Senior Applied AI Engineer

About us: Axiomatic AI is building a new class of AI systems designed to reason with the rigor of the scientific method. By combining deep learning with formal logic and physics-based modeling, we create verifiable, interpretable AI systems that collaborate with and support human researchers in high-stakes scientific and engineering workflows.  Our mission, 30×30, is to deliver a 30× improvement in the speed, accessibility, and cost of semiconductor and photonic hardware development by 2030.  We aim to revolutionize hardware design and simulation in these industries and are building a team of highly motivated professionals to bring these innovations from research into commercial products. Position Overview  As an Senior Applied AI Engineer, you are the bridge between AI research and production software. You will: Partner with users and internal stakeholders to identify high-impact workflows where AI can help Design and implement LLM-powered product features (agents, tools, prompting strategies) using frameworks like PydanticAI (or similar) Make principled trade-offs between models and approaches (quality, latency, cost, privacy, reliability) Enable AI developers to deploy their work reproducibly and safely Establish engineering standards for applied AI development: testing, reviews, maintainability, and operational readiness Collaborate with backend engineers to integrate AI capabilities into the product Mentor junior AI developers and supervise PRs to ensure high quality and consistent patterns Your mission Applied AI Product Development Own applied AI features end-to-end: discovery → design → implementation → rollout → iteration Translate user feedback into clear technical requirements and pragmatic delivery plans Build LLM workflows such as tool-calling agents, structured output pipelines, retrieval/tool integrations, and safe prompting strategies Iterate quickly while keeping production quality (readability, maintainability, debuggability) Model & Prompt Strategy Select and evaluate LLMs (OpenAI/Anthropic/others) based on real constraints: quality, cost, latency, context limits, and reliability Develop prompt patterns and guardrails (structured prompts, schemas, constraints, fallbacks) Design and run lightweight evaluations to prevent regressions (golden datasets, acceptance criteria, failure-mode testing) Document model decisions and trade-offs in a way that enables other engineers to execute confidently Production Engineering & Quality Write production-grade code: clear abstractions, solid API boundaries, strong typing where appropriate, and consistent error handling Define and enforce testing practices for applied AI (unit tests, integration tests, golden/regression tests) Implement instrumentation appropriate for debugging and iteration (basic logging/tracing/metrics for AI features) Ensure reliability and security basics: rate limiting where needed, safe input handling, prompt-injection awareness, and sensible defaults Collaboration & Enablement Work with AI Developers to productionize their experiments regarding improving user workflows Define workflows: notebook/test repository → PR → staging → production Document AI infrastructure and best practices Review code and mentor AI developers on software practices Key requirements 7+ years of software engineering experience (Python preferred) with strong production ownership Experience with LLMs and AI/ML in production: OpenAI API, HuggingFace, LangChain, or similar (beyond prototypes) Strong software engineering fundamentals: design patterns, code structure, testing practices, and code review habits Cloud infrastructure experience: GCP (Vertex AI preferred) or AWS (SageMaker) API/service development experience (e.g., FastAPI/REST/async programming) and collaboration across teams CI/CD and DevOps: Docker, Terraform, GitHub Actions Monitoring and observability Problem-solving mindset: comfortable debugging complex distributed systems Operating experience with AI deployment in enterprise environment  Excellent communication: able to work directly with users, capture requirements, and explain trade-offs clearly Nice-to-Have Experience fine-tuning or training models Familiarity with LangChain, Pydantic AI or similar frameworks Knowledge of prompt engineering and evaluation techniques Experience with real-time inference and streaming responses Background in data engineering or ML engineering Understanding of RAG architectures Contributions to open-source AI/ML projects What we offer: Competitive compensation Stock Options Plan: Empowering you to share in our success and growth. Cutting-Edge Tools: Access to state-of-the-art tools and collaborative opportunities with leading experts in artificial intelligence, physics, hardware and electronic design automation. Work-Life Balance: Flexible work arrangements in one of our offices with poten