Research Engineer

• Algorithms: Conduct exploratory research and drive algorithmic and architectural advances on the critical path to AGI. Algos has been responsible for a number of high profile OpenAI releases including GPT-2, Image GPT, Jukebox, DALL-E.
  
  
• Alignment: Fine-tune large language models to perform tasks in accordance with the user's intentions. Explore learning from human feedback and assisting humans evaluating AI. Recent projects: InstructGPT, Book summarization
  
  
• Applied AI Research: Develop innovative solutions and open-ended research to solve real-world problems. Care about applications driven by user feedback and long-term research ​​with significant impact on products, while keeping a high safety product standard. Recent project: Text and Code Embeddings in OpenAI API
  
  
• Code Generation: Research and develop AI programmers, the neural models that write, debug and improve computer programs. Creating AI which can solve hard symbolic reasoning problems is one of the most difficult problems in modern deep learning and you can attack it head-on. Seek new ways for increasingly powerful AI systems to interact with the world through a very general interface of computer code. Recent project: Codex that powered the creation of GitHub Copilot.
  
  
• Language: Develop GPT advancements by exploring and forecasting future capabilities and resource needs. Seek a deep understanding of the scaling of our models across multiple orders of magnitude to optimize for best-case performance at the largest scale. Value impact over novelty – Team has found outstanding results from solid engineering with good design, implementation, and benchmarking.
  
  
• Mathgen: Help models learn to solve problems in informal natural text and prove theorems in formal languages like Lean. Large language models are known to make false claims that sound plausible, while mathematics requires the utmost rigor so training models that can reason robustly is a critical step on the path to AGI. Recent projects: Training Verifiers and Solving Olympiad Problems.

• Policy Research: Measure the risks, benefits, and overall impact of our technology on the world with an eye towards informing our policies and those of other institutions. Develop novel ways of characterizing model properties, work with internal and external partners to measure our economic impact, and build novel mitigations that help us safely deploy models in an iterative fashion. Conduct far-sighted research on ideal governance and regulation of powerful AI systems.

• Scaling: Build the model training software stack, solving problems at all layers of the stack including iteration speed, observability, compute efficiency, correctness, and fault detection and recovery. Scaling owns the engineering and research required to harness custom-built hyperscale supercomputers, the latest algorithmic improvements, and massive datasets to train AI models of unprecedented capability.
  
  
• Science of Deep Learning: Explore and understand the dynamics of training large models to guide both the training of current models and the trajectory of the next ones.

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We expect you to:

• Have strong programming skills
• Have experience working in large distributed systems
• Be excited about OpenAI’s approach to research

Nice to have:

• Interested in and thoughtful about the impacts of AI technology
• Past experience in creating high-performance implementations of deep learning algorithms