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About This Role
Company Overview
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TENEX is an AI\-native, automation\-first, built\-for\-scale Managed Detection and Response (MDR) provider. We are a force multiplier for defenders, helping organizations enhance their cybersecurity posture through advanced threat detection, rapid response, and continuous protection. Our team is composed of industry experts with deep experience in cybersecurity, automation, and AI\-driven solutions. Backed by leading investors, we are rapidly growing and seeking top talent to join our mission of revolutionizing the AI\-Native MDR landscape.
We’re a fast\-growing startup backed by industry experts and top\-tier investors led by Crosspoint Capital Partners and also backed by Shield Capital, DTCP (formerly Deutsche Telekom Capital Partners), Deepwork Capital, and the Florida Opportunity Fund. Seed round led by Andreessen Horowitz (a16z). As an early employee, you’ll play a meaningful role in defining and building our culture. Get in on the ground floor. We’re a small but well\-funded team that just raised a substantial round – joining now comes with limited risk and unlimited upside.
As an AI Enablement Engineer at TENEX, you will partner with our current AI Engineer to build TENEX's internal AI platform: the reusable tools, integrations, and AI work companion that help every TENEX'er do their best work. You will partner with teams across the company to surface needs that shape our platform roadmap, then own that roadmap and ship against it predictably. This role is internal\-facing. Your "customers" are your colleagues, and your impact is measured by how much faster, smarter, and more leveraged TENEX becomes because of the platform you build.
This role is right for someone who finds satisfaction in building systems that scale beyond themselves, who would rather embed AI fluency across the company than be the only person who can do this work.
Culture is one of the most important things at TENEX.AI. Explore our culture deck at culture.tenex.ai to witness how we embody it, prioritizing the irreplaceable collaboration and community of in\-person work.
Location: This role will require Monday through Thursday onsite in our Kansas City office (preferred), with San Jose or Sarasota, FL also considered. WFH Friday. Candidates must live in or be willing to relocate to one of these three cities.
### Job Responsibilities
- Build and evolve TENEX's internal AI platform: a reusable foundation of tools, custom skills, Claude Projects, integrations, and department\-specific specialists that scale AI impact across every team.
- Partner with teams across TENEX (Sales, Customer Success, Marketing, Finance, Legal, HR, Detection Engineering, Security Operations, Forward\-Deployed Engineering) to surface needs that shape our internal AI platform roadmap.
- Own and execute against a published roadmap. Predictable, visible execution is a core expectation of this role.
- Maintain, expand, and refine our AI work companion based on user feedback and emerging needs.
- Build integrations between Claude and our internal tools (e.g., Ashby, Wonderlic, Fireflies, Asana, Google Chat) using webhooks, MCP connectors, and APIs. Favor reusable patterns over one\-off solutions.
- Run enablement: training sessions, documentation, AMAs, and 1:1 onboarding to help every TENEX'er become more AI\-native.
- Contribute to AI governance, monitoring, and security tooling decisions as TENEX scales its AI usage.
### Required Skills \& Qualifications
Anthropic Claude ecosystem expertise
- Has shipped real work using Anthropic's Claude products, including Claude.ai, Claude Code, Cowork, Projects, custom skills, and MCP connectors. Tutorial\-level familiarity is not enough. You should be able to point to things you've built that are actually in use by other people.
- Comfortable using Claude Code for technical work, including building scripts, integrations, and tooling.
- Strong prompt engineering instincts and an ability to translate vague user needs into clear, scoped AI workflows.
- Familiarity with Gemini Enterprise is a plus, as we also use it.
Engineering fundamentals
- 5\+ years of experience in software development. Python is preferred. Comfort with REST APIs, webhooks, and JSON\-based integrations is required.
- Comfortable working across the integration surface: reading API docs, configuring webhooks, debugging auth flows, building lightweight services that connect tools together.
- Familiarity with version control (Git/GitHub) and modern engineering practices.
- Demonstrated ability to design and build for reuse. You see common patterns across requests and abstract them into platform capabilities rather than building one\-off solutions every time.
Security and data handling
- Strong instincts for protecting sensitive data. You understand that internal AI workflows must be designed with strict guardrails around what data they can access, especially when customer information, employee information, or internal IP could be in scope. You build accordingly.
- Demonstrated production experience working with sensitive data (PII, customer data, internal IP, or regulated data). You can speak to specific decisions you've made to protect it.
- Familiarity with AI\-specific security risks: prompt injection, data leakage to third\-party LLMs, unsanctioned tool use, output misuse. You consider these risks proactively, not retroactively.
- Working knowledge of secrets management, secure SDLC practices, and the basics of compliance frameworks (e.g., SOC 2\).
Communication and cross\-functional skills
- Can sit with a CSM, finance lead, recruiter, or executive and translate their workflow into something AI can help with, without condescension, without overengineering, and without losing patience.
- Comfortable presenting to groups: running training sessions, demoing new tools at all\-hands meetings, hosting AMAs.
- Strong written communication. You will write documentation, policy drafts, status updates, and company\-facing announcements.
- Comfortable saying "no" or "not yet" when an idea isn't ready, scoped, or appropriately resourced.
Nice\-to\-have
- Experience building or maintaining an internal AI platform at another company.
- Familiarity with Asana, Atlassian (Jira/Confluence), Google Chat, Google Workspace, Salesforce, Ashby, or other common B2B SaaS tools we integrate with.
- Background in change management, technical writing, or internal enablement.
- Experience with cybersecurity, even at a basic level, since several of our internal workflows involve our security and detection teams.
- Hands\-on experience with AI governance, AI security tooling, or AI usage monitoring platforms.
Soft skills
- Strong consultative instincts. You naturally ask "why" and "what does success look like" before building.
- Curious about other people's work. Energized by helping non\-technical teammates use AI effectively.
- Self\-directed. You will not have someone managing your daily work, and you will be expected to triage, prioritize, and execute independently.
- Bias toward shipping, paired with predictable execution. We move fast, iterate, and deliver against committed timelines.
- Platform mindset. You think about how to make your work multiply through other people rather than scaling only with your own time.
### Education \& Certifications
- Bachelor's degree in Computer Science, Engineering, or a related field, or equivalent practical experience.
- Anthropic certifications, AI/ML certifications, or security\-related credentials are a plus but not required.
Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 3,823 AI roles we're tracking, AI/ML Engineer positions make up 69% of the market. At Tenex.Ai, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $181,170 based on 12,692 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $165,000.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
Tenex.Ai AI Hiring
Tenex.Ai has 5 open AI roles right now. They're hiring across AI/ML Engineer. Positions span Overland Park, KS, US, US, Sarasota, FL, US.
Location Context
Across all AI roles, 15% (590 positions) offer remote work, while 3,217 require on-site attendance. Top AI hiring metros: New York (2,643 roles, $211,000 median); San Francisco (2,168 roles, $253,000 median); Los Angeles (1,792 roles, $191,580 median).
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (112) are outnumbered by mid-level (1,798) and senior (1,516) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $200,100. Top-quartile roles start at $253,500, and the 90th percentile reaches $307,500. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Engineering Manager roles lead at $275,000 median, while Prompt Engineer roles sit at $140,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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