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About This Role
PENNYMAC:
Pennymac (NYSE: PFSI) is a specialty financial services firm with a comprehensive mortgage platform and integrated business focused on the production and servicing of U.S. mortgage loans and the management of investments related to the U.S. mortgage market.
At Pennymac, our people are the foundation of our success and at the heart of our dynamic work culture. Together, we work towards a unified goal of helping millions of Americans achieve aspirations of homeownership through the complete mortgage journey.
A Typical Day:
As a Frontend Engineer on the AI Platform Services team, you will be a core builder of the platform's "face" and a champion for the user experience. While the backend team builds the "brain" (RAG pipelines and autonomous agents), you will help develop the "Collaborative Canvas"—the interface where human ingenuity meets artificial intelligence. You will not just be building standard dashboards; you will be engineering complex, AI\-native interfaces alongside senior and principal engineers. You will tackle unique challenges in Generative UI, real\-time state synchronization, and browser\-based code editing. You will help build the tools that define how our engineers interact with autonomous agents, contributing to a deeply integrated, multi\-modal workspace.
This role requires strong frontend engineering skills, an interest in developer tooling, and a desire to build intuitive user interfaces in the age of AI.
What You'll Do (Responsibilities)
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- Develop the Agentic Workspace: Build and maintain core frontend features for the AI platform using React, TypeScript, and Next.js, ensuring excellent performance and responsiveness.
- Contribute to the "Collaborative Canvas": Help implement a real\-time, multi\-player workspace where AI agents and humans can co\-author artifacts. Work with WebSockets and state\-syncing technologies to manage shared state seamlessly.
- Implement "Generative UI": Build out a robust component registry and help develop the rendering engine that allows AI agents to dynamically generate interactive UI elements (charts, forms, checklists) on the fly.
- Visualize Intelligence: Create intuitive data visualizations allowing users to see *why* an AI agent made a decision, trace its sources, and inspect its reasoning via interactive diagrams.
- Partner with the Backend: Collaborate closely with Backend Engineers to consume complex APIs (GraphQL, Streaming JSON, Server\-Sent Events) and bring agent outputs to life in the browser.
- Focus on Developer Experience (DX): Work with your "customers" (Pennymac engineers and product analysts) to understand their workflows and build elegant UI solutions that reduce friction.
- Uphold Frontend Standards: Write clean, modular, and well\-tested code. Participate in code reviews, contribute to E2E/Integration testing, and help maintain our architectural standards.
What You’ll Bring:
What We're Looking For (Qualifications)
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Must\-Haves:
- Bachelor’s Degree in Computer Science, HCI, or related engineering disciplines, or equivalent professional experience.
- Strong React \& TypeScript Skills: Proven experience (3\-5\+ years) building complex Single Page Applications (SPAs). Solid understanding of React fundamentals, hooks, context, and state management.
- Next.js Experience: Familiarity with modern Next.js architectures (App Router) and concepts like Server Components and progressive UI rendering.
- Interest in Real\-Time Applications: Experience with or a strong desire to learn about building multi\-user, real\-time applications using WebSockets, SSE, or state\-syncing libraries.
- AI Integration Experience: Experience (or strong personal interest/projects) integrating LLM APIs (OpenAI, Anthropic, etc.) into frontend applications. Comfortable handling unstructured data and "streaming" UI responses.
- Data Visualization: Experience with visualization libraries (e.g., D3\.js, Recharts, React Flow, or Mermaid.js) to render charts, graphs, or flow diagrams.
- A "Startup Mentality": Collaborative spirit, eagerness to learn, and adaptability. You are comfortable iterating quickly on an MVP based on user feedback.
Nice\-to\-Have (Bonus Points):
- Experience with AI SDKs: Familiarity with the Vercel AI SDK, LangChain.js, or similar frameworks.
- Design Systems: Experience working with and contributing to enterprise\-grade Design Systems (e.g., using Tailwind CSS, Radix UI, or Shadcn).
- Backend Familiarity: Basic understanding of Node.js to support "Frontend\-for\-Backend" (BFF) patterns.
- Knowledge of CRDTs: Familiarity with Conflict\-Free Replicated Data Types (Yjs, Automerge) for collaborative editing.
- FinTech/Mortgage Domain: Familiarity with the regulatory and compliance constraints of the financial industry.
Why You Should Join:
As one of the top mortgage lenders in the country, Pennymac has helped over 4 million lifetime homeowners achieve and sustain their aspirations of home. Our vision is to be the most trusted partner for home. Together, 4,000 Pennymac team members across the country are guided by our core values: to be Accountable, Reliable and Ethical in all that we do. Pennymac is committed to conducting a business that makes positive contributions and promotes long\-term sustainable growth and to fostering an equitable and inclusive environment, where all employees and customers feel valued, respected and supported. Benefits That Bring It Home: Whether you're looking for flexible benefits for today, setting up short\-term goals for tomorrow, or planning for long\-term success and retirement, Pennymac's benefits have you covered. Some key benefits include:* Comprehensive Medical, Dental, and Vision
- Paid Time Off Programs including vacation, holidays, illness, and parental leave
- Wellness Programs, Employee Recognition Programs, and onsite gyms and cafe style dining (select locations)
- Retirement benefits, life insurance, 401k match, and tuition reimbursement
- Philanthropy Programs including matching gifts, volunteer grants, charitable grants and corporate sponsorships
To learn more about our benefits visit: https://pennymacnews.page.link/benefits
For residents with state required benefit information, additional information can be found at: https://www.pennymac.com/additional\-benefits\-information Compensation: Individual salary may vary based on multiple factors including specific role, geographic location / market data, and skills and experience as defined below:* Lower in range \- Building skills and experience in the role
- Mid\-range \- Experience and skills align with proficiency in the role
- Higher in range \- Experience and skills add value above typical requirements of the role
Some roles may be eligible for performance\-based compensation and/or stock\-based incentives awarded to employees based on company and individual performance.
Salary: $75,000 \- $130,000 Work Model: OFFICE
Salary Context
This $75K-$130K 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 PENNYMAC, 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 ($102K) sits 43% below the category median. Disclosed range: $75K to $130K.
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.
PENNYMAC AI Hiring
PENNYMAC has 2 open AI roles right now. They're hiring across AI/ML Engineer. Based in Carrollton, TX, US. Compensation range: $130K - $130K.
Location Context
Across all AI roles, 15% (590 positions) offer remote work, while 3,217 require on-site attendance. Top AI hiring metros: New York (2,643 roles, $211,000 median); San Francisco (2,168 roles, $253,000 median); Los Angeles (1,792 roles, $191,580 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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