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About This Role
Facet was founded in 2016 on the belief that objective, personalized financial advice is essential to living well. Our mission is to empower people to live more enriched lives by delivering a new standard of advice. By removing asset minimums, we provide holistic planning that goes far beyond investments to address every way money impacts life. To achieve this, we leverage a "human\-plus\-tech" approach, combining the expertise of CFP® professionals with cutting\-edge AI and automation. This model allows Facet to integrate advice into our members’ entire financial picture—all for an affordable, flat membership fee.
Our commitment to innovation and member success has earned national recognition, including being ranked the \#1 Best Financial Advisory Firm 2025 by USA TODAY and named to Newsweek’s list of America’s Top Financial Advisory Firms 2025\. With an A\+ BBB Rating and a Trustpilot “Excellent” score (as of Sept. 16, 2025\)\*, we are proud to be redefining the industry for our members and our team alike.
Why Join the Facet Security \& Platform Engineering Team?
- Reinvent the Experience: Have the agency to build new ways of working by treating the platform as a product, directly improving experience throughout the company via self\-service and high\-leverage automation.
- Work with Cutting\-Edge Technologies: Work at the technical forefront, leveraging best\-in\-class serverless and AI infrastructure (FaaS, LLMs, Vector Databases) to redefine financial services.
- Directly Drive Company Growth: Your foundational work in speed, security, and scalability is the engine that enables Facet's rapid growth and delivers superior financial outcomes for thousands of members.
What You’ll Do:* AI Infrastructure Orchestration: Design and maintain the infrastructure required to power AI features, including vector database management, model deployment pipelines, and secure integration with LLM providers.
- Platform Architecture: Build and evolve our Internal Platforms, focusing on self\-service, automation, and reducing cognitive load for internal teams.
- Serverless \& Event\-Driven Systems: Optimize and secure our 100% cloud\-native environment, developing and deploying scalable serverless and event\-driven architectures (AWS Lambda, Google Cloud Functions, EventBridge, Pub/Sub).
- DevSecOps: Implement automated security guardrails within the CI/CD pipeline. You will lead the "Identity/IAM Architecture" to ensure least\-privilege access and build automated compliance checks into our Infrastructure\-as\-Code (Terraform).
- AI Guardrails: Develop and enforce technical guardrails for our internal AI platform to manage data privacy, cost controls, and ethical usage monitoring.
- Reliability \& Automation: Drive a "manual\-is\-a\-bug" culture by automating environment provisioning, incident response, and performance monitoring.
Requirements
Basic Qualifications* Experience: 4\-8 years of experience in a Platform, DevOps, or Site Reliability Engineering role.
- Cloud Native: Deep experience with AWS and/or GCP, specifically focused on serverless architectures. (Note: We are a Kubernetes\-free environment; expertise in Serverless/FaaS is essential).
- Infrastructure\-as\-Code: Expert\-level proficiency with Terraform and a "policy\-as\-code" mindset.
- Security Focus: Practical experience designing IAM architectures and implementing automated security scanning/compliance in CI/CD.
- Scripting \& Logic: Proficiency in at least one modern language (Python, Go, or TypeScript/Node.js) to build custom tooling and integrations.
Preferred Qualifications* AI/ML Ops: Experience supporting production AI workflows (e.g., Pinecone, LangChain, OpenAI API integrations).
- Identity Management: Familiarity with modern AuthN/AuthZ protocols and providers (e.g., Okta, Auth0, AWS IAM Identity Center).
- Event\-Driven Design: Experience building or maintaining high\-scale event buses and asynchronous messaging systems.
- Product Mindset: Experience treating "The Platform" as a product, with a focus on improving the Developer Experience (DX).
Benefits
Benefits* Competitive Compensation: $160,000 \- 180,000 base salary \+ bonus
- Ownership: Equity in a rapidly growing company on the path to becoming the "Next Great Financial Services Company".
- Flexibility: Remote\-first culture—work from anywhere in the U.S..
- Time Off: Flexible Paid Time Off (PTO).
- Comprehensive Benefits and Perks: Medical, dental, vision, 401(k) match, disability, paid parental leave, wellness reimbursement, and education reimbursement programs.
- Facet was ranked \#1 Best Financial Advisory Firm 2025 by USA TODAY in April 2025 who partnered with Statista to rank the top 500 RIAs. Recommendations were collected via an independent survey among over 30,000 individuals. self\-recommendations were prohibited, and no compensation was provided for the ranking. Facet has been Accredited with the Better Business Bureau® since June 2025, and as of February 2026 holds an A\+ BBB Rating. This Accreditation does not imply BBB approval or endorsement. Plant\-A Insights Group and Newsweek partnered to identify America's Top Financial Advisory Firms 2025\. This analysis was performed with data as of September 2024 and reviewed over 15,000 financial advisories registered with the SEC. These rankings prepared by Plant\-A reflect editorial content and no compensation was provided by Facet in connection with America's Top Financial Advisory Firms 2025\." As of February 25, 2026; Facet has a 4\.8 star rating with TrustPilot.com. TrustPilot uses a formula for calculation that considers three factors: time span, frequency, and Bayesian average. After the calculation, a TrustScore is then visualized into a star rating from 1 to 5 stars, including half stars. Facet’s star rating is calculated from reviews from January of 2022 to present day. Daily star rating may fluctuate. Companies on Trustpilot aren't allowed to offer incentives or pay to hide reviews and Facet has not made any payments in offer or receipt of any reviews. Please see additional information
Salary Context
This $160K-$180K range is above the median for AI/ML Engineer roles in our dataset (median: $100K across 15465 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 26,159 AI roles we're tracking, AI/ML Engineer positions make up 91% of the market. At Facet, 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 $166,983 based on 13,781 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $131,300. Disclosed range: $160K to $180K.
Across all AI roles, the market median is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Architect ($292,900). By seniority level: Entry: $76,880; Mid: $131,300; Senior: $227,400; Director: $244,288; VP: $234,620.
Facet AI Hiring
Facet has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in US. Compensation range: $180K - $180K.
Location Context
AI roles in Austin pay a median of $212,800 across 317 tracked positions. That's 16% 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 26,159 open positions tracked in our dataset. By seniority: 2,416 entry-level, 16,247 mid-level, 5,153 senior, and 2,343 leadership roles (Director, VP, C-Level). Remote roles make up 7% of the market (1,863 positions). The remaining 24,200 roles require on-site or hybrid attendance.
The market median for AI roles is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. Highest-paying categories: AI Engineering Manager ($293,500 median, 28 roles); AI Architect ($292,900 median, 108 roles); AI Safety ($274,200 median, 19 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 26,159 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (23,752), AI Software Engineer (598), AI Product Manager (594). 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 (2,416) are outnumbered by mid-level (16,247) and senior (5,153) 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 2,343 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 7% of all AI roles (1,863 positions), with 24,200 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 $184,000. Top-quartile roles start at $244,000, and the 90th percentile reaches $309,400. 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 Engineering Manager roles lead at $293,500 median, while Prompt Engineer roles sit at $122,200. 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: Rag (16,749 postings), Aws (8,932 postings), Rust (7,660 postings), Python (3,815 postings), Azure (2,678 postings), Gcp (2,247 postings), Prompt Engineering (1,469 postings), Openai (1,269 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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