Block
Senior Machine Learning Engineer, Risk Modeling
Rol remoto de 11003 Risk - Prod Dev - Square con fit claro de ubicación del candidato.
PublicadoAgregado recientemente
Países elegibles27 países aceptados
Señal de senioritySenior
Modelo de trabajoRemoto
Ubicaciones aceptadas para candidatos
Resumen del rol
Senior Machine Learning Engineer, Risk Modeling
Requisitos y responsabilidades
Contenido del rol extraído en secciones para revisar más rápido.
You Will
- Partner with product, engineering, data science, policy, and operations to design and productionize ML-driven risk solutions at scale
- Own end-to-end machine learning systems — from problem definition and modeling to deployment, monitoring, and iteration
- Lead technical decision-making within your workstreams and influence ML strategy and planning
- Build tooling and processes that improve the speed, reliability, and impact of the ML development lifecycle
- Apply state-of-the-art modeling techniques and third-party data sources to improve detection and decision-making
- Investigate emerging fraud, abuse, and risk patterns to proactively inform product safeguards and policy
- Collaborate closely with ML platform and engineering teams to ensure models operate reliably in real time and at scale
You Have
- 8+ years of industry experience in machine learning, applied AI, or related fields
- Bachelor’s degree in a quantitative field (Computer Science, Engineering, Statistics, Physics, Applied Math); Master’s or PhD preferred
- Proven experience independently designing, deploying, and maintaining ML solutions in production
- Strong familiarity with techniques such as tree-based models, deep learning, transfer learning, or reinforcement learning
- Experience influencing technical direction and collaborating with cross-functional partners at scale
- Strong communication skills, sound judgment, and an ownership mindset
- Curiosity and alignment with Block’s mission of economic empowerment
Technologies We Use and Teach
- Python (NumPy, Pandas, scikit-learn, XGBoost, PyTorch, TensorFlow/Keras)
- PySpark, MLflow, workflow orchestration tools (Airflow, Prefect)
- GCP (Vertex AI), AWS
- Snowflake, MySQL, Tableau, Mode
- Containerization, CI/CD, and production ML best practices
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