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About This Role
About Ent
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Ent is the intent\-aware workspace security platform for securing human and AI\-driven work. Built to protect productivity, the new attack surface, Ent understands not just what users and agents do but why, and intervenes at the moment of risk before incidents occur. Founded by Lou Manousos and Brandon Dixon (co\-founders of RiskIQ, acquired by Microsoft, and the team behind Microsoft Security Copilot), Ent is in production with Global 2000 customers across hospitality, financial services, and defense, and backed by Decibel, Sequoia, Crosspoint Capital, Craft Ventures, Shield Capital, Felicis, and In\-Q\-Tel.
How We Work
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*Customer first.* Every roadmap conversation starts with what a CISO told us last week.
*Humble.* No drama. Teamwork over showmanship. Accountability over politics. The work speaks louder than the person doing it.
*Urgency.* The window to build a durable security company in the AI era is open right now. We move at the speed of the people we want to protect.
About the Role
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We are hiring a builder who will drive AI transformation for the operation of Ent's rapidly\-growing business. This person's job is to deeply understand how Ent's operations actually runs, then hands\-on build the agents, automations, integrations, and data tooling that make every function faster and sharper.
This is a rare mix of consultant and engineer. You will embed with teams to learn their work, find the highest\-leverage problems, and then go build the solution yourself — from prototype to production to adoption. You'll start by prioritizing business systems (the connective tissue between CRM, support, finance, and ops) because that's where reliable automation compounds fastest, then expand outward to whichever function has the biggest opportunity.
You'll report to the Chief of Staff with direct CEO sponsorship, giving you cross\-functional access and air cover to change how work gets done.
What You'll Achieve (Responsibilities)
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- Embed with each business function — sales, exec staff, operations, customer success , and business systems — to map how work actually gets done and where AI creates leverage.
- Prioritize business systems first: instrument, connect, and automate the core stack (CRM, support, finance/ops tooling) so downstream automations are reliable.
- Design and personally build scalable, cost\-effective AI solutions end\-to\-end: agents and copilots, workflow/process automation, and data \& analytics that remove manual work and speed decisions.
- Own solutions from discovery prototype production adoption measurement; you ship, you don't just advise.
- Integrate AI into Ent's systems responsibly, respecting data governance and security — we hold ourselves to the standard we sell.
- Establish lightweight standards, evaluations, and guardrails so automations are trustworthy, observable, and maintainable.
- Enable teams through documentation, onboarding, and training so adoption sticks after you move to the next problem.
- Partner with R\&D to reuse Ent's own AI capabilities internally where it makes sense.
- Maintain a prioritized, ROI\-ranked internal AI backlog and report impact to the CEO/CoS.
What You'll Bring (Requirements)
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- Demonstrated hands\-on building with LLMs: prompt engineering, working with model APIs (Anthropic, OpenAI, etc.), retrieval/RAG, structured outputs, and at least one agent framework (e.g., LangGraph, CrewAI).
- Working software engineering ability: Python and/or TypeScript, comfort with REST APIs and one cloud, and enough SQL/data handling to be dangerous.
- Business\-systems fluency: experience integrating or automating CRM/support/ops tools (e.g., Salesforce/HubSpot, Zendesk) and workflow platforms (Zapier/Make/n8n or code).
- A process mind: can decompose a messy business process, quantify the cost, and design a measurably better one.
- Consultative communication: can run a working session with a sales leader and a check\-in with an exec in the same day, and translate between them.
- Bias to ship: gets a working prototype in front of users fast and iterates.
- High\-degree of discretion and integrity: in building these internal systems, you will have access to sensitive company information
- Equivalent demonstrated building ability can substitute for a degree, especially at entry/IC2\. A strong portfolio of shipped AI/automation work outweighs credentials.
Our Benefits
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- Distributed workplace. SF office roles plus remote across North America.
- Own a piece of the journey. Meaningful equity on top of salary.
- We've got you covered. 90% of medical, dental, and vision paid by Ent; 75% for dependents.
- Take the time you need. Flexible PTO.
- Family matters. 12 weeks fully paid maternity leave; 8 weeks fully paid paternity leave.
- Live well. $100 monthly lifestyle account.
- Set up your space. $500 home office stipend for remote employees.
Diversity \& Accommodations
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We're committed to building a diverse, inclusive, and equitable workplace. We value nontraditional career journeys and diverse perspectives, and are happy to provide reasonable accommodations at any stage of hiring — just let your recruiter know.
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 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At ENT, 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 $214,900 based on 6,420 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $194,400.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
ENT AI Hiring
ENT has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in San Francisco, CA, US.
Location Context
AI roles in San Francisco pay a median of $265,000 across 1,335 tracked positions. That's 23% 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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 Safety roles lead at $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 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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