CarbonForge - Senior Machine Learning Engineer (Production Systems)

The Role

You will lead the end-to-end lifecycle of machine learning, from algorithmic design to high-scale deployment. Your focus will be on system efficiency, model pruning, and MLOps excellence.

Key Responsibilities

• •Production Excellence: Design, build, and maintain scalable ML pipelines that move models from research to production environments seamlessly.
• •Algorithmic Optimization: Apply your research background to optimize model architectures for inference speed and memory footprint (quantization, distillation, and pruning).
• •Infrastructure & Deployment: Work closely with Platform teams to implement CI/CD for ML, ensuring high availability and automated monitoring of model drift.
• •Efficiency Research: Identify bottlenecks in the current stack and implement state-of-the-art techniques to reduce computational costs ($O$ complexity analysis) without sacrificing accuracy.
• •Mentorship: Act as a technical bridge between junior data scientists and senior infrastructure engineers.

Required Qualifications

• •PhD in Computer Science, Statistics, Physics, or a related field (focus on AI/ML preferred)
• •Proven track record of shipping production-grade models in a fast-paced industrial environment.
• •3+ years of industry experience specifically focused on deploying models at scale.
• •Expertise in MLOps frameworks (e.g., Kubeflow, TFX, MLflow) and orchestration tools (Kubernetes, Docker).
• •Deep proficiency in Python, C++, and high-level frameworks like PyTorch or TensorFlow.
• •Strong understanding of distributed computing and low-latency API design.