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About This Role
*About Us*
Finance of America helps homeowners 55\+ access the equity they’ve built while staying in full control of their home and their financial future. Through a range of reverse mortgage solutions, we help customers shape the retirement they’ve earned while continuing to evolve how we serve and work together.
Joining Finance of America now means stepping into a period of momentum and growth, with teams actively shaping what comes next and opportunities to make an impact and grow your career.
To learn more about us, visit www.financeofamerica.com (http://www.financeofamerica.com/)
Purpose of Role
Responsible for designing, developing, and deploying AI\-powered solutions across the company. Focuses on building and fine\-tuning AI models, creating scalable AI applications, and ensuring their seamless integration into business operations. Collaborates closely with data scientists, engineers, and business leaders to deliver AI\-driven innovations that enhance efficiency, automation, and decision\-making.
Expectations
- Develops, trains, and optimizes machine learning and deep learning models by leveraging state\-of\-the\-art algorithms, hyperparameter tuning, and advanced training techniques to address complex business challenges and improve decision\-making processes.
- Builds AI\-driven applications and tools by integrating machine learning models, natural language processing, and computer vision into software solutions to enhance automation, personalization, and intelligent insights for users and stakeholders.
- Works with structured and unstructured data by implementing data cleaning, transformation, feature extraction, and augmentation techniques to ensure high\-quality datasets that maximize model accuracy and efficiency.
- Collaborates with business teams, IT, and data scientists by gathering requirements, translating business needs into AI\-driven solutions, and aligning technical implementations with organizational objectives to maximize value and impact.
- Deploys AI models into production environments by utilizing cloud\-based infrastructure, containerization, and MLOps best practices to ensure models are scalable, resilient, and seamlessly integrated into business workflows.
- Continuously monitors AI models' performance by implementing tracking mechanisms, analyzing drift, and proactively retraining models to maintain accuracy and effectiveness.
- Ensures AI solutions adhere to legal, regulatory, and ethical guidelines by applying fairness\-aware algorithms, explainability techniques, and bias detection methods to promote responsible AI adoption and mitigate potential risks.
- Stays updated on the latest AI trends, tools, and best practices by researching academic papers, experimenting with emerging technologies, and participating in industry conferences to drive continuous innovation and maintain a competitive edge.
- Develops technical documentation, best practices, and training materials by creating detailed guides, tutorials, and presentations to support knowledge sharing, foster AI adoption across teams, and ensure smooth onboarding of new AI solutions.
- Performs other duties as assigned.
*Reports To*
- Chief Data Officer
Qualifications – Experience/Skills/Competencies
- Minimum 4 years of hands\-on experience developing, training, and deploying AI/ML models.
- Strong proficiency in machine learning frameworks (TensorFlow, PyTorch, Scikit\-Learn, etc.).
- Proficiency in programming languages such as Python, R, or Julia.
- Experience working with cloud AI services (AWS SageMaker, Azure AI, Google Vertex AI).
- Knowledge of MLOps, model deployment, and API integration.
- Understanding of deep learning, NLP, computer vision, and other AI subfields.
- Strong problem\-solving and critical\-thinking skills.
- Ability to translate business problems into AI\-driven solutions.
- Excellent communication skills to explain AI concepts to non\-technical stakeholders.
- Experience working with large datasets and implementing data pipelines.
Qualifications – Education Requirements
- Bachelor's Degree or comparable qualifications
Qualifications – Education – Field(s)/Profession(s)
- Computer Science, Information Security, Management of Information Systems or related field preferred.
Compensation
The base salary range for this position is ($130,000 \- $150,000 ) inclusive of all geographical differences in the labor market. The base salary for the position will be determined based on factors such as the candidate’s work location, skills, education, and experience. In addition to those factors, we believe in the importance of pay equity and consider the internal equity of our current team members in determining any final offer. We offer a competitive benefits package including health, dental, vision, life insurance, paid time\-off benefits, flexible spending account, 401(k) with employer match, and ESPP.
Additional Information
The application deadline for this job opportunity is 9/21/2026\.
The above statements are intended to describe the general nature and level of work being performed by people assigned to this classification. They are not to be construed as an exhaustive list of all responsibilities, duties, and skills required of personnel so classified.
Finance of America is an Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, color, sex (including pregnancy), sexual orientation, religion, creed, age, national origin, physical or mental disability, gender identity and/or expression, marital status, veteran status or other characteristics protected by law.
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 $130K-$150K range is below the median for AI/ML Engineer roles in our dataset (median: $175K across 2162 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 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Finance of America, 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 $214,900 based on 6,420 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $194,400. This role's midpoint ($140K) sits 35% below the category median. Disclosed range: $130K to $150K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
Finance of America AI Hiring
Finance of America has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in US. Compensation range: $150K - $150K.
Location Context
AI roles in Austin pay a median of $214,343 across 143 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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 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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