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PEAK6
Austin, Texas, United States of America
WHO WE ARE
We are PEAK6, a leading investment firm, using technology to find a better way of doing things. The company’s first tech-based solution was developed in 1997 to optimize options trading, and over the past two decades, the same formula has been used across a range of industries, asset classes, and business stages to consistently deliver superior results. Today, PEAK6 seeks transformational opportunities to provide capital and strategic support to entrepreneurs and forward-thinking businesses. PEAK6’s core brands include PEAK6 Capital Management, PEAK6 Strategic Capital, Apex Fintech Solutions, FOCUS, We Insure, Evil Geniuses, Poker Power, Zogo, and Bruce Markets.
ABOUT THIS ROLE
ABOUT AI STRATEGIES
AI Strategies is the incubator group at PEAK6. We go after big new opportunities: (1) zero-to-one businesses built on PEAK6’s fintech distribution, (2) software transformations, and (3) our latest thesis, AI services roll-ups, where we acquire services businesses and rebuild how they operate with AI. A central team of engineers at the cutting edge of applied AI is deployed across every initiative. We hire for entrepreneurial instinct and an insane work ethic, we bet on talented people early, and we put them on a track to lead. We are starting the roll-up in insurance BPO, and this role is one of the first hires to build it.
THE ROLE
You will source, underwrite, and close the acquisitions that build PEAK6’s insurance BPO roll-up. You are a private equity or investment professional with a real insurance network, roll-up experience, and the conviction that AI is about to change what a services business is worth and how it is run. You will work directly with our applied AI engineers on the transformation case for every target.
As an early joiner you will hold equity in what we build, with upside that looks like a venture exit.
WHAT YOU’LL DO
- Build and own the acquisition pipeline: insurance BPOs, TPAs, and adjacent services businesses where AI changes the margin structure.
- Source directly through your insurance network, brokers, and outbound to owners ready to sell or partner.
- Lead diligence and underwriting with an AI-transformation lens, sizing with our engineers what automation unlocks in each business.
- Structure, negotiate, and close, then support integration into the platform.
- Shape the roll-up strategy: sub-sectors, sequencing, and what the platform looks like in three years.
WHO YOU ARE
- Private equity, investment banking, or corporate development background with hands-on roll-up experience. Having joined or built an existing roll-up is ideal.
- Deep insurance industry knowledge and relationships you can call on to find deals.
- You expect AI to reshape your own profession and want to be on the side doing the reshaping.
- Big ideas, insane work ethic, and the ambition to build something that lasts.
On experience: we would rather hand the reins to someone ambitious with two to six years of experience, give them real autonomy, and let them grow into the role than hire a résumé. We will also hire someone experienced, if they are at the point in their career where they want a defining challenge, are willing to fundamentally rebuild how they work with AI, and have something to prove. This is not a role for anyone who is not motivated by the size of the opportunity and the growth in scope that comes with it.
OUR REWARDS
We offer a robust package of employee perks and benefits, including healthcare benefits (medical, dental and vision, EAP), competitive PTO, 401k match, parental leave, and HSA contribution match. We also provide our employees with a paid subscription to the Calm app and offer generous external learning and tuition reimbursement benefits. As a hybrid workforce, we offer our employees the ability to work remotely up to two days a week depending on the role. The base pay offered may vary depending on job-related knowledge, skills, experience, and office location. This position also may be eligible for a discretionary annual bonus in addition to a range of health & wellness benefits.
PEAK6 is proud to be an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex (including pregnancy, sexual orientation, and gender identity), national origin, age, disability, veteran status, marital status, or any other protected characteristic. Our hiring practices ensure that all qualified applicants receive fair consideration without regard to these characteristics.
PEAK6 is committed to creating an inclusive and accessible workplace for all candidates, including those with disabilities. We are dedicated to ensuring equal employment opportunities and providing reasonable accommodations to qualified individuals with disabilities. If you require reasonable accommodations to participate in the application or interview process, please contact our HR department at [email protected]. We will work with you to provide the necessary accommodations to ensure your full participation in our hiring process.
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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 9,079 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At PEAK6, 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 $211,950 based on 13,048 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
PEAK6 AI Hiring
PEAK6 has 3 open AI roles right now. They're hiring across AI/ML Engineer. Positions span Austin, TX, Austin, TX, US.
Location Context
AI roles in Austin pay a median of $209,100 across 306 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 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 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 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). 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 (106) are outnumbered by mid-level (4,384) and senior (3,343) 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 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 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 $213,000. Top-quartile roles start at $263,911, and the 90th percentile reaches $318,400. 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 $256,000 median, while Prompt Engineer roles sit at $141,440. 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 (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 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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