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About This Role
Playing an essential role in the U.S. economy, Fannie Mae is foundational to housing finance. Here, your expertise can help fuel purpose\-driven innovation that expands access to homeownership and affordable rental housing across the country. Join Fannie Mae to grow your career and help people find a place to call home.
Job Description
As a valued contributor to our team, you will design, produce, test, or implement software, technology, or processes across multiple projects, programs, or products, as well as create and maintain IT architecture, large scale data stores, and cloud\-based systems.
THE IMPACT YOU WILL MAKE
The Advisor Software Engineer (AI/ML) role will offer you the flexibility to make each day your own, while working alongside people who care so that you can deliver on the following responsibilities:
Determine the needs of the customer groups across multiple projects, programs, or products while identifying and resolving conflicting or complementary needs across customer groups.
Design and develop software solutions to meet needs and may also lead matrixed teams.
Apply extensive expertise in process\-driven approach in designing solutions.
Implement new software technology and coordinate simultaneous implementation tasks across teams.
Oversee the maintenance of existing software
There is 1 opening for this position which can be based in our Reston, VA office.
An Advisor role at Fannie Mae is on the same level as a Manager, but in an IC capacity.
THE EXPERIENCE YOU BRING TO THE TEAM
Minimum Required Experiences
6 years of hands\-on software engineering experience designing, developing, and maintaining scalable enterprise applications and cloud\-native solutions.
Strong proficiency in Python development, including backend services, APIs, automation, data processing workflows, and production\-ready AI/ML applications.
Strong skills in system design and architecture, including scalable, resilient, secure, and maintainable solution design.
Experience building API\-driven solutions, including REST APIs, microservices, service orchestration, secure API development, and enterprise system integrations.
Hands\-on experience with AWS cloud\-native development, including serverless, event\-driven, containerized, and distributed application patterns.
Experience with SQL and data platforms, including PostgreSQL, Snowflake, or similar relational and analytical database technologies.
Deep understanding of the software development lifecycle, including requirements analysis, design, development, testing, deployment, production support, and maintenance.
Experience with engineering best practices, including secure coding, code reviews, automated testing, CI/CD, observability, performance tuning, and production issue resolution.
Experience collaborating with technical and business stakeholders, including translating business needs into technical solutions and communicating risks, trade\-offs, and delivery impacts.
Desired Experiences
Bachelor’s or master’s degree in Computer Science, Engineering, Information Technology, Data Science, Machine Learning, Artificial Intelligence, or a related field.
Experience designing and delivering AI\-enabled enterprise software solutions, including GenAI applications, intelligent automation, AI\-assisted workflows, and AI\-driven decision support.
Experience with MLOps, vector databases, embedding\-based search, MCP\-based tool integration, and enterprise AI governance practices.
Experience writing technical papers, invention disclosures, patent\-supporting documentation, or reusable engineering playbooks for emerging technology solutions.
Experience with testing strategies and tools, including unit, integration, functional, regression, and performance testing.
Experience with Scaled Agile Framework, Agile methodology, cybersecurity vulnerability remediation, and enterprise delivery practices.
Strong relationship management skills with the ability to collaborate across stakeholders, influence outcomes, and support strategic enterprise technology initiatives.
AWS Cloud Technologies
Hands\-on AWS software engineering experience, including application development using AWS service APIs, AWS CLI, AWS SDKs, and cloud\-native deployment patterns.
Hands\-on experience with core AWS services, including AWS Lambda, Amazon S3, Amazon EC2, Amazon API Gateway, IAM, CloudWatch, EventBridge, SQS, SNS, and Step Functions.
Experience with AWS AI/ML services, including Amazon SageMaker, Amazon Bedrock, and AWS\-based model deployment or inference patterns.
Experience with containers and DevOps practices, including Docker, Kubernetes, ECS/EKS, CI/CD pipelines, automated testing, and release management.
Understanding of cloud security and compliance practices, including IAM, encryption, secrets management, vulnerability remediation, logging, and secure application design.
AI/ML and GenAI Technologies
Hands\-on experience in machine learning, AI engineering, data science, or applied AI solution development.
Hands\-on experience with Generative AI and Large Language Models, including OpenAI, Anthropic, Cohere, Amazon Bedrock, or similar enterprise AI platforms.
Strong proficiency in Python and AI/ML libraries, including PyTorch, TensorFlow, Scikit\-learn, Pandas, NumPy, and related ML frameworks.
