Staff Engineer - Machine Learning

The Impact You Will Create

As a Staff Machine Learning Engineer, you will serve as the critical architectural bridge between cutting-edge Data Science research and massive-scale, product-ready implementation. You will move beyond standard feature delivery to define the technical vision and infrastructure that brings sophisticated algorithms to life. Your work will directly result in:

• Massive Scale & Reliability: Architecting and deploying robust ML APIs and pipelines capable of serving millions of requests with ultra-low latency and unwavering reliability.
• Engineering Excellence: Setting the gold standard for ML Engineering practices, MLOps, and system design across the organization.
• Accelerated AI Innovation: Transforming theoretical models into high-performance, production-grade systems, directly shrinking the time-to-market for complex ML business solutions.
• Cross-Organizational Multiplier: Acting as a strategic technical anchor, influencing cross-product architects, leading POCs, and mentoring teams to ensure tight technical alignment across all engineering groups.

Roles & Responsibilities

• End-to-End Pipeline Architecture: Architect, build, and manage comprehensive, highly scalable ML pipelines covering data pre-processing, model generation, automated deployment, cross-validation, and active feedback loops.
• ML Algorithm Implementation: Partner deeply with Data Scientists to translate complex, theoretical ML models and algorithms into high-performance, production-grade code.
• High-Performance Service Delivery: Design, develop, and deploy highly extensible ML API services rigorously optimized for low latency and massive scalability.
• Operational Intelligence & Observability: Devise and build advanced monitoring capabilities to track both engineering system health and ML model performance metrics (drift, accuracy, etc.) over the long term.
• Strategic Innovation & Architecture: Architect solutions from scratch, leading Proof of Concept (POC) initiatives across various tech stacks to validate optimal solutions for complex business challenges.
• Technical Leadership & Execution: Own the full lifecycle of feature delivery autonomously—from requirement gathering with product stakeholders to final deployment—while collaborating with cross-product architects to drive platform adoption.