Machine Learning Engineer

Python PyTorch TensorFlow AWS MLOps Spark

About the role

As a Machine Learning Engineer, the candidate will design, build, and deploy ML models that solve complex business problems at scale. This role owns the full ML lifecycle—from problem formulation and data preparation through model training, evaluation, and production deployment—with a focus on building systems that are reliable, performant, and maintainable.The engineer will work at the intersection of research and engineering, translating state-of-the-art ML techniques into production systems that serve real users. They will collaborate with data engineers to build robust feature pipelines, partner with product teams to identify high-impact ML applications, and establish MLOps practices that enable rapid experimentation and deployment.The ideal candidate combines deep ML expertise with strong software engineering skills. They are comfortable working across the stack, from distributed training infrastructure to model serving and monitoring. This role offers significant ownership and the opportunity to shape ML strategy and architecture.

What you'll do

• Design and implement machine learning models to solve business problems
• Build end-to-end ML pipelines for training, evaluation, and deployment
• Develop and maintain feature engineering pipelines
• Optimize model performance for accuracy, latency, and cost
• Deploy models to production using AWS SageMaker, Kubernetes, or similar
• Implement monitoring and alerting for model performance in production
• Collaborate with data engineers, product managers, and stakeholders
• Stay current with ML research and evaluate new techniques for applicability

What you bring

• 3-6 years of experience in machine learning engineering or applied ML
• Strong Python skills with deep experience in PyTorch or TensorFlow
• Experience training and deploying models in production environments
• Solid understanding of ML fundamentals (deep learning, classical ML, optimization)
• Experience with cloud ML platforms (AWS SageMaker, GCP Vertex AI, or similar)
• Familiarity with MLOps practices (experiment tracking, model versioning, CI/CD)
• Experience with distributed computing (Spark, Ray) is a plus
• Strong communication skills and ability to work cross-functionally

What you get

• Medical, dental, and vision insurance
• 401(k)
• Equity
• Flexible PTO
• Hybrid work options

Job details and compensation are subject to change.