ML Software Engineering Lead

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We are seeking an experienced and visionary ML Software Engineering Lead to serve as the technical and functional leader for the Data Science Enablement engineering function, which owns the production development and ongoing operations of high‑profile ML products.

While you will have no direct people‑management responsibilities, you will be accountable for the technical strategy, operational maturity, engineering standards, platform capabilities, and long‑term effectiveness of the ML software engineering practice. You will balance strategic leadership with hands‑on technical contribution, dividing your time between setting technical direction and actively participating in architecture, design reviews, code reviews, and selected implementation efforts.

In this strategic role, you will collaborate closely with product, data science, platform engineers, and other product delivery teams to translate ML models and data‑driven algorithms into robust, scalable, and low‑latency production solutions.

• Define the technical vision and strategy for ML software engineering initiatives, aligning them with business goals.
• Develop scalable capabilities to power real‑time decisioning engines throughout the payment lifecycle and beyond.
• Enable rapid experimentation while ensuring robust, scalable, and secure deployment of ML solutions.
• Establish and evolve engineering standards, operating practices, and technical governance.
• Mentor engineers, provide technical coaching, and promote technical excellence.
• Champion collaboration, continuous improvement, and knowledge sharing.
• Drive alignment across teams through technical influence, architectural guidance, and shared engineering standards rather than direct management authority.
• Identify capability gaps and drive improvements to tooling, automation, observability, and operational processes.
• Drive consistency in engineering practices and operational processes across teams delivering and supporting ML‑powered products.
• Establish operational standards for production ML systems, including reliability objectives, observability, incident management, and support processes.
• Guide the architecture, implementation, deployment, and operation of ML products and reusable components.
• Ensure systems and components meet requirements for scalability, latency, explainability, and regulatory compliance.
• Establish and promote best practices for ML software engineering; stay abreast of industry trends and emerging technologies to drive adoption of modern tools, frameworks, and infrastructure.
• Contribute to QA and code as needed.
• Partner closely with research‑focused data science teams, business stakeholders, infrastructure support teams, data engineering teams, security/compliance teams, etc. to identify opportunities and incorporate ML into products and systems.
• Collaborate with other data science and engineering leaders to establish an operating model for machine learning R&D that optimizes end‑to‑end delivery of business value.
• Communicate complex technical concepts to non‑technical stakeholders effectively.

• Bachelor’s or Master’s degree in Computer Science, Statistics, Mathematics, Engineering, or a related field (PhD a plus).
• 7+ years of ML software engineering, ML ops, ML engineering, or ML research experience.
• 5+ years of experience deploying large‑scale, real‑time ML models in customer‑facing, production environments, including significant hands‑on experience.
• 2+ years of technical leadership experience on an early‑stage ML software engineering team.
• 2+ years of data science research experience.
• Proven experience developing microservices at scale (API design, monitoring, deployment strategies, containerization) in a cloud environment (preferably AWS and Databricks).
• Strong understanding of the data science/ML research process.
• Strong understanding of software engineering, MLOps, and Dev Ops best practices.
• Strong Python skills, including relevant libraries such as Pandas, Num Py, scikit‑learn.
• Proficiency in SQL and No
  
  SQL databases.
• Ex…