Machine Learning Engineer

Designing and implementing machine learning models for production deployment

Optimizing model inference performance for latency and throughput requirements

Building scalable distributed systems for training and serving machine learning models

Working with GPU and specialized hardware acceleration for model training and inference

Profiling and debugging performance bottlenecks in machine learning systems

Implementing and optimizing neural network architectures and training algorithms

Architecting end-to-end machine learning systems from data collection to model serving

Developing data pipelines and ETL workflows for model training and evaluation

Managing production ML systems and responding to incidents in real-time environments

Implementing monitoring, observability, and alerting systems for ML infrastructure

Leveraging large language models and foundation models for downstream applications

Building and deploying agentic AI systems and autonomous agents

Implementing retrieval-augmented generation and semantic search systems

Optimizing model quantization and compression techniques for efficient inference

Implementing reinforcement learning and alignment techniques for model training

Cross-functional collaboration with research, product, and infrastructure teams

Communicating technical decisions and providing architectural vision to stakeholders

Defining and measuring quality metrics for machine learning system performance

Breaking down complex, open-ended problems into manageable engineering strategies

Mentoring junior engineers and promoting best practices in machine learning development