Member of Technical Staff, Training Infra

The Role

We re looking for engineers and scientists to design, optimize, and maintain the core systems that enable scalable, efficient training of LLMs. Your goal is to make experimentation and training at Inception fast and reliable so our team can focus on science, not system bottlenecks.

• Design, implement, and optimize distributed training systems that scale across thousands of GPUs and nodes.
• Develop high-performance optimizations to maximize throughput and efficiency.
• Develop reusable frameworks and libraries to improve training reproducibility, reliability, and scalability for new model architectures.

• BS/MS/PhD in Computer Science, Engineering, or a related field (or equivalent experience).
• Understanding of ML frameworks (PyTorch, Tensor Flow) from a systems perspective.
• Strong engineering skills — ability to contribute performant, maintainable code and debug in complex codebases.
• Proficiency in Python and at least one systems programming language (C++/Rust/Go).
• Experience with containerization (Docker), orchestration (Kubernetes), and CI/CD pipelines.

• Experience building and maintaining large-scale language models with tens of billions of parameters or more.
• Experience with ML workflow orchestration tools (Kubeflow, Airflow).
• Background in performance optimization and profiling of ML systems (Prometheus, Grafana, Open Telemetry).
• Familiarity with distributed frameworks such as PyTorch/XLA, Deep Speed, Megatron-LM.