Machine Learning Engineer

Role Overview

Own the full lifecycle of machine learning systems—from experimental architecture and training high-performance models to deploying and monitoring scalable production inference endpoints.

You will partner with data scientists, backend engineers, and product managers to meet strict latency, throughput, and accuracy requirements for millions of daily users.

Responsibilities

• Architect, train, and validate machine learning models using Python, PyTorch, and distributed training frameworks.
• Build feature extraction and data ingestion pipelines with Spark, Airflow, and cloud storage utilities.
• Deploy, monitor, and scale production models on AWS or GCP using Docker, Kubernetes, and managed ML platforms.
• Implement model monitoring to detect data drift, concept drift, and performance degradation in real time.
• Optimize inference latency and memory usage via quantization, distillation, and ONNX Runtime optimizations.
• Write clean, heavily tested code, participate in peer design reviews, and drive engineering best practices.

Requirements

• 3–6 years software engineering experience, with at least 3 years focused on ML engineering.
• Strong Python skills and deep familiarity with PyTorch, Hugging Face, and scikit-learn.
• Hands-on experience deploying and managing containerized ML workloads with Docker, Kubernetes, and cloud services.
• Solid grounding in distributed systems, RESTful API design, and CI/CD pipelines for ML.
• BS or MS in Computer Science, Machine Learning, Statistics, or a related technical field.

Nice to Haves

• Experience with LLM fine-tuning and RAG architectures.
• Vector databases experience.
• Contributions to major open-source ML projects.