Research lab and product company building the platform for continual learning. Backed by Bessemer and Conviction.
About the role
As an ML Infrastructure Engineer at Trajectory, you will develop algorithms and own the innermost loop of our continual learning platform: the ML training stack! This role spans training, inference, and kernels. Bring deep expertise in at least one area and curiosity across the stack. We’ll shape your initial ownership around your strengths and have high opportunities for you to work across the whole ML training platform. Found on 1752vc Careers, the job board for startup and VC roles.
What they're looking for
- Strong fundamentals in distributed systems, networking, storage, and failure recovery, with experience shipping demanding systems
- Required deep specialization in one of training infrastructure, inference systems, or GPU kernels, supported by systems built or measurable optimizations delivered
- Strong Python skills and languages relevant to your specialty, such as C++, CUDA, or Triton
- Understanding of PyTorch, JAX, or comparable framework internals, with strong profiling and debugging skills
- Ownership and clear communication: work closely with researchers and deliver measurable performance gains while preserving correctness and reliability
- We value demonstrated capability over credentials
More about this role
As an ML Infrastructure Engineer at Trajectory, you will develop algorithms and own the innermost loop of our continual learning platform: the ML training stack!
Our team’s goal is to improve the training stack by 10× every month across GPU scale, model size, throughput, memory efficiency, caching, and latency.
This role spans training, inference, and kernels. Bring deep expertise in at least one area and curiosity across the stack. We’ll shape your initial ownership around your strengths and have high opportunities for you to work across the whole ML training platform.
Build and optimize distributed training and RL infrastructure, including rollout execution, GPU scheduling, training parallelisms, checkpointing, and recovery. Improve training experimentation throughput and shorten research iteration cycles is a key part of training 100B+ parameter models here.
Optimize serving for production agents and training rollouts. Improve batching, scheduling, and KV-cache management while balancing latency, throughput, cost, and model quality.
Develop and optimize GPU kernels and runtimes using CUDA, Triton, or comparable tools. Improve memory use and execution efficiency, preserving...
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