PA
PartSpaceMachine Learning Engineer – m/f/d
Machine Learning Engineer responsible for optimizing AI pipelines at Easy2Parts. Join a growing team to revolutionize component sourcing with AI technology.
About the role
- 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.
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NO
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