Tesla ML Platform Engineer interview questions

Count gradient traffic from a named dtype, separate initialization and buffer broadcasts, and measure the reduction tail instead of assuming it is hidden.

Follow the weight bytes through a small decode batch and a long prefill. A scoped roofline calculation explains the difference without pretending to predict actual latency.

Choose between supported InfiniBand and RoCE designs using workload tests, congestion behavior and a complete cost model.

Follow a token into retained attention state, derive bytes per position for a 70B model, and distinguish present cache usage from the room needed to finish a response.

Work from each GPU's cache budget, reserve room for generated tokens, then check latency separately. A limiting-rank example shows why fitting today's prompts is not enough to admit tomorrow's work.

Compute MFU from a declared global token count and time interval. Check the accounting before judging the percentage or choosing an optimization.

Work from token timestamps to first-token wait, average pace and individual stalls. Then count requests meeting both latency limits, keeping failures and measurement boundaries explicit.

Draw query-head sharing, count actual stored state and compare payloads with explicit model geometry and binary units.

Use the Llama 3 interruption record without turning job interruptions into device lifetime, predictable failures or a universal spare percentage.

Trace one cache position through a block table, calculate the memory recovered from oversized reservations, and explain why sharing still needs careful writes and matching.

Separate weight storage, matrix arithmetic and attention-cache precision. Choose a supported candidate using task quality, memory and service measurements.

Handle an inaccessible GPU with bounded recovery and preserved evidence, without treating a reboot or repeat count as proof of root cause.

Use DCGM fields with their actual semantics, combine device health with workload progress, and calibrate alerts on correlated fleet data.

Separate the all-reduce result from NCCL execution choices, and compare latency models without treating them as universal selection rules.

Compare FSDP2 and ZeRO-3 through one parameter group’s lifetime, a scoped traffic model and a save-resume experiment, rather than a universal speed ranking.

Count tensor-parallel messages and compare their payload service time on two explicit paths. Explain why TP8 is a common placement choice, not a fixed ceiling.