About The Role

The Machine Learning Engineer will build, productionize, and operate machine learning systems across the full development lifecycle—from data and feature pipelines through model training, deployment, and monitoring. The work will span predictive modeling, ranking, NLP, and generative AI use cases running on cloud infrastructure.

This role matters because model quality, latency, reliability, and maintainability directly influence product performance. The engineer will partner with data scientists, software engineers, and platform teams to turn research prototypes into scalable services used by customers in production.

Key Responsibilities

Design, train, and evaluate supervised and deep learning models using Python, PyTorch, TensorFlow, or scikit-learn for high-impact product use cases
Build reliable data and feature pipelines with SQL, Python, Spark, and workflow orchestration tools such as Airflow or Dagster
Deploy and serve models through AWS SageMaker, GCP Vertex AI, Kubernetes, or equivalent production infrastructure
Develop model APIs and inference services using FastAPI, Docker, and REST or gRPC, with clear latency and availability targets
Implement experiment tracking, model versioning, CI/CD, and reproducible training workflows using tools such as MLflow, Weights & Biases, and GitHub Actions
Monitor production models for accuracy, drift, data quality, latency, and resource usage, then establish alerting and rollback procedures
Collaborate with product, engineering, and research partners to define success metrics, analyze model performance, and prioritize improvements

What We Are Looking For

3–8 years of experience in machine learning engineering, applied machine learning, or a closely related software engineering role, including production model deployment
Strong Python proficiency and hands-on experience with PyTorch, TensorFlow, scikit-learn, or comparable machine learning frameworks
Solid understanding of machine learning fundamentals, including feature engineering, model selection, evaluation metrics, regularization, and error analysis
Experience building production data pipelines with SQL and at least one distributed processing or orchestration technology such as Spark, Airflow, or Dagster
Practical experience with cloud platforms and deployment technologies, including AWS, GCP, Azure, Docker, Kubernetes, or managed ML services
Bachelor’s or master’s degree in computer science, machine learning, statistics, mathematics, or a related technical field
Bonus: Experience with LLM applications, embeddings, RAG systems, model fine-tuning, vector databases, GPU optimization, or MLOps observability platforms

Job description