Startups.Com interview questions

Showing 1–3 of 3 results

• Debug and scale a PyTorch training loopMedium

  This question evaluates debugging and performance engineering skills for deep learning training pipelines, including diagnosing convergence and instability issues in PyTorch, scaling training with Fully Sharded Data Parallel (FSDP), designing sparse gradient all-reduce communication patterns, and judging when to implement custom CUDA/Triton kernels. Commonly asked for Machine Learning Engineer roles within the Software Engineering Fundamentals domain, it assesses both conceptual understanding of numerical stability and distributed systems and practical application skills in configuring multi-GPU training, communication trade-offs, and low-level performance and correctness validation.

  Startups.ComMachine Learning EngineerSoftware Engineering FundamentalsOnsite
• Design efficient Transformer inference with KV cacheMedium

  This question evaluates an engineer's competency in ML systems engineering, specifically understanding of decoder-only Transformer inference, KV cache semantics and which tensors are cached per layer, attention mechanics, memory layout, and correctness under branching and long-context workloads.

  Startups.ComMachine Learning EngineerML System DesignOnsite
• Implement common neural network layers in PyTorchMedium

  This question evaluates proficiency in implementing core neural-network primitives (ReLU, numerically-stable Softmax, LayerNorm, BatchNorm with running statistics, and RMSNorm), numerical stability and normalization concepts, and understanding of autograd-compatible tensor operations.

  Startups.ComMachine Learning EngineerCoding & AlgorithmsOnsite