Machine Learning Engineer

About The Role

The role owns the design, implementation, and scaling of production machine learning systems powering high-throughput applications.

The engineering team collaborates closely with applied researchers and data platform engineers to bridge the gap between experimental modeling and reliable production infrastructure.

Key Responsibilities

• Design and deploy production-grade machine learning models, ensuring high availability, low latency, and robust fault tolerance
• Build scalable data ingestion and feature engineering pipelines using Python, Apache Spark, and distributed compute frameworks
• Implement robust CI/CD pipelines for ML models, integrating automated testing, experiment tracking, and model registry tools
• Monitor deployed models for data drift, concept drift, and performance anomalies, establishing automated alerting and retraining loops
• Optimize model inference costs and resource utilization across cloud infrastructure using quantization, pruning, and hardware acceleration
• Collaborate with cross-functional teams to translate complex business requirements into robust technical specifications and ML solutions
  
  

What We Are Looking For

• 3-6 years of professional software engineering experience, with at least 3 years focused specifically on machine learning engineering in production
• Proficiency in Python and deep familiarity with core ML frameworks such as PyTorch, TensorFlow, or JAX
• Hands-on experience with cloud infrastructure and MLOps platforms including AWS SageMaker, GCP Vertex AI, or MLflow
• Solid understanding of distributed systems, data processing pipelines, and containerization technologies like Docker and Kubernetes
• BS or MS in Computer Science, Machine Learning, Statistics, or a related technical field
• Bonus: Contributions to open-source ML projects or published research at top-tier AI/ML conferences