Senior Machine Learning Engineer

The role

Remote · Any time zone · English C1+

You will own production ML systems end to end for a large-scale e-commerce platform: data processing, model training, deployment, and continuous improvement of accuracy, reliability and cost. You’ll work in a small, senior team alongside data scientists and an MLOps engineer.

Stack: Python, PySpark, Databricks, PyTorch/TensorFlow, AWS (SageMaker, ECS/Fargate, Lambda, S3, Glue, Athena, DynamoDB, CloudWatch).

Current priorities: modernizing large-scale data pipelines, consolidating orchestration, reducing cloud processing costs, and improving model quality through A/B testing.

What you’ll do

• Build and productionize models for recommendation, ranking, prediction and forecasting.
• Develop batch and real-time PySpark/Databricks pipelines for ingestion, features, training data and inference.
• Ship models to production with serving, APIs, containers, CI/CD, monitoring and retraining.
• Evaluate models with offline metrics and online A/B tests tied to business outcomes.
• Optimize performance, scalability and cost of ML and data workloads.
• Contribute to architecture decisions and engineering standards.

Must-haves

• 5+ years of hands-on ML engineering in industry. Research, academic, data engineering or AI architecture roles don’t count.
• Recommender systems or consumer-domain experience (at least one).
• Recommender work: collaborative filtering, implicit feedback, ALS/matrix factorization, candidate generation and ranking, cold start, offline and online evaluation.
• Consumer domain: ML engineering in retail, e-commerce or consumer companies.
• Production deep learning. Models you designed, trained, deployed and operated with PyTorch or TensorFlow, including evaluation, monitoring and retraining.
• Large-scale distributed processing. Spark/PySpark or equivalent on real big-data workloads, including performance tuning. You should be able to state the data volumes and explain why distributed processing was needed.
• Cloud ML deployment. AWS preferred.
• Strong Python and SQL with production-quality engineering practices.
• Clear technical communication in English (C1 minimum).

Nice-to-haves

• Both recommender systems and e-commerce/marketplace personalization experience
• Databricks (Jobs, Workflows, Delta Lake)
• Airflow or similar orchestration
• MLflow or similar experiment tracking and model registry
• Docker and FastAPI-based model serving
• A/B testing against business metrics
• Latency and cloud cost optimization
• LLM, RAG or agent experience, as a complement to core ML, not a replacement for it

Not a fit if your background is mainly in

• ML limited to financial services or insurance
• Research or academia without 5+ years in industry
• Data engineering, research data science or AI architecture without hands-on model ownership
• GenAI/LLM work without production deep learning on large data volumes

Lugar de trabajo: Empleo remoto