AI Engineer

AI Engineer — NetSpeek

NetSpeek is the agentic control plane for enterprise physical infrastructure. We govern how AI agents reason about, decide on, and execute actions across enterprise endpoints. Our reasoning and execution layer — Lena — sits in customer production environments, where reliability, observability, and auditability are non-negotiable.

The 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 apply

We prefer applications through our hiring site: https://build.netspeek.ai/roles/ai-engineer

There are three paths there, and we prioritize them in this order:

1. Incident Lab / Case Study —work through a real scenario for this role and send your reasoning with the application. Read first. The scenario gives us the most signal on how you actually think.
2. Field Note —a single written take instead of the scenario. Read second. Faster than the lab, still a strong signal.
3. Standard application —resume and the basics through the site's apply form. Read third. Applying directly through Workable below also works, and we'll still read it. The site's three paths exist because they give us better context for the read.

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.

After an offer

We run standard pre-employment checks before your start date: identity verification, right-to-work confirmation, employment verification, and (where lawful and role-relevant) a criminal record check. We don't run credit checks or online reputation scoring.

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.

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)