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
- Own applied AI features end-to-end: discovery 14 design 14 implementation 14 rollout 14 iteration
- 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
- Collaboration & Enablement
- Experience collaborating with software engineers
- Work with AI Developers to productionize their experiments regarding improving user workflows
- Define workflows: notebook/test repository 14 PR 14 staging 14 production
- 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, 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)CI/CD and DevOps: Docker, Terraform, GitHub 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 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 potential options for remote work.
- Professional Growth: Opportunities to attend industry conferences, present research findings, and engage with the global AI research community.
- Impact-Driven Culture: Join a passionate team focused on solving some of the most challenging problems at the intersection of AI and hardware.
Work model & location expectations:Hybrid from Boston office, 3x/week
