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About This Role
Location: Austin, TX, United States
Salary Range:
Date Posted: Jun 2, 2026
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Employ is transforming how hiring gets done. With our three ATS solutions \- Jobvite, Lever, and JazzHR \- plus cutting\-edge AI Companions, we free recruiters from admin work and give them more time for what matters: connecting with people. More than 23,000 global customers rely on Employ to make millions of candidate connections each year, helping them hire smarter, faster, and at scale. From growing startups to the world’s most recognized brands, we’re redefining what’s possible in talent acquisition.
We’re a fast\-moving, remote\-first team of builders and innovators who live our people\-first philosophy every day. We back it up with flexible work scheduling and paid time off, comprehensive benefits, and career development opportunities that help you thrive. At Employ, you won’t just grow your career \- you’ll help millions of others grow theirs.
Come join our team where we have each other’s backs, champion our customers, hold ourselves accountable, and shape what’s next in talent acquisition.
*Candidate Safety Notice*
*Please note, employ takes candidate safety seriously. Employ recruiters and employees communicate only via official* *@employinc.com* *email addresses. We will never request payment or personal financial information during the hiring process. If you receive suspicious outreach claiming to be from Employ, please report it to* *[email protected]**.*
In this senior\-level role, you will lead the architecture and evolution of AI\-powered systems that transform probabilistic model outputs into reliable, production\-grade product capabilities. You will design and ship systems around agents, retrieval pipelines, orchestration layers, and evaluation frameworks, while defining the architectural standards that ensure AI\-driven features operate safely and at scale across our platform.
This is not a feature\-delivery role — it is a systems\-definition role. Your decisions will shape how AI capabilities are built, evaluated, and deployed across the organization.
About the Team
Our AI Foundations team is building the foundation for how AI will power the next generation of products at Employ. This is a high\-ownership, high\-autonomy team responsible for turning rapidly evolving model capabilities into reliable, customer\-facing systems.
### What You'll Do:
Architect \& Ship AI\-Native Systems
- Lead the architecture and delivery of end\-to\-end AI\-powered systems, including agents, RAG pipelines, orchestration layers, and reasoning workflows.
- Translate product vision into scalable technical systems.
- Define contracts, state management strategies, and guardrails for AI\-driven workflows.
- Own and evolve API contracts that AI systems interact with, ensuring reliability, idempotency, authentication safety, and rate limiting.
Engineer Reliability at Scale
- Design schema enforcement and validation layers for AI\-generated outputs.
- Implement retries, fallback strategies, and failure\-mode containment.
- Establish evaluation frameworks for benchmarking, regression testing, and drift detection.
- Create observability standards for AI systems, including structured logging, telemetry, tracing, and performance monitoring.
- Productionize experimental AI capabilities into scalable, secure services.
Set Direction \& Elevate the Organization
- Establish architectural patterns and standards adopted across teams.
- Mentor engineers in AI\-native and spec\-driven development practices.
- Influence engineering culture through clarity, urgency, and execution.
- Decompose high\-level business outcomes into executable technical systems.
### What You Bring:
You have mastered the fundamentals of software engineering and operate at a higher level of abstraction. You are not simply accepting AI\-generated code — you review, restructure, and integrate it into cohesive, production\-grade systems.
- 4\+ years building AI\-augmented product capabilities (LLMs, RAG systems, agents, orchestration frameworks).
- Event\-Driven \& Asynchronous Systems : Experience designing decoupled systems using queues such as Kafka, SQS, or BullMQ, and implementing asynchronous workflows that prevent blocking operations in user\-facing systems.
- State Management Strategy : Experience persisting state across sessions, managing context windows efficiently, and handling concurrency and race conditions when multiple agents interact with shared data.
- Structured Data Enforcement : Experience enforcing structured outputs using schema validation tools such as Pydantic, Zod, or JSON Schema to ensure AI\-generated outputs are reliable and machine\-readable.
- API Design \& Integration : Strong understanding of REST, GraphQL, or RPC interface design, along with authentication, rate limiting, and idempotent API patterns.
### Tech Stack \& Hard Skills:
Languages: Python, including asyncio, decorators, and the modern Python ecosystem. TypeScript / Node for integration and application\-layer logic
AI Stack: Orchestration frameworks such as LangChain, LangGraph, or custom agent loops. Retrieval\-Augmented Generation (RAG) systems. Hybrid search, re\-ranking, and chunking strategies. Vector databases such as Pinecone, pgvector, or Weaviate
Database \& Infrastructure: Advanced SQL skills including query optimization and indexing strategies. Containerization using Docker, Kubernetes or similar orchestration platforms. Experience running isolated environments for code execution
Why You’ll Love Working Here:Make an impact: Your work will directly shape how thousands of employees experience total rewards at Employ — ensuring programs that attract, engage, and retain top talent across the globe.
Meaningful, mission\-driven work: Join a team where operational excellence in HR directly supports Employ’s mission to improve hiring outcomes for both people and businesses.
Unlimited PTO: Trust\-based time off so you can recharge and bring your best self to work.
Comprehensive benefits: Medical, dental, and vision coverage to support you and your family’s health and well\-being.
Learning \& development programs: Access to training, mentorship, and development resources to grow your skills — from HR operations to total rewards strategy.
*At Employ, we believe that when people from different backgrounds and perspectives come together, amazing things happen. We’re proud to be an* *Equal Opportunity Employer,* *and we’re committed to creating an environment where everyone feels welcomed, respected, and able to thrive.*
*We do not discriminate based on race, color, religion, sex, sexual orientation, gender identity, national origin, age, disability, veteran status, or any other characteristic protected by law. We believe that when people feel they belong, they can do their best work and we want every member of our team to feel that sense of belonging.*
*If you need accommodation at any stage of the hiring process, please reach out. We’ll be glad to support you.*
Recruitment Fraud Notice:
*Employ takes candidate safety seriously. Please be aware that* *Employ recruiters and interviewers will only communicate with candidates using official email addresses ending in @employinc.com.*
*We will* *never* *ask for payment, banking information, or personal financial details at any stage of the hiring process, and we do not conduct interviews via messaging apps or social media platforms.*
*If you receive communication that appears suspicious or claims to represent Employ but does not come from an @employinc.com email address,* *do not respond or continue engaging with the sender. Please report the message to* *[email protected]****.* Share with Email Share on Twitter Share on Facebook Share on LinkedIn
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 KORE Power, 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.
KORE Power AI Hiring
KORE Power has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Austin, TX, 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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