Senior AI Engineer

Senior 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 Senior AI Engineer owns Lena's reasoning layer end-to-end: retrieval, grounding, evaluation, and the boundary between AI suggestions and governed actions.

What you'll work on

• Designing and improving RAG pipelines for grounding Lena's diagnostic reasoning in structured operational telemetry, device state, and product documentation.
• Building evaluation harnesses that measure groundedness, hallucination, refusal calibration, and action accuracy on every release.
• Setting the boundary between Lena's probabilistic reasoning and the platform's deterministic action layer - what she's allowed to do, when, and under what audit.
• Owning AI cost and latency budgets per workflow.
• Partnering with backend (.NET) and platform engineers to land changes safely.

You're a fit if

• You have 5+ years of ML / applied AI engineering experience.
• You've built and shipped production LLM systems (RAG, agents, structured outputs, evaluations) at a B2B SaaS company.
• You've owned a production RAG system end-to-end.
• You've built evaluation pipelines that ran on every release and caught real regressions.
• You've worked at a growth-stage AI-native SaaS company where AI was the primary product.
  

You probably aren't a fit if

• Your AI exposure stops at experimentation or coursework.
• You haven't deployed AI systems to customer production environments.
• You want a process-heavy environment where decisions go through committees.

How to apply

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

• 5+ years in machine learning engineering, or 5+ years combined across ML and applied AI systems
• 2+ years building and shipping LLM-powered systems in a growth-phase AI SaaS company where AI was the product, not a side feature
• Hands-on experience with RAG systems including vector databases, embedding tuning, and retrieval optimization
• Experience building evaluation pipelines for LLM performance, hallucination, and reliability in production
• Strong Python with production-level ML system implementation
• Track record operating under real product constraints: latency, cost, observability, and safety
• Comfortable being accountable for AI behavior in production

Strong signal

• Designed agentic workflows with measurable performance improvements
• Worked at AI-native startups that scaled from early traction to growth stage
• Reduced hallucination and improved grounding in production systems
• Cost optimization at scale, including token modeling and caching strategies
• Familiarity with compliance-aware AI logging and enterprise audit requirements
• Defined evaluation pipelines before feature release rather than after

Not the right fit if

• Your LLM experience is limited to experimentation, side projects, or non-production systems
• You are a backend engineer looking to pivot into AI
• Your background is research-focused without production ownership
• Your AI work has not operated under real customer impact

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)