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