Mistral AI는 제품 팀 내에서 AI 모델의 품질을 책임질 AI 엔지니어를 채용합니다. TypeScript 또는 Python을 활용한 프로덕션 환경의 LLM 경험이 필수입니다. 평가 설계, 프롬프트 최적화, A/B 테스트를 통해 제품의 성능과 안정성을 개선하는 역할을 수행합니다. 데이터 기반의 의사결정과 프로덕트 마인드셋을 갖춘 분을 찾습니다.
At Mistral AI, we believe in the power of AI to simplify tasks, save time, and enhance learning and creativity. Our technology is designed to integrate seamlessly into daily working life.
We democratize AI through high-performance, optimized, open-source and cutting-edge models, products and solutions. Our comprehensive AI platform is designed to meet enterprise needs, whether on-premises or in cloud environments. Our offerings include le Chat, the AI assistant for life and work.
We are a dynamic, collaborative team passionate about AI and its potential to transform society.
Our diverse workforce thrives in competitive environments and is committed to driving innovation. Our teams are distributed between France, USA, UK, Germany and Singapore. We are creative, low-ego and team-spirited.
Join us to be part of a pioneering company shaping the future of AI. Together, we can make a meaningful impact. See more about our culture on https://mistral.ai/careers.
Embedded directly in a product team as search, chat, documents, or audio, you'll improve AI-powered features through rigorous evaluation, prompt and orchestration design, and rapid experimentation. You'll own your domain's AI quality end-to-end: define what "good" looks like, measure it, run experiments, and ship what works. Work with Science to deliver measurable improvements to quality, latency, safety, and reliability.
• Design and run evaluations for your product area: reference tests, heuristics, model-graded checks tailored to search relevance, chat quality, document understanding, or audio performance.
• Define and track metrics that matter: task success, helpfulness, hallucination proxies, safety flags, latency, cost.
• Own prompt and orchestration design: write, test, and iterate on prompts and system prompts as a core part of your work.
• Run A/B tests on prompts, models, and configurations; analyze results; make rollout or rollback decisions from data.
• Set up observability for LLM calls: structured logging, tracing, dashboards, alerts.
• Operate model releases: canary and shadow traffic, sign-offs, SLO-based rollback criteria, regression detection.
• Improve core behaviors in your product area, whether that's memory policies, intent classification, routing, tool-call reliability, or retrieval quality.
• Create templates and documentation so other teams can author evals and ship safely.
• Partner with Science to diagnose regressions and lead post-mortems.
• 3-4 years of experience; backgrounds that fit well include ML engineers moving closer to product, or software engineers with real AI/ML production experience.
• Strong TypeScript or Python skills - we have both tracks depending on team fit.
• Production LLM experience: prompts, tool/function calling, system prompts.
• Hands-on with evals and A/B testing; you can design metrics, not just run them.
• Comfortable implementing directly in product code, not only notebooks.
• Observability experience: logging, tracing, dashboards, alerting.
• Product mindset: form hypotheses, run experiments, interpret results, ship.
• Clear communication, autonomous, and oriented toward production impact over experimentation for its own sake.
• Safety systems experience: moderation, PII handling/redaction, guardrails.
• Release operations: canary/shadowing, automated rollbacks, experiment platforms.
• Prior work on search ranking, chat systems, document AI, or audio ML features.
• Introduction call - 30 min
• Hiring Manager interview - 30 min
• Technical Rounds
Live-coding Interview - 45 min
AI Engineering Interview - 45 min
• Culture-fit discussion - 30 min
• References
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