Senior Applied AI Engineer Job Cambridge area,Massachusetts USA,Software Development

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 a 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
Monitor, evaluate and optimize AI and agentic workflows
Collaborate with specialist and scientist in bringing their expertise into the AI product

Your mission

Applied AI Product Development
Translate user feedback and behaviour 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
Experience in shipping data projects to real users
Experience collaborating with software engineers
Work with AI Developers to product ionize their experiments regarding improving user workflows
Document AI infrastructure and best practices
Review code and mentor AI developers on software practices
Communication skills: able to precisely and concisely justify decisions and explain technical concepts to colleagues and stakeholders.

Key requirements

7+ years of software engineering experience (Python preferred) with strong production ownership
Experience with LLMs and AI/ML in production:
OpenAI API, Hugging Face, Lang Chain, 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 (Sage Maker)
CI/CD and Dev Ops:
Docker, Terraform, Git Hub Actions
Monitoring and observability
Problem-solving mindset

Operating experience with AI deployment in enterprise environment
Proactiveness and versatility:
The role requires being comfortable with defining and scoping tasks independently as well taking the initiative and finding ways to improve

Nice-to-Have

Experience fine-tuning or training models
Familiarity with Lang Chain, 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…