> Markdown version of [/jobs/ext/1119653-ai-deployment-engineer-startups](https://www.wearedevelopers.com/jobs/ext/1119653-ai-deployment-engineer-startups). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # AI Deployment Engineer - Startups - **Company:** SquareMileConsulting - **Location:** UK - **Contract:** Permanent contract - **Skills:** Artificial Intelligence, Software Debugging, Software Deployment, Software Engineering, Reinforcement Learning - **Published:** July 2, 2026 - **Apply:** https://www.totaljobs.com/job/deployment-engineer/squaremileconsulting-job107624739 ## About the Role Have strong software engineering & AI fundamentals. For example, experience as a startup CTO, software engineer, ML engineer, Data Scientist or equivalent. Experience shipping production systems end-to-end is a strong plus. Have experience as a technical founder, or engineer at an early stage startup Have familiarity with, or interest in, model training pipelines and reinforcement learning. Have experience building AI applications, agents, or evaluation systems, and can reason clearly about model behavior in complex workflows. Are comfortable working directly with highly technical users and translating their challenges into concrete technical signals. Can move fluidly between prototyping, debugging, evaluation design, and cross-functional collaboration. Communicate clearly across technical and non-technical audiences. Bring high agency, strong product sense, and a bias toward building durable improvements rather than one-off fixes ## Description The AI Deployment Engineering team works closely with frontier startups. We are trusted advisors to, and thought partners with, startups to ensure that OpenAI's technology is deployed safely and effectively, whilst also partnering with engineering, research, and product to turn those insights into evaluation systems, product improvements, and better model behavior. This team sits at the intersection of customer reality and model quality. We combine hands-on technical depth with strong product judgment, helping translate complex, high-value use cases into clear signals that can improve both the customer experience and the underlying systems. About the role We are seeking a technically proficient, product-minded engineer to help push the frontier of advanced AI with our strategic startup customers. You'll work with some of the most exciting AI startups in the world, helping them optimize their own systems and turning those learnings into durable improvements across OpenAI's research and products. You will partner deeply on complex workflows, identify the gaps that matter, and help transform those gaps into reproducible evaluations, technical insights - helping shape OpenAI's research and product direction. This role is well suited to engineers who are equally comfortable debugging a workflow, iterating on prompts or agents, designing evaluations, and collaborating across research and product. You should be excited by ambiguous, high-impact problems and motivated by the opportunity to shape how advanced AI systems improve in practice. This role is based in London. We use a hybrid work model of 3 days in the office per week. We offer relocation assistance. In this role, you will: Work directly with strategic startup customers to understand critical workflows, uncover failure modes, and identify high-impact opportunities for improvement. Prototype and iterate on prompts, agents, and workflow designs to better understand system behavior and unlock customer value. Synthesize and deliver valuable feedback to the Product and Research teams, turning real usage patterns into clear, reproducible evals, benchmarks, and technical artifacts that improve model and product quality and ensure customer-grounded learnings influence roadmap and model development. Build repeatable tools, patterns, and evaluation approaches that raise the quality bar across multiple use cases. Operate with strong judgment in ambiguous environments, balancing immediate technical problem-solving with longer-term system improvement. 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