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.

Experience: 9+ years of progressive, highly relevant experience in software engineering and machine learning development.

Production Excellence: A proven, demonstrable track record of successfully architecting, building, and productionizing complex Machine Learning solutions at an enterprise scale.

MLOps Mastery: Deep, practical experience with modern MLOps practices, ensuring seamless, automated, and secure model transitions from development and training into production environments.

Education: A Bachelor’s or Master’s degree in Computer Science, Artificial Intelligence, Mathematics, or a related quantitative field.

Skills Core Programming: Expert-level Object-Oriented Programming (OOP) expertise in Python and Java.

ML & Deep Learning Frameworks: Mastery of industry-standard ML libraries and Deep Learning frameworks, including PyTorch, Keras, TensorFlow, and TFServing.

Foundational Engineering: Deep, advanced understanding of Data Structures, Algorithms (DSA), and complex, distributed System Design.

Cloud & Infrastructure: Hands-on knowledge and proficiency with Cloud infrastructure and services (AWS highly preferred) for large-scale data processing and ML model hosting.

Analytical Problem Solving: Exceptional analytical and debugging skills with a relentless focus on algorithmic optimization, resource efficiency, and resolving bottlenecks in distributed systems.