Requirements:

Have an understanding of modern ML architectures and an intuition for how to optimize their performance, particularly for inference.
Own problems end-to-end, and are willing to pick up whatever knowledge you're missing to get the job done.
Have at least 3 years of professional software engineering experience.
Are an expert in core HPC technologies: InfiniBand, MPI, CUDA.
Understand how to overlap compute and communication to maximize utilization of scarce compute, memory, and bandwidth resources.
Have experience architecting, observing, and debugging production distributed systems.
Have a humble attitude, an eagerness to help your colleagues, and a desire to do whatever it takes to make the team succeed.
Have needed to rebuild or substantially refactor production systems several times over due to rapidly increasing scale.
Are self-directed and enjoy figuring out the most important problem to work on.
Have a good intuition for when off-the-shelf solutions will work, and build tools to accelerate your own workflow quickly if they won’t.

What You'll Be Doing:

Work alongside machine learning researchers, engineers, and product managers to bring our latest technologies into production.
Introduce new techniques, tools, and architecture that improve the performance, latency, throughput, and efficiency of our deployed models.
Build tools to give us visibility into our bottlenecks and sources of instability and then design and implement solutions to address the highest priority issues.
Optimize our code and fleet of Azure VMs to utilize every FLOP and every GB of GPU RAM of our hardware.

Competitive compensation range: $200K - $370K
Equal opportunity employer
Commitment to providing reasonable accommodations to applicants with disabilities