The OpenAI ML Engineer process typically includes 5-6 rounds: a recruiter screen (30 min), a technical phone screen covering ML fundamentals and coding (60 min), a machine learning system design interview (60 min), a deep-dive ML coding round (60 min), a values and safety alignment interview (45 min), and a final cross-functional loop. Expect a higher bar on both ML theory and engineering execution than a typical industry ML role — OpenAI works at the frontier.

Core requirements: deep understanding of neural network architectures (Transformers, attention mechanisms, scaling laws), PyTorch proficiency at production level (custom CUDA kernels, distributed training with FSDP/DeepSpeed, mixed precision), strong software engineering skills (systems design, clean code, testing), and experience with ML training infrastructure at scale. RLHF, preference learning, and safety-relevant ML techniques are highly valued. Familiarity with inference optimisation (quantisation, speculative decoding, KV-cache management) is increasingly important.

Review Transformer architecture deeply — attention, positional encoding, layer normalisation, scaling behaviour. Study distributed training patterns (data parallelism, model parallelism, tensor parallelism, pipeline parallelism) and their trade-offs. Practise implementing ML components from scratch in PyTorch. Read OpenAI's key papers (GPT series, InstructGPT, RLHF, Codex) and be ready to discuss the engineering decisions they describe. Be prepared to reason about numerical stability, memory optimisation, and throughput vs latency trade-offs.

OpenAI ML Engineer compensation (2025 data): ML Engineer: $220k–$320k base, $500k–$900k total; Senior ML Engineer: $280k–$380k base, $700k–$1.2M+ total. Packages include significant profit interest units (equity), performance bonuses, and comprehensive benefits. OpenAI competes aggressively with DeepMind, Anthropic, and Google Brain for ML talent.

Standout candidates combine research-level ML understanding with strong production engineering skills — they can implement and optimise the systems that train and serve frontier models. They show genuine curiosity about ML safety, demonstrate understanding of OpenAI's research direction, and can reason clearly about the engineering trade-offs involved in training large models responsibly. Open-source contributions to ML infrastructure (PyTorch, Triton, DeepSpeed) or published research are strong differentiators.