Machine Learning Infrastructure Engineer, Kernel Optimization

About the company

Efference provides high-performance perception and compute infrastructure for training and deploying robot models in the cloud. Our vision is to make robotic deployments scalable with shared compute.

We approach the problem by owning the entire path from the image sensor to the datacenter GPU. We shrink the load at the sensor, fix the uplink so robot traffic is fast and reliable, and clock synchronize the system so observations arrive time-aligned, ready for inference.

About the role

You'll make robot models run efficiently on datacenter GPUs, reducing inference latency and the compute cost of serving a fleet. You'll work from model-level profiling down to GPU kernels, verifying that each optimization preserves the behavior the robot depends on.

What you’ll do

• Profile representative perception and robot-policy workloads, separating kernel execution, memory movement, synchronization, launch overhead, and CPU bottlenecks.
• Implement and optimize CUDA or Triton kernels for expensive operators, using fusion, tiling, suitable layouts, and reduced memory traffic where they help.
• Improve inference graphs and execution paths through compilation, graph capture, allocation reuse, and elimination of unnecessary host-device transfers.
• Evaluate precision and quantization choices with researchers, checking numerical error and closed-loop task performance alongside speed and memory use.
• Benchmark realistic batch sizes, sequence lengths, image shapes, and concurrent sessions with the orchestration team, including tail latency under contention.
• Maintain correctness and performance regression tests across hardware and software versions, and upstream improvements where a shared library is the right home.

What we are looking for

• Experience delivering measured performance improvements in GPU workloads or high-performance ML systems.
• Strong CUDA or comparable GPU programming skills and understanding of memory hierarchies, occupancy, synchronization, and parallel execution.
• Proficiency in C++ and Python and experience with PyTorch or another major ML framework.
• Ability to use profiling tools to identify the limiting resource and choose an optimization that addresses it.
• Careful benchmarking and correctness practice, including handling precision changes and separating microbenchmark gains from application gains.

Nice to Have

• Experience with Triton, CUTLASS, TensorRT, compiler internals, or custom framework operators.
• Work on attention, vision encoders, diffusion or flow models, and small-batch latency-sensitive inference.
• Experience with multi-GPU communication, training kernels, model parallelism, or performance tuning for shared accelerators.