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About This Role
Full Stack Product Engineer (AI\-Native)
Location: Austin, TX preferred (hybrid); open to remote for the right candidate Type: Full time Reports to: VP of Engineering Compensation range: $105,000 to $130,000 base salary, plus benefits and any applicable equity or bonus compensation
About Living Security
Living Security is a B2B SaaS company in human risk management — we help large enterprises understand and reduce the human side of security risk. Our customers are major enterprise security teams, and our platform increasingly runs on AI: AI\-generated training content, risk scoring, and AI\-native product capabilities are core to where we're headed, not bolt\-ons. We're a small engineering team with an unusually high output\-per\-engineer model, and we're hiring builders who want that leverage.
About the Role
We are hiring a product engineer who ships outcomes, not tickets.
This is a full stack, high autonomy role for someone who develops almost entirely through AI tools — Claude Code or similar — but who can actually drive the AI because they genuinely understand how web applications work. You know the full architecture of a modern webapp: frontend, backend, APIs, databases, auth, background jobs, deployment. You know the common failure modes, the framework tradeoffs, and the problems that show up in real production systems. That understanding is what lets you direct AI aggressively instead of just accepting what it produces.
You don't need a decade of experience. You need to be a true generalist who has built complete webapps end to end, understands why they're structured the way they are, and can take an ambiguous business problem and turn it into working, shipped software without someone writing you a spec first.
The work in front of us is concrete: LLM\-powered product features, integrations into the Microsoft enterprise ecosystem (Entra ID, Outlook add\-ins, Teams apps), real\-time reporting and dashboards, and the enterprise platform capabilities — identity, permissions, data pipelines — that large security organizations depend on.
How We Work
AI tools do most of the typing here. Your value is in the judgment layer: knowing what to build, how to structure it, when the AI's output is wrong, and what "done" actually means for the customer. If you've used AI coding tools as your primary workflow — not as autocomplete, but as the way you plan, build, test, and debug — this will feel natural. If you've mostly worked from detailed tickets inside one slice of a large system, it won't.
We run a continuous\-flow model, not sprints: work is scoped into roughly one\-week shippable deliverables, milestones carry the multi\-week arcs, and there's minimal ceremony between you and production. Success here is measured in shipped customer outcomes, not story points or activity.
One thing to be clear\-eyed about: this is a startup with hard enterprise commitments and real deadlines, and the pace reflects that. We move with genuine urgency, the team is small enough that there's no one to hand things off to, and when something matters it gets finished — not parked until the next planning cycle. People here go hard because they're building something they care about. If you're looking for a season of your career to do the most intense, highest\-leverage work you've done, this is that. If you're looking for a comfortable cruising altitude, it isn't.
What You'll Do
- Own features from ambiguous problem statement through production, end to end.
- Build LLM\-powered product features — generation, agents, scoring, automation — where they create clear customer value, and own their quality the same way you'd own any other code path.
- Build integrations into the enterprise ecosystems our customers live in, especially Microsoft: Entra ID, Outlook add\-ins, Teams apps, and the APIs and sync jobs behind them.
- Build across the full stack: frontend, backend, APIs, databases, integrations, background jobs, and cloud infrastructure.
- Use Claude Code (or similar) as your core development workflow: planning, implementation, refactoring, testing, debugging, and documentation.
- Make practical product and architecture decisions without heavy oversight, and explain your reasoning.
- Validate AI\-generated code rigorously and take full responsibility for quality, security, and maintainability.
- Ship quickly, learn from real usage, and iterate.
What We're Looking For
- Solid full stack experience building and shipping complete web applications — you've taken apps from zero to production, not just contributed to one layer.
- Real fluency with modern web architecture: how frontend, API, database, auth, caching, queues, and deployment fit together, and where they typically break.
- Daily, deep use of AI development tools (Claude Code strongly preferred). You drive the tool; it doesn't drive you.
- Working familiarity with a startup\-friendly stack: React, Next.js, TypeScript, Node.js, Python, Postgres, and basic AWS (Lambda, S3, RDS, or similar).
