AI Engineer - NetSpeekThe AI Engineer builds the retrieval, evaluation, and workflow plumbing that makes Lena reliable. You'll work alongside the Senior AI Engineer on the reasoning layer, and across the team on integration into the broader platform.
What you'll work on- Building and tuning RAG components: chunking strategy, retrieval scoring, prompt scaffolding, grounding heuristics.
- Wiring evaluation harnesses that catch regressions on every release.
- Working on the Python services that mediate between Lena and the rest of the platform.
- Working with backend (.NET) and frontend (React) engineers to land AI-affecting changes safely.
You're a fit if- You have 1+ years of software / ML engineering experience.
- You're advanced in Python.
- You've shipped Python code that real users relied on (production or internal stakeholders).
- You've worked hands-on with LLMs, RAG, or AI workflows beyond tutorials.
- You're comfortable with REST APIs and JSON-based integrations.
You probably aren't a fit if- Your AI work is mostly coursework or side projects without production exposure.
- You want a heavily structured environment with predefined work queues.
How to applyRun the Improve the RAG for this role, then submit your structured response with the application. Or take the Field Note path as a single essay question instead.
Either path is read by a human on our hiring team. No AI scoring, no auto-rejection.
Read the Engineering Handbook and How We Evaluate before applying.
Requirements
Must-have
- 1-3 years of software engineering, ML engineering, or applied AI experience.
- Hands-on exposure to LLMs, RAG systems, or AI workflows beyond tutorials or coursework.
- Strong Python fundamentals; you have shipped Python that other people relied on.
- Comfort with REST APIs, JSON structures, and modern SaaS systems.
- Familiarity with prompt engineering and modern AI tooling.
Strong signal
- Designed agentic workflows with measurable performance improvements.
- Exposure to AI-native startups that scaled from early traction to growth stage.
- Reduced hallucination and improved grounding in production.
- Cost optimization at scale, including token modeling and caching strategies.
- Familiarity with compliance-aware AI logging and enterprise requirements.
Benefits
We are growth-stage and fully remote, not late-stage. We invest in the work, the tools, and the people, not the manifesto.
What that looks like in practice:
- Flexible / unlimited time off
- Health insurance
- Equity participation, discussed at offer
- Fully remote
- Architectural ownership of work that ships to real enterprise customers
- Direct working relationships with the people setting platform strategy
- A growth-stage platform where the decisions you make in your first year shape the product for years
- AI-assisted tooling licensed by NetSpeek (Cursor, Claude Code, GitHub Copilot, or comparable)
