Lead Machine Learning Engineer

WHAT YOU WILL DELIVER

ML system architecture

Define end-to-end architectures spanning data and feature access, training, model artifacts, serving, integration, monitoring, and operational controls for search and recommendation workloads.

Reusable platform patterns

Establish scalable patterns and shared components for training, batch and real-time inference, deployment, performance optimization, testing, and continuous delivery.

Training and inference pipelines

Lead implementation of modular pipelines for data loading, preprocessing, training, artifact management, validation, and low-latency or high-throughput prediction.

Search and recommendation enablement

Support retrieval, ranking, personalization, and recommendation models with appropriate serving strategies, APIs, feature pipelines, and system integrations.

Performance engineering

Drive profiling, load testing, capacity planning, and optimization of compute, latency, throughput, memory, and resource utilization.

Deployment and reliability

Guide containerization, orchestration, environment promotion, model versioning, rollback, health checks, and production-readiness practices.

Observability and governance

Define standards for logging, metrics, alerts, data and model quality checks, traceability, auditability, and incident diagnosis.

Cross-functional technical leadership

Align Data Science, platform, data, backend, and product partners on architecture, interfaces, dependencies, risks, and execution plans.

Technical enablement

Lead design and code reviews, document reference implementations, and transfer knowledge so internal teams can maintain and extend delivered capabilities.

Engagement outcomes

Deliver agreed software, infrastructure patterns, documentation, recommendations, and knowledge transfer within the contract scope and timeline. REQUIRED QUALIFICATIONS

• Bachelor's or Master's degree in Computer Science, Engineering, Data Science, or a related quantitative field, or equivalent practical experience.
• 6+ years of experience building and operating production machine learning systems, including technical leadership across complex ML engineering initiatives.
• Strong Python and distributed data processing skills, including experience with Spark and large-scale data or feature pipelines.
• Demonstrated ability to define reusable ML engineering patterns, influence architecture across teams, and lead delivery without relying on formal authority.
• Experience with deep learning or machine learning frameworks such as TensorFlow, PyTorch, Hugging Face, scikit-learn, or comparable technologies.
• Hands-on experience building training pipelines, batch or online inference systems, and model-serving APIs.
• Experience with cloud platforms such as GCP or Azure and production-oriented containerization and orchestration using Docker and Kubernetes.
• Experience with software engineering practices including package development, automated testing, CI/CD, version control, and code review.
• Understanding of APIs, distributed systems, model lifecycle management, monitoring, logging, and production reliability.
• Ability to diagnose and optimize performance across latency, throughput, concurrency, compute, and resource utilization.
• Ability to communicate architectural decisions, technical tradeoffs, risks, and delivery status to technical and non-technical partners. PREFERRED QUALIFICATIONS

• Experience supporting search systems, recommender systems, learning-to-rank, retrieval, embeddings, personalization, or multi-stage ML pipelines.

• Experience with managed ML platforms and orchestration capabilities such as Vertex AI Pipelines, Azure Machine Learning, MLflow, or comparable tooling.
• Experience with feature stores or shared offline and online feature pipelines.
• Experience using infrastructure-as-code tooling and production observability platforms.
• Experience with Databricks, Linux, shell scripting, REST APIs, or FastAPI.
• Experience in retail, e-commerce, marketplaces, or another high-scale consumer domain.
• Experience establishing engineering standards, guiding technical reviews, and creating reference architectures across teams.