The process typically includes: recruiter screen (30 min), technical phone interview with coding (45-60 min), take-home coding assignment or live system design (2-4 hrs), onsite virtual loop with 4-5 rounds: two coding rounds (algorithms/data structures), one system design round (distributed systems or ML infrastructure), one behavioral round, and sometimes a safety/values alignment discussion. The entire process can take 3-5 weeks.

OpenAI primarily uses Python for ML, data pipelines, and API services; C++ for performance-critical training and inference code; and TypeScript/JavaScript for web products (ChatGPT). Triton and CUDA are used for GPU kernel development. Most SWE roles require strong Python — systems roles also require C++ or Rust experience.

Common topics include: designing large-scale inference serving systems for LLMs, distributed training pipeline architecture, high-throughput API gateway design, real-time streaming systems (for ChatGPT-style token streaming), data ingestion pipelines for pretraining, and observability systems for AI models. Questions blend classical distributed systems knowledge with ML-specific requirements.

Similar difficulty to FAANG (LeetCode medium/hard level), but with an additional emphasis on writing clean, production-quality code rather than just arriving at a solution. OpenAI interviewers may ask you to walk through error handling, testing strategies, and code maintainability. ML-adjacent coding problems (gradient descent, tokenization, vector operations) appear occasionally.

OpenAI SWE compensation (2025 data): L3 (junior): $150k-$200k base, $250k-$400k total; L4 (mid): $190k-$260k base, $350k-$600k total; L5 (senior): $240k-$320k base, $600k-$1M+ total. OpenAI offers significant equity (includes profit participation units) which represents substantial upside. Total compensation has risen sharply as the company's valuation has grown.

Yes, though it varies by team. Safety-adjacent teams (policy, research, model behavior) have explicit safety alignment discussions. Product/infrastructure teams typically include one behavioral question about how you think about responsible development. OpenAI increasingly expects engineers to understand the safety implications of their systems, especially those that affect model output or user interactions.

The typical timeline is 4-7 weeks from first contact to offer. Recruiter screens are usually scheduled within 1-2 weeks. The onsite loop happens within 2-3 weeks of the phone screen. Offer and compensation negotiation can add 1-2 weeks. Senior and staff roles may take longer due to additional calibration rounds.

Active hiring areas include: Model Training Infrastructure (scaling training runs), Inference and Serving (optimizing GPT-4/o deployment), ChatGPT product engineering, API Platform (developer tools, SDKs, docs), Safety Systems (content filtering, jailbreak prevention), and Research Engineering (prototyping new model capabilities). Teams vary significantly in culture and day-to-day work.