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About This Role
Company Overview
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 a Senior AI/ML Engineer at TENEX, you will be a senior technical leader and architect responsible for designing, developing, and optimizing scalable, high\-performance AI systems. You will play a crucial role in implementing our AI\-driven cybersecurity solutions while collaborating across engineering teams and contributing to technical innovation.
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 \- Thursday onsite in any of our locations. WFH Friday.
Job Responsibilities
- Project Execution: Lead and own the architecture and delivery of technical components of complex projects. This means communicating effectively to align on requirements, executing on high\-quality code, and collaborating with senior engineers and stakeholders throughout the development lifecycle.
- AI Layer Engineering: Design \& build the AI layer that powers autonomous detection, RAG\-backed investigation, and auto\-remediation workflows.
- Productionize Reasoning Engines: Develop and productionize large\-scale LLMs, graph\-based reasoning engines, and streaming feature pipelines that operate on billions of security events.
- Evaluation \& Reliability: Own evaluation \& reliability—from prompt libraries and fine\-tuning to red\-team testing, latency budgets, and fallback strategies.
- Cross\-Functional Collaboration: Lead cross\-functional initiatives, partnering with Product, Detection Engineering, and Customer Success to translate real\-world attacker behavior into robust ML and rule\-based detections.
- Push the Frontier: Experiment with retrieval\-augmented generation, tool\-calling agents, and multi\-modal models (text \+ logs \+ graphs) to keep defenders decisively ahead.
- Mentorship: Provide technical mentorship to junior engineers, foster engineering best practices, and contribute to architectural design reviews.
Required Skills \& Qualifications
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### Software Engineering \& Architecture Expertise
- Core Engineering: 7\+ years of experience in software development, engineering production systems using modern programming languages (Python, Go, Rust, or Java).
- Agentic Systems: Deep knowledge of agentic systems design, such as Centralized and/or Decentralized MAS (Multi\-Agent Systems) architectures.
- Graph Architectures: Solid understanding of Graph structures and specifically graph databases.
- Orchestration Frameworks: Hands\-on experience building agents, orchestration frameworks (LangChain/LangGraph, Agno AGI, or custom), and evaluation harnesses.
- Distributed Systems: Deep understanding of microservices architecture, containerization (Docker, Kubernetes), and event\-driven systems.
- APIs: Strong fundamentals in API design (REST/gRPC) and distributed systems.
### Soft Skills
- Communication: Clear, concise communication skills and a bias for collaborative problem\-solving.
- Leadership Alignment: Proven track record of gathering consensus and guiding multi\-stakeholder initiatives through uncertain boundaries.
- Analytical Rigor: Strong problem\-solving and analytical skills.
### Nice\-to\-have
- Domain Background: Prior work in cybersecurity (SIEM, EDR, SOAR, or MDR).
- Startup Mentality: Background driving high\-impact engineering initiatives in high\-growth startups or enterprise SaaS.
- Cloud Infrastructure: Familiarity with cloud infrastructure security (AWS, GCP, or Azure).
Education \& Certifications
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- Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field.
- Relevant certifications (AWS/GCP Professional Engineer, Kubernetes, or security\-related credentials) are a plus.
Why Join Us?
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- Opportunity to work with cutting\-edge AI\-driven cybersecurity technologies and Google SecOps solutions.
- Collaborate with a talented and innovative team focused on continuously improving security operations.
- Competitive salary and benefits package.
- A culture of growth and development, with opportunities to expand your knowledge in AI, cybersecurity, and emerging technologies.
*If you're passionate about combining cybersecurity expertise with artificial intelligence and have experience with advanced multi\-agent architectures, we encourage you to apply!*
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. Senior-level AI roles across all categories have a median of $227,400.
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
AI roles in Austin pay a median of $215,300 across 523 tracked positions. That's 8% above the national 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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