Who you are

- 7+ years of professional software engineering experience, with deep, recent, multi-year Python and strong relational database and schema design skills

- Solid CS fundamentals and a demonstrated track record of owning complex systems end to end, from design through production reliability

- At least 1 year of hands-on experience building with modern LLMs (as an IC)

- Real RAG depth: hybrid search (keyword plus vector), re-ranking or fusion methods, and grounded citations, tuned in production rather than read about

- Hands-on with at least one of LangChain, LangGraph, or LlamaIndex

- Vector databases beyond pgvector (Pinecone, Qdrant, Milvus)

- Built end-to-end agentic data-processing systems (crawling, dedup, structuring) with whole-system ownership

- Evaluation and observability for AI systems: golden datasets, precision vs. recall, LLM-as-judge, and drift monitoring

- Ruby on Rails (our core platform is on Rails), deep SQL, and experience with AWS or GCP, Docker, and CI/CD

- Startup experience and comfort operating in fast, scrappy, low-process environments

What the job involves

- You'll join our AI Engineering team as a Senior Engineer embedded in one of our product pods, reporting to our AI Engineering Lead

- You'll own AI features for grant discovery end to end, from the data backbone that crawls and structures messy source data, through the RAG and agentic systems that match grants to nonprofits, to production deployment and ongoing evaluation

- It's a hands-on, high-ownership seat with direct access to founders and real room to grow as the engineering team scales!

- Build tool-using LLM systems that plan, call tools, and run multi-step workflows for tasks like grant discovery, data ingestion, and research assistance





- Build agentic data-processing pipelines that crawl the web, pull and dedupe messy source data at scale, and structure it into clean, queryable databases other teams build on

- Turn prototypes into resilient production services with explicit fallback, cost, and latency budgets

- Own RAG end to end: ingestion, chunking and embedding strategy, hybrid retrieval, re-ranking, citations, and grounding

- Build ranking and scoring systems that match grants to nonprofits, universities, and foundations using complex relevance techniques

- Continuously improve recall and precision and keep indices healthy as the dataset grows

- Stand up evaluation and observability so our AI is grounded, safe, and cost-effective, and treat LLM behavior as non-deterministic by design rather than as a regular API

- Partner directly with founders and your pod on undefined, complex problems with real autonomy

- Write clear, maintainable, well-tested code and build reusably so your work expands across teams

Benefits

- Competitive salary: We offer a competitive salary as part of a comprehensive benefit package including a 401k

- Equity: We're doubling year over year. This means if we keep executing, the company (and your equity) will be worth a lot

- Health insurance: 100% coverage of employee health & dental insurance plans and 50% for dependents

- Paid time off: Generous PTO policy, including parental leave, so you can rest and recharge

- Office setup: Company laptop + $500 to set up your home workstation

- Investment in you: We help fund your professional development by providing coaching and education

- Remote: The team is fully distributed (read: no office!)

- Extras!: In-person retreats, surprise food deliveries, and games to add zest to your work life

📌 Senior Software Engineer (Canada)
🏢 Instrumentl
📍 Canada