Senior Machine Learning Engineer

Impact

As a Senior Machine Learning Engineer on our Fraud Detection & Risk Assessment Data Science team, you will design, build, and deploy cutting-edge ML systems that protect millions of users and multiple product lines from evolving fraud and risk threats. You'll work with complex, high-volume data streams to develop models and infrastructure that identify anomalies, assess risk, and enable real-time decisioning across our ecosystem.

Collaborating closely with Product, Fraud Operations, Engineering and Data Science teams, you will translate business risk signals into robust machine learning pipelines—improving the accuracy, efficiency, and impact of our fraud detection systems. The ideal candidate brings deep technical expertise, strong engineering discipline, and a passion for applying ML to real-world risk problems. You thrive in a fast-paced environment and are excited to influence ML strategy and best practices across the organization.

Core 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

What You'll Need to Be Successful

• 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.

Work Arrangement

Shipt considers candidates located near a Shipt office or workspace in Birmingham, San Francisco, or Minneapolis to be hybrid, which means that they have the flexibility to work from home (with leader approval) or at a Shipt office in order to facilitate the ability to innovate, collaborate, and spark team connections. In-office expectations will vary by role and leader. Certain roles may require in-office presence on a full-time basis. Please work with your recruiter to learn more about the classification of this role.

About Shipt

Shipt is a retail tech company that connects people to reliable, high-quality delivery with a personal touch. Shipt connects customers to the things they want from the stores they love, retail businesses to more satisfied customers, and workers to new earning opportunities.

At Shipt, we aim to put our team first to boost a sense of belonging, spark opportunities for growth, provide unique benefits and commit to giving back to our communities in ways that make life better, both personally and professionally. We understand that our service, our culture, and our connection to our communities are only made better by every single person who shows up to work here every day.

Learn More.

Shipt is an independently operated, wholly owned subsidiary of Target Corporation and available in more than 5,000 U.S. cities. Shipt was founded and is headquartered in Birmingham, Alabama. For more information, please visit Shipt's company site at

We are an equal opportunity employer and value diversity at our company. We do not discriminate on the basis of race, color, national origin, ethnicity, religion or religious belief, sex (including pregnancy, childbirth, or related medical conditions), sexual orientation, gender, gender identity, gender expression, transgender status, sexual stereotypes, age, military or veteran status, disability, or any other characteristic protected by law.

Please inform your recruiting contact upon initial connection if you need a reasonable accommodation. If you need assistance filling out a job application, please complete this

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Pursuant to the San Francisco Fair Chance Ordinance, we will consider for employment qualified applicants with arrest and conviction records.

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.

Pay range:

Metro Areas of Boston, District of Columbia, Los Angeles, San Francisco, Seattle and New York City:

$123,000-$247,000

All other locations:

$109,000-$219,000

Please note that the salary range above is a guideline, and individual total compensation will vary based on factors such as qualifications, skill level, competencies, and work location.