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About Paradox Machines
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Paradox Machines is the data foundation enabling organizations to leverage AI. We pair expert data engineering and architecture with our proprietary AI\-enabled platform, which aggregates data from across an organization's systems, standardizes and governs it, and exposes it for reporting, analytics, and AI\-driven use cases. Our focus is ensuring you have trusted analytics and AI, so you can drive business impact without worrying about data quality and reliability.
About the Role
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This is a founding role at a data and AI company — which means high ownership, direct client impact, and a real chance to shape how the company operates and grows.
You'll lead complex, 0\-to\-1 engagements: taking ambiguous goals from mid\-market leadership teams and turning them into working data systems and measurable business outcomes. You'll move between executive stakeholders and engineering teams, own the client relationship end\-to\-end, and help build the internal playbooks that make Paradox scalable. You’ll report to the CEO of Paradox. Expect your career to take a step function up.
If you want a role where you're executing someone else's process, this isn't it. If you want to build something, this is.
What You'll Own
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- Client engagements, end\-to\-end: You'll be the primary partner to clients — leading discovery, aligning stakeholders, defining scope, and staying accountable through launch and adoption. The problems are real and often messy: unifying sales data across business units, building AI layers on operational systems, replacing spreadsheet reporting with something that actually works.
- Translating business problems into working systems: You'll take whiteboard conversations with clients and turn them into clear specs for engineers — ETL pipelines, semantic models, metric definitions, evaluation criteria. You'll stay close through prioritization, testing, and rollout.
- Building the company, not just the client: As a founding team member, you'll help identify what's working across engagements and turn it into repeatable assets — delivery templates, data models, internal tools. You'll contribute to proposals, shape solution approaches for prospective clients, and help Paradox get better at what it does with every project. We are excited to help you grow, and learn with you.
What You Bring
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- 6\-10 years in data, analytics, consulting, or similar roles where you led multiple projects and had to manage clients or customers (whether internal or external).
- Experience delivering data systems or products alongside engineers — pipelines, analytics platforms, AI\-driven applications.
- Comfort moving from ambiguous business problems to clear engineering spec without losing the core objective. Weighing tradeoffs is critical.
- Client\-facing experience in environments where stakeholders weren't always aligned and the path forward wasn't obvious.
- Genuine fluency with AI tools and a habit of incorporating them into your work — not a curiosity checkbox, but a real part of how you operate. We’re an AI\-native company.
- Bias for action: you'll write a quick SQL query to check the numbers, draft a data definition for quick feedback, or step into a demo when it moves things forward.
What We Offer
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You'll be an early leader at a venture\-backed company working on one of the most consequential problems in business right now: making company data actually usable for AI\-driven decisions.
Most data and AI consultants spend years executing inside someone else's framework. Here, you'll help build the framework — and your scope will grow as the company does.
- Competitive base salary \+ performance bonus
- 401(k) Plan
- Medical, dental, and vision coverage
- Unlimited PTO
- Remote\-first
- Annual company offsite
- Home office budget
- Conference budget
Equal Opportunity
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Paradox is an equal\-opportunity employer. We celebrate diverse backgrounds and perspectives and are committed to an inclusive environment for all teammates.
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 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Infinity, 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 in Demand for This Role
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 $218,750 based on 3,817 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $200,000.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
Infinity AI Hiring
Infinity has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in US.
Location Context
AI roles in Austin pay a median of $214,343 across 87 tracked positions.
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 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 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 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 $300,000 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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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