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JAX / accelerator operations engineer
Make model workloads observable and recoverable. Track progress and resource use, diagnose failed or stalled runs and rehearse recovery before relying on it.
The work
A job restarts with the right weights but a different data position. Compare subsequent sample IDs and optimizer steps before accepting recovery.
Preparation
Understand the values that make a training update reproducible. Basic process lifecycle and filesystem knowledge help when rehearsing worker failure, checkpoint acceptance and rollback.
Your portfolio
An incident runbook with a deliberate interruption, recovery evidence and a measured resource budget.
Skills and evidence
Distributed JAX and workload configuration
Required route practice
Explain ownership and replay boundaries; multi-host TPU qualification is separate.
Explain the independent check, repeat it under a changed condition and retain the observed result.
Practice project: stages 1, 2, 3.
Grain, Orbax, and recovery protocols
Required route practice
Distinguish checkpoint requested, completed and accepted before recovery.
Explain the independent check, repeat it under a changed condition and retain the observed result.
Practice project: stages 3.
Profiling, observability, and operational runbooks
Required route practice
Retain a failed process, diagnose the symptom and verify continuation.
Explain the independent check, repeat it under a changed condition and retain the observed result.
Practice project: stages 1, 2, 3.
Containerized delivery and release evidence
Required route practice
Bind approval and rollback to immutable model and evaluation identities.
Explain the independent check, repeat it under a changed condition and retain the observed result.
Practice project: stages 3.
Milestones
Build the shared CPU foundation
A reproducible regression fit with checked gradients and explicit random state.
Run a reference job
A reproducible classifier
Verify a recovery point
An interrupted run restored with its full state
Observe and diagnose workload behavior
A trace-backed before-and-after performance report
Operate a sharded job
A resilient sharded training experiment
Rehearse failure and recovery
An observable local job, a deliberate worker failure and a tested restore/rollback runbook; accelerator transfer needs a separate target run.
Review your readiness
Detect and diagnose a controlled failure, restore full workload state, and explain operational tradeoffs.
- Which signal distinguishes slow progress from a failed or stalled worker?
- How do you know the restored job uses the accepted checkpoint and correct next data batch?
- Which artifact would you roll back to, and what evidence triggers that decision?
- When does a changed artifact invalidate approval, and how do you prove a failed rollout left the previous release active?
This is a learning route for practicing the work, not a claim of job readiness. Use the milestone coverage and a reviewer’s feedback to identify what remains to demonstrate. The capability map below separates required route practice from optional extensions. Public checks and self-reported completion are not independent review or hardware qualification.