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About This Role
Product Engineer, Customer Experience (Full Stack, AI\-Native)
Location: Austin, TX preferred (hybrid); open to remote for the right candidate Type: Full time Reports to: VP of Engineering Compensation: $90,000 to $110,000 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 content, risk scoring, and AI\-native capabilities are core to where we're headed. We're a small engineering team with an unusually high output\-per\-engineer model, hiring builders who want that leverage.
About the Role
We are hiring a full stack product engineer focused on customer experience. The mission is simple: our customers experience a fast, reliable, polished product — and when they don't, you find out why and ship the fix.
This role sits at the intersection of three teams. You'll partner with Support as their engineering counterpart — picking up what they've triaged and can't resolve, and building the tooling that makes their job easier. You'll work with Product to turn what customers actually experience into roadmap input. And you'll engage directly with customers when an issue warrants it — joining calls, understanding impact firsthand, and closing the loop when the fix ships.
You'll own the loop from customer\-reported issue to root cause to shipped fix, and drive down recurring problems instead of patching symptoms. Your definition of "fix" extends past code defects: when the root cause is a confusing flow, a misleading error message, an awkward integration, or thin documentation, the experience is the bug — and improving it is your job.
How We Work
Every engineer here develops primarily through AI tools — Claude Code or similar — and your value is the judgment you bring: you understand how web applications actually work, where they break, and how to direct AI to fix them correctly rather than cosmetically.
We run continuous flow, not sprints: work is scoped into roughly one\-week shippable deliverables with minimal ceremony between you and production. A customer issue can go from escalation to shipped fix in days, not quarters — and success is measured by what stops recurring, not tickets closed.
Be clear\-eyed: this is a startup with hard enterprise commitments, and the pace reflects that. When a customer is hurting, it's ours until it's fixed, and the team is small enough that there's no one to hand it off to. If you want a season of your career doing the most intense, highest\-leverage work you've done, this is that. If you want a comfortable cruising altitude, it isn't.
What You'll Do
- Own customer\-reported issues end to end: reproduce, root\-cause, fix, ship, verify with the customer. When three customers hit the same class of bug, fix the class, not the instances.
- Debug the enterprise surface where customer problems most often originate: SSO/identity (SAML, OIDC, SCIM provisioning, Entra/Okta quirks), integration failures, webhook and sync breakdowns, permissions edge cases, and feature flag states that explain why one customer sees something nobody else does.
- Resolve data integrity issues directly in the database — SQL to find bad records, understand how they got that way, fix them safely, and ship the change that prevents that class of corruption.
- Act as Support's engineering partner: take triaged escalations, give clear answers and timelines, and build the debugging aids, admin tools, and observability that raise their first\-touch resolution rate.
- Engage customers directly on high\-impact issues, communicating root cause and resolution in plain language.
- Feed patterns to Product: you'll have the clearest view of where the product hurts customers — turn it into concrete roadmap input.
- Treat poor UX as a bug. When a flow confuses customers or a workflow fights the user, redesign and ship the better experience — don't close it "working as intended."
- Improve the developer and admin experience: API ergonomics, integration setup, error messages, documentation — where technical customers form their opinion of us.
- Use Claude Code as your core workflow for investigation, implementation, testing, and validation.
What We're Looking For
- Full stack experience with real production webapps — you've debugged live systems, not just built greenfield.
- Genuine understanding of modern web architecture — how frontend, API, database, auth, queues, and deployment fit together, and the failure modes of each layer.
- Enterprise technology familiarity, because our customers are enterprises: you've worked with or debugged against IdPs and SSO (SAML, OIDC, SCIM, Entra ID, Okta) and know they're a top source of B2B customer issues.
- Strong SQL — a must, not a nice\-to\-have. You can query production data confidently, diagnose how records went bad, and write safe corrective migrations.
- Understanding of modern integration layers — unified API platforms like Nango, OAuth token lifecycles, webhooks, sync jobs, rate limits — and how to debug them when a customer's data stops flowing.
- Working knowledge of feature flagging, as both a debugging dimension (a broken experience is often a flag state nobody checked) and a shipping tool: safe rollouts, per\-customer targeting, and cleaning up stale flags.
- Strong debugging instincts: you dig until you find the actual root cause and are skeptical of fixes you can't explain.
- An eye for UX: you can tell "works as specified" from "works well" and ship a meaningfully better flow without a designer holding your hand.
- Daily, deep use of AI dev tools (Claude Code strongly preferred) — you drive the tool and catch it when its fix is wrong or superficial.
- Working familiarity with our kind of stack: React, Next.js, TypeScript, Node.js, Python, Postgres, basic AWS.
- Clear communication across three audiences: support teammates who need actionable answers, product teammates who need patterns, and enterprise customers who need confidence and plain language.
- High ownership, low ego. "Someone else's code" is not a category you recognize.
This is a mid\-level role. We care about demonstrated debugging and shipping ability far more than years of experience or titles.
You'll Thrive Here If
- You get satisfaction from making things *work right*, not just work — and "right" includes how it feels to use.
- You like variety — a frontend bug one day, a queue backing up the next, then redesigning a flow customers keep tripping over.
- You see customer pain as the most honest signal of what to build.
- An open mystery in production nags at you until it's closed.
- You want a stretch of your career defined by maximum output and learning, around people operating the same way.
- You use AI tools systematically and aggressively, with your own judgment as the quality gate.
This Role Is Not For Someone Who
- Only wants net\-new features and considers maintenance beneath them.
- Patches symptoms rather than understanding systems.
- Closes confusing\-but\-functional experiences as "working as intended."
- Needs detailed reproduction steps before investigating.
- Only wants one layer of the stack.
- Treats AI tools as an occasional helper rather than the central workflow.
- Wants big\-company pace and predictability. We're a startup in a sprint, and we're honest about it.
Interview Process
Three steps, designed to evaluate real working ability without days off or speculative travel:
- Screen (45 min, remote). How you debug: production issues you've root\-caused — especially SSO/identity, integration, or data integrity — how you think about failure modes, and how you use AI tools daily.
- Live working session (2 hrs, remote). Investigate a realistic bug in our stack using Claude Code — one where the symptom doesn't point at the cause, with some SQL along the way. We watch how you drive the AI, validate its diagnosis, and whether you catch its plausible\-but\-wrong first answer.
- In\-person final (half day, Austin). Meet the team and make sure it's a fit, both ways. Travel covered for remote candidates.
For candidates between roles or contracting, we can structure the final step as a paid 30\-day contract\-to\-hire — an option, not a requirement.
Location
Austin, TX hybrid is our strong preference; Austin\-area candidates should work in person on a regular cadence. For the right candidate we're open to US remote, with occasional travel to Austin.
How to Apply
Include production issues you've debugged end to end — the gnarlier the better. Tell us how you use Claude Code or similar tools when investigating real problems, including a time the AI's first answer was wrong and how you caught it.
Pay: $90,000\.00 \- $110,000\.00 per year
Benefits:
- 401(k)
- Flexible schedule
- Health insurance
- Paid time off
Education:
- High school or equivalent (Preferred)
Experience:
- IT support: 2 years (Preferred)
Work Location: Hybrid remote in Austin, TX 78748
Salary Context
This $90K-$110K 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 ($100K) sits 45% below the category median. Disclosed range: $90K to $110K.
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.
Frequently Asked Questions
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