Machine Learning Engineer

About The Role

The role owns the architecture, development, and scaling of machine learning systems that power core product features and data-driven infrastructure.

The team operates at the intersection of applied research and production engineering, turning complex algorithms into reliable, high-throughput microservices.

Key Responsibilities

• Design and implement scalable machine learning pipelines in Python, PyTorch, and Apache Spark to process large-scale datasets
• Deploy and manage models in production using cloud infrastructure like AWS, Docker, and Kubernetes with strict latency and throughput SLAs
• Collaborate with data scientists and software engineers to transition experimental models into robust, maintainable production code
• Optimize model inference performance through quantization, distillation, and hardware acceleration techniques
• Implement comprehensive monitoring, logging, and alerting systems to track model drift, latency spikes, and system health
• Contribute to technical design reviews and establish engineering best practices across the team

What We Are Looking For

• 3-6 years of professional experience in software engineering with a strong focus on machine learning systems and MLOps
• Proficiency in Python and deep familiarity with ML frameworks such as PyTorch or TensorFlow
• Hands-on experience building and maintaining containerized applications and services using Docker, Kubernetes, and CI/CD pipelines
• Solid understanding of distributed computing paradigms and cloud platforms (AWS, GCP, or Azure)
• BS or MS in Computer Science, Machine Learning, or a related technical field
• Bonus: Experience with LLM orchestration frameworks, vector databases, or real-time streaming architectures such as Kafka