Machine Learning Engineer

We're working with a well-funded early-stage AI startup building cutting-edge machine learning systems at the intersection of large language models, distributed training, and production AI infrastructure.

This is an opportunity to join a highly technical team where engineers work across the full machine learning lifecycle, from large-scale data generation and model training through deployment, optimization, and production infrastructure. The team operates at the boundary of research and engineering, giving engineers the opportunity to contribute to new ideas while building systems that directly power real-world AI products.

What You'll Be Working On

Building scalable data pipelines to collect, process, and generate large synthetic datasets for machine learning

Developing infrastructure for distributed multi-GPU model training

Profiling and optimizing model training and inference performance

Deploying and maintaining high-throughput inference systems for large language models

Working closely with researchers to translate new ideas into reliable production systems

Building tooling that supports the complete machine learning development lifecycle, from experimentation through deployment and monitoring

Contributing to technical research, experimentation, and engineering best practices

We're Looking For Someone Who Has

Bachelor's or Master's degree in Computer Science or a related technical discipline

Strong Python programming skills and experience with modern machine learning frameworks

Solid understanding of transformer architectures and large language models

Experience building production-quality machine learning systems

Comfortable working across both research and engineering environments

Strong software engineering fundamentals and systems thinking

Nice to Have

Experience with GPU programming and performance optimization

Familiarity with distributed training frameworks such as DeepSpeed, FSDP, Ray, or similar technologies

Experience serving large language models using modern inference frameworks

Experience building large-scale data processing pipelines using technologies such as Spark, Beam, or similar distributed systems

Familiarity with workflow orchestration tools

Experience with experiment tracking, MLOps, and production ML workflows

Knowledge of cloud infrastructure and modern DevOps practices

Experience designing scalable AI infrastructure supporting production machine learning workloads

Why Join

Work on technically challenging problems at the intersection of AI research and production engineering

Significant ownership across the full machine learning lifecycle

Opportunity to influence architecture, infrastructure, and model development

Collaborative environment where engineering and research work closely together

Join a small, high-performing team building next-generation AI systems from the ground up