# Senior AI Engineer

**Company:** [NetSpeek](http://jobs.workable.com/companies/7Toby5ZjToGjmk1riCkwQB.md)
**Location:** Remote
**Workplace:** remote
**Employment type:** Full-time
**Department:** Engineering

[Apply for this job](http://jobs.workable.com/view/6e7863c5-5a3f-466d-9095-60d6ab6be002)

## Description

### **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

Run the [Incident Lab scenario](https://build.netspeek.ai/lab/lena-was-wrong) for this role, then submit your structured response with the application. Or take the [Field Note path](https://build.netspeek.ai/apply/senior-ai-engineer/field-note) 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](https://build.netspeek.ai/handbook) and [How We Evaluate](https://build.netspeek.ai/principles) 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.[](https://build.netspeek.ai/handbook#pre-employment-checks\).)

## Requirements

### 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

## 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)