About the Role
You will partner with product, engineering, data science, policy, and operations to design and productionize ML driven risk solutions at scale. You own end-to-end machine learning systems from problem definition and modeling to deployment, monitoring, and iteration. You will lead technical decision-making within your workstreams and influence ML strategy and planning. You will build tooling and processes that improve the speed, reliability, and impact of the ML development lifecycle. You will apply state-of-the-art modeling techniques and third-party data sources to improve detection and decision-making. You will investigate emerging fraud, abuse, and risk patterns to proactively inform product safeguards and policy. You will collaborate closely with ML platform and engineering teams to ensure models operate reliably in real time and at scale.
Requirements
- 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
Responsibilities
- 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
Benefits
- Remote work
- Medical insurance
- Flexible time off
- Retirement savings plans
- Modern family planning