Interested in this AI/ML Engineer role at Guild Mortgage Company LLC?
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About This Role
Guild Mortgage Company, closing loans and opening doors since 1960\. As a mortgage banking firm we are dedicated to serving the home owner/buyer. Our goal is to provide affordable home financing for our customers, utilizing the best terms available while providing a level of professionalism and service unsurpassed in the lending industry.
Position Summary
The AI Engineer is responsible for the development and implementation of advanced artificial intelligence systems. This role involves designing, programming, and training complex algorithms that emulate human cognitive processes. Key responsibilities include managing and processing large datasets, developing, and testing machine learning models, and integrating these models into applications using API calls or embedded code.
The position requires a strong foundation in software development, data science, and data engineering, as well as a deep understanding of machine learning principles. The AI Engineer will collaborate with cross\-functional teams to bring AI\-driven solutions to life, enhancing the functionality and user experience of various applications.
Compensation
This role is an exempt position with a Targeted Salary Range of $109,000 to $156,000 annually.
Compensation at Guild is influenced by a wide array of factors including but not limited to local and federal minimum wage requirements, education, level of experience, and applicant’s geographical location.
Essential Functions
- Source relevant data from various channels, including databases, online sources, and internal systems, essential for training AI models.
- Process raw data to make it suitable for use in machine learning, which includes cleaning, normalizing, and segmenting the data.
- Create machine learning models using appropriate algorithms and techniques to solve specific business problems or enhance application capabilities.
- Rigorously test AI models to ensure their accuracy, reliability, and robustness, and validate them against predefined metrics.
- Seamlessly integrate AI models into existing software applications, employing API calls or embedded code for smooth functionality.
- Continuously optimize AI systems for better speed, efficiency, and accuracy, ensuring they meet the required performance standards.
- Identify and fix bugs or issues in AI models and their integration into applications, ensuring smooth operation.
- Keep abreast of the latest advancements in AI, machine learning, and related technologies to incorporate cutting\-edge solutions.
- Work alongside other professionals, including AI Engineers, data scientists, software developers, and project managers, to achieve cohesive project goals.
- Create detailed documentation for AI systems and their architectures, aiding in maintenance, updates, and knowledge transfer.
- Utilize AI to improve the user interface and experience of applications, making them more intuitive and responsive.
- Adhere to ethical guidelines and standards in AI development, ensuring fairness, privacy, and transparency.
- Conduct in\-depth analysis of AI systems' performance, identifying areas for improvement and optimization.
- Utilize cloud computing platforms for efficient AI model development, testing, and deployment.
- Incorporate user and stakeholder feedback into AI system development to continuously improve its effectiveness and usability.
- Serve as a bridge between technical and non\-technical teams, facilitating the integration of AI into various business processes and ensuring alignment with business objectives.
- Partner with key stakeholders, including the business unit leaders, Product, Data \& Technology teams, to assist with their AI\-related technical issues.
- Contribute to the team of AI professionals by using expertise to answer questions and sharing repeatable design patterns with less experienced teammates, enhancing skillsets and competencies of team members, and sharing technical knowledge throughout the team.
- Participate in stakeholder reviews and design sessions.
- Provide data, reports, and information to management as needed.
- Drive strong communications, partnerships, and stakeholder management with senior leaders, functional managers, and staff.
- Perform other duties as assigned.
Qualifications
- Bachelor’s or Master’s degree in Computer Science, Artificial Intelligence, Data Science, Engineering, or a related field.
- Minimum of 4 years of experience.
- Advanced knowledge and experience working with Python
- Proficiency in other programming languages such as Java, R, or Scala.
- Experience with machine learning frameworks like TensorFlow, Keras, PyTorch, or similar.
- Strong understanding of algorithms and data structures.
- Knowledge of AI principles and technologies, including neural networks, NLP, and computer vision.
- Experience working with cloud cognitive services in Azure, AWS, Google, etc.
- Problem solver with an ability to work as a team towards a solution.
- Ability to prioritize multiple tasks in a deadline\-driven environment, strong sense of urgency and responsiveness.
- Strong detail orientation and highly organized with proven ability to lead effectively and drive results in a matrixed management environment.
- Ability to think critically, including the ability to evaluate facts and data to draw conclusions, determine the downstream impact of decisions and associated risks.
- Excellent verbal and written communication skills plus demonstrate strong leadership capabilities.
- Strong interpersonal and team building skills.
- Self\-starter with the demonstrated ability to learn/adapt to new technologies and techniques.
Requirements
Travel: Infrequent based on company events and/or relevant conferences or training
Physical: Work is primarily sedentary; mobility in an office setting.
Manual Dexterity: Frequent use of computer keyboard and mouse.
Audio/Visual: Ability to accurately interpret sounds and associated meanings at a volume consistent with interpersonal conversation. Regularly required to accurately perceive, distinguish and interpret information received visually and through audio, e.g., words, numbers and other data broadcasted aloud/viewed on a screen, as well as print and other media.
Environmental: Office environment – no substantial exposure to adverse environmental conditions.
Guild offers a pleasant work environment, competitive compensation and excellent benefits package; including medical, dental, vision, life insurance, AD\&D, LTD and 401(k) with employer match.
Guild Mortgage Company is an Equal Opportunity Employer.
REQ\#: AIENG018214
Equal Opportunity Employer
This employer is required to notify all applicants of their rights pursuant to federal employment laws. For further information, please review the Know Your Rights (https://www.eeoc.gov/poster) notice from the Department of Labor.
Salary Context
This $109K-$156K 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 Guild Mortgage Company LLC, 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 ($132K) sits 27% below the category median. Disclosed range: $109K to $156K.
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.
Guild Mortgage Company LLC AI Hiring
Guild Mortgage Company LLC has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in US. Compensation range: $156K - $156K.
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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