Machine Learning Engineer

About The Role

The role owns the design, training, and production deployment of machine learning systems at scale — from experimentation notebooks to monitored, low-latency inference services handling real traffic daily.

This is a hands-on engineering position where model performance, infrastructure reliability, and shipping velocity all matter. The team operates at the intersection of applied research and production software engineering, and the impact of deployed models is measurable within weeks.

Key Responsibilities

• Design, train, and evaluate ML models — classification, ranking, forecasting, and NLP — for use cases that directly drive product outcomes
• Build and maintain feature pipelines in Python and PySpark, ensuring training/serving consistency and data quality across environments
• Deploy and version models using AWS SageMaker, Vertex AI, or Kubernetes-based serving stacks, including canary rollouts and rollback strategies
• Instrument production models for data drift, latency, and performance regression with automated monitoring and alerting pipelines
• Optimize inference performance: quantization, batching, caching, and GPU utilization for latency-sensitive workloads
• Collaborate with data scientists and product engineers to translate ambiguous problem statements into scoped, testable ML solutions
• Contribute to MLOps maturity: CI/CD for models, experiment tracking (MLflow or W&B), and reproducible training pipelines
  
  

What We Are Looking For

• 3–7 years of experience in machine learning engineering, with multiple models taken from prototype to production
• Strong Python engineering skills; proficiency with PyTorch, TensorFlow, or XGBoost in production settings
• Hands-on experience with cloud ML platforms and infrastructure: AWS SageMaker, GCP Vertex AI, or equivalent Kubernetes-based serving
• Deep understanding of ML fundamentals: evaluation methodology, regularization, class imbalance, and leakage prevention
• Experience with workflow orchestration and data tooling: Airflow, dbt, Spark, Kafka, or equivalent
• B.S. or M.S. in Computer Science, Statistics, or a related quantitative field (or equivalent practical experience)
• Bonus: experience fine-tuning LLMs or optimizing transformer inference; contributions to open-source ML tooling; prior work in high-scale, low-latency serving environments