ML Infrastructure Engineer
OpenAI · Senior
Recommended next: your first attempt
Do the Practical coding assessment round.
A complete first attempt shows where you stand before any narrow practice starts.
The loop
Pick a round to see what it asks of you and where you stand on it.
Practical coding assessment
Produce a clear stateful implementation with explicit behavior checks and a defensible performance argument.
Coding · 60 minSet up this round
Not tried yetNo attempt yet.
What strong sounds like
- Specify observable behavior and what must be retained between operations.
- Write complete code with consistent state transitions.
- Manually trace normal, boundary and resumed behavior against expected results.
- Account for time and retained memory, then respond to an extension.
Expect to be pushed on
What happens if progress is restored after the input changes?
Can memory remain bounded over a long run?
Shapes of problem you may get
- One resumable iterator or bounded-state component with incremental requirements
How to prepare
- 01
Define resumable state
Specify the iterator’s stored position, accepted inputs, exhaustion behavior, restart behavior, and restore semantics. Write three expected outcomes before coding.
A reported OpenAI candidate described a resumable iterator exercise. The published guide also emphasizes clear design, code, performance, and verification.
Company researchOpenAI Interview GuideOpenAI SWE candidate: coding, design and technical deep dive
- 02
Trace boundary transitions
Select normal, empty, exhausted, resumed, and invalid-input cases. Trace each state transition by hand, then note the retained memory and time cost.
This directly prepares the stated bar for observable behavior, resumed behavior, boundary checks, and a defensible performance argument.
Suggested approach
- 03
Rehearse collaborative explanation
Briefly explain why the representation keeps state explicit, how you would test it, and how you would respond to a request for a bounded-memory extension. Confirm tool permissions from the preparation materials.
OpenAI reports that tool permissions vary by format. Communication and openness to feedback are published evaluation themes.
Company researchOpenAI Interview Guide
Then try the round
You have a written state model, five expected traces, and one representation decision with time and retained-memory estimates.
Resources to focus on
- OpenAI interview guide | OpenAI
“Skills-based assessment” and “Final interviews”; access is public, and tool rules vary by team.
Use its guidance on clear code, testing, performance, edge cases, communication, and permission checks.
A first attempt takes the full 60 minutes.
What this loop tests
Prepare for practical coding and a final loop of systems, technical depth, and collaboration interviews. For ML infrastructure, center preparation on reliable training and inference: resource scheduling, data movement, checkpoint recovery, utilization, and latency. Strong answers turn ambiguous requirements into a working design, quantify bottlenecks, and connect low-level symptoms to system behavior. Bring a project deep dive and a specific account of learning quickly under pressure.
They reward
- Practical implementation depth
- Quantitative resource reasoning
- Reliable progress under failure
- Fast learning with effective collaboration
What to avoid
- Do not hide state semantics inside a library call.
- Do not claim coverage from one happy-path example.
- Do not optimize an unproven implementation.
- Do not let a new operation silently change existing semantics.
- Do not list ML infrastructure products without defining their role.
How sure we are
Worth redoing if you hear something from the recruiter that contradicts this.
Confirmed · 9
- OpenAI lists pair coding, take-home projects and technical tests as possible skills assessments.
- OpenAI describes final interviews as typically 4–6 hours with 4–6 people across one or two days.
- The engineering guide evaluates solution design, code quality, performance and test coverage.
- The guide explicitly values communication, collaboration, openness to feedback and rapid learning.
Likely · 2
- For ML infrastructure, training/inference architecture and bottleneck diagnosis are appropriate domain specializations of the published technical-depth bar.
- A senior preparation plan should practice durable progress, reproducibility and measured resource utilization rather than generic AI terminology.
Worth asking them
Questions that close the gaps above.
Which bottleneck most limits useful training or inference throughput today?
How does this team divide responsibility between researchers and infrastructure engineers?
What does a successful first infrastructure project look like for this role?
Attempts
Every session saved against this target, newest first. Standings live on the round cards.
Practical coding assessment, Final coding: progressive implementation, Training and inference systems design, Technical project and ML infrastructure depth, Infrastructure failure diagnosis, and Collaboration, learning and mission have no attempts yet.