Machine Learning Engineer

About The Role

The role owns the full lifecycle of machine learning systems, from designing experimental architectures and training high-performance models to deploying scalable inference endpoints in production environments.

The team collaborates closely with data scientists, backend engineers, and product managers to ensure models meet strict latency, throughput, and accuracy requirements for millions of daily users.

Key Responsibilities

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

What We Are Looking For

• 3–6 years of software engineering experience, with at least 3 years focused specifically on machine learning engineering
• Proficiency in Python and deep familiarity with core ML libraries such as PyTorch, Hugging Face, and scikit-learn
• Hands-on experience deploying and managing containerized ML workloads using Docker, Kubernetes, and cloud services
• Solid grounding in distributed systems, RESTful API design, and CI/CD pipelines for machine learning
• BS or MS in Computer Science, Machine Learning, Statistics, or a related technical discipline
• Bonus: Experience with LLM fine-tuning, RAG architectures, vector databases, or contributing to major open-source ML projects