Backed by a16z.
About the role
You will build the platform our ML team works on. Training runs, datasets, experiments, compute, and the path from a checkpoint to a model running on a robot all sit on infrastructure someone has to own. The measure of this role is iteration speed. When an ML engineer has an idea, how long until they know whether it worked. Found on 1752vc Careers, the job board for startup and VC roles.
What they're looking for
- Strong software engineering with depth in distributed systems and data infrastructure. Python, plus Go, Rust, or C++
- Experience building ML platforms that other engineers depended on daily
- Hands-on GPU cluster operations: provisioning, drivers, interconnect, and diagnosing hardware and network failures under load
- Experience with large-scale data infrastructure: object storage, columnar formats, and pipelines handling terabytes or more
- Familiarity with cluster scheduling: Kubernetes, Slurm, Ray, or equivalent
- Track record of reducing the time between an idea and a result
More about this role
You will build the platform our ML team works on. Training runs, datasets, experiments, compute, and the path from a checkpoint to a model running on a robot all sit on infrastructure someone has to own.
The measure of this role is iteration speed. When an ML engineer has an idea, how long until they know whether it worked.
Own the data platform: storage, versioning, and lineage for large multimodal datasets, so any model can be traced back to the data that produced it.
Build dataset assembly and serving, including the interfaces ML engineers use to slice, filter, and load training data.
Own our ML compute, on-premise and cloud: cluster provisioning, GPU node health, drivers, networking, and storage.
Own scheduling and quota across the team, so compute is allocated sensibly and utilization stays high.
Keep the cluster reliable, including monitoring, failure recovery, and being the person who figures out why a multi-day run died.
Own training orchestration: job scheduling, multi-node runs, checkpointing, and recovery.
Build experiment tracking and comparison, so results are reproducible and a run from six months ago can be rerun.
Own the model registry and the release path onto...
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