the calendar year and paid sick leave.
• Other compensation includes eligibility for an annual bonus and the potential for restricted stock units based on role.