Senior Machine Learning Engineer

What you'll do

• Design, develop, and deploy production-scale machine learning models and data pipelines.
• Own the end-to-end ML workflow from data ingestion through model serving.
• Write and review code for model training, evaluation, and inference pipelines.
• Develop technical specifications for ML components, projects, and model enhancements.
• Set the architectural direction for ML platforms and data infrastructure.
• Provide technical guidance and oversight to less experienced engineers and data scientists.
• Lead team members in problem analysis, model debugging, and issue resolution.
• Execute major model improvements using new or existing technologies.

What we're looking for

• Bachelor’s degree in Computer Science, Statistics, Mathematics, or a related field, or related work experience.
• 5+ years of relevant experience designing, training, and deploying machine learning models in production environments at scale.
• Experience with Python and ML frameworks such as TensorFlow, PyTorch, or scikit-learn.
• Strong understanding of ML fundamentals including supervised/unsupervised learning, model evaluation, and feature engineering.
• Strong expertise in MLOps practices including model versioning, experiment tracking, CI/CD for ML, and model monitoring.
• Experience with cloud-based ML services and infrastructure such as AWS SageMaker, EC2, and S3.
• Experience with data pipeline and orchestration tools like Airflow, Spark, or Kafka.
• Experience with Snowflake, database technologies, containerization (Docker, Kubernetes), and data manipulation tools.
• Experience with large-scale recommendation systems, personalization, NLP, or computer vision (preferred).
• Experience with real-time ML inference and low-latency serving architectures (preferred).
• Familiarity with LLMs and generative AI integration in production systems (preferred).
• Experience with Java (e.g., Spring Boot) (preferred).