- Comfort with the realities of enterprise B2B software — SSO/identity, permissions, integrations, and data customers actually depend on — or the architecture sense to come up to speed on them fast.
- Strong product instincts — you can figure out what matters and prioritize for customer value.
- A strong plus: experience shipping LLM\-powered features to production — prompt design, streaming, structured outputs, evals, or agent workflows — and a realistic sense of what these systems do well and where they fail.
- High ownership, high urgency, low ego. You're comfortable being handed an outcome instead of a ticket.
This is a mid\-level role. We care far more about demonstrated ability to ship complete products with AI\-native workflows than about years of experience or prior titles.
You'll Thrive Here If
- You like being handed a problem, not a fully defined ticket.
- You can figure out what matters, make a plan, and start building the same day.
- You'd rather ship something useful and improve it than polish something for weeks before users see it.
- You find it hard to put an unsolved problem down, and a hard deadline energizes you rather than drains you.
- You want a stretch of your career defined by maximum output and maximum learning, surrounded by people operating the same way.
- You use AI coding tools systematically and push them hard — and you catch them when they're wrong.
This Role Is Not For Someone Who
- Needs detailed specs before starting.
- Only wants to work in one layer of the stack.
- Has only worked in narrow roles where ownership was split across many specialized teams.
- Treats AI coding tools as an occasional helper rather than the central workflow.
- Prefers process over shipping.
- Is looking for big\-company pace and predictability. We're a startup in a sprint, and we're honest about it.
Interview Process
Our process is designed to evaluate real working ability without asking you to take days off or fly anywhere on spec. It has four steps:
- Screen (45 minutes, remote). A conversation about what you've built end to end, how you think about webapp architecture, and how you actually use AI development tools day to day.
- Live working session (2 hours, remote). A screen\-shared pairing session where you work a realistic problem in our stack using Claude Code. We're not watching you type — we're watching how you drive: how you direct the AI, what you accept and reject, how you validate its output, and how you reason about tradeoffs out loud.
- Paid mini\-project (roughly one day of effort, async, on your schedule). A time\-boxed, realistic product problem, compensated with a flat fee. You deliver a working repo plus a short recorded walkthrough of your decisions and how you used AI tools along the way. Evenings and weekends are fine — no PTO required.
- In\-person final (half day, Austin). Meet the team, see how we work, and make sure the setup and the people are a fit — for both sides. For remote candidates, we cover travel.
For candidates between roles or currently contracting, we can alternatively structure the final step as a paid 30\-day contract\-to\-hire. That's an option, not a requirement.
Compensation
Full time base salary range: $105,000 to $130,000, plus benefits and any applicable equity or bonus compensation. The interview mini\-project is paid a flat fee. Final compensation depends on experience, scope, and demonstrated fit.
Location
This role is based in Austin, TX, hybrid — that's our strong preference, and Austin\-area candidates should be able to work in person on a regular cadence. For the right candidate, we're open to remote within the US, with occasional travel to Austin for onboarding and team time.
How to Apply
Include examples of complete webapps or products you've built — especially ones where you owned the architecture end to end. We especially want to hear how you use Claude Code or similar AI tools in your daily engineering workflow, and an example of a time you caught and corrected something the AI got wrong.
Pay: $105,000\.00 \- $130,000\.00 per year
Benefits:
- 401(k)
- Flexible schedule
- Health insurance
- Paid time off
Application Question(s):
- Describe your experience building web applications.
Work Location: Hybrid remote in Austin, TX 78748
Salary Context
This $105K-$130K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $180K across 1937 roles with salary data).
View full AI/ML Engineer salary data →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 Living Security, 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. This role's midpoint ($117K) sits 35% below the category median. Disclosed range: $105K to $130K.
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.
Living Security AI Hiring
Living Security has 3 open AI roles right now. They're hiring across AI/ML Engineer. Based in Austin, TX, US. Compensation range: $110K - $130K.
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.
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