Experience building Retrieval\-Augmented Generation solutions, including embeddings, vector databases, semantic search, document retrieval, chunking strategies, prompt grounding, and response evaluation.
Experience with LLM application patterns, including prompt engineering, guardrails, model evaluation, tool/function calling, agentic workflows, and responsible AI considerations.
Familiarity with AI application frameworks and tools, such as LangChain, LlamaIndex, FastAPI, MCP tools, vector databases, and API\-based AI service integration.
Experience with model development and deployment practices, including feature engineering, model serving, model monitoring, MLOps, and productionizing AI/ML capabilities.
Leadership and Innovation Skills
Proven experience leading technical delivery within software engineering teams, including solution direction, task assignment, progress monitoring, issue resolution, and delivery accountability.
Experience mentoring and coaching engineers, including technical guidance, code review feedback, design support, and professional development.
Ability to influence technical decisions and engineering practices, including architecture discussions, design trade\-offs, quality improvements, and adoption of modern AI/ML and cloud engineering standards.
Experience partnering with product owners, architects, business stakeholders, risk, security, and operations teams to deliver solutions aligned with business outcomes and enterprise standards.
Ability to produce high\-quality technical documentation, including architecture papers, solution design documents, technical white papers, AI/ML implementation guides, and executive\-ready technical summaries.
Experience contributing to innovation artifacts, such as invention disclosures, patent\-supporting technical writeups, proof\-of\-concept documentation, and publication\-ready technical papers when applicable.
Enterprise Risk Technology \- Software Engineering \- Advisor
155,000\.00 \- 209,000\.00
JR2645
Qualifications Amazon Web Services (AWS), Amazon Web Services (AWS), Atlassian JIRA, AWS Machine Learning, Business Process Management Skills, Cloud Technology, Communicating in Technical Writing, Communication, Computer Vision, Configuration Management (CM), Coordination, Customer and Market Insights, Data Analysis Interpretation, Data Mining, Data Visualization, Enterprise Information Security Architecture, Gradient Boosting Algorithms, Identity Management (IdM), Internal Auditing, Knowledge Management, Machine Learning (AI), Model Explainability, Multi\-modal Machine Learning Models, Natural Language Processing (NLP), Neural Networks Methods and Algorithms {\+ 20 more}
Education: Master's Level Degree: Artificial Intelligence and Robotics (Required)
The future is what you make it to be. Discover compelling opportunities at Fanniemae.com/careers.
For most roles, employees are expected to work onsite on a regular basis at their designated office location. In\-office work cadence is determined by your manager. Proximity within a reasonable commute to your designated office location is preferred unless the job is noted as open to remote.
Fannie Mae is an equal opportunity employer and considers qualified applicants for employment without regard to race, color, religion, sex, national origin, disability, age, sexual orientation, gender identity/gender expression, marital or parental status, or any other protected factor. Fannie Mae is committed to providing reasonable accommodations to qualified individuals with disabilities who are employees or applicants for employment, unless to do so would cause undue hardship to the company. If you need assistance using our online system and/or you need a reasonable accommodation related to the hiring/application process, please complete this form .
The hiring range for this role is set forth below. Final salaries will generally vary within that range based on factors that include but are not limited to, skill set, depth of experience, certifications, and other relevant qualifications. This position is eligible to participate in a Fannie Mae incentive program (subject to the terms of the program). As part of our comprehensive benefits package, Fannie Mae offers a broad range of Health, Life, Voluntary Lifestyle, and other benefits and perks that enhance an employee's physical, mental, emotional, and financial well\-being. See more here .
Requisition compensation: 155000
to 209000
Salary Context
This $155K-$209K range is below the median for AI Software Engineer roles in our dataset (median: $183K across 194 roles with salary data).
Role Details
About This Role
AI Software Engineers build the applications and systems that AI models run inside. They own the API layers, data pipelines, frontend integrations, and infrastructure that turn a model into a product users interact with. Every AI company needs engineers who can build the software around the AI.
The challenge is building reliable systems around inherently unreliable components. Models are probabilistic. They'll give different answers to the same question. They hallucinate. They're slow. They're expensive. Your job is to build an application layer that handles all of this gracefully while delivering a product that users trust and enjoy.
Across the 3,708 AI roles we're tracking, AI Software Engineer positions make up 7% of the market. At Fannie Mae, this role fits into their broader AI and engineering organization.
AI Software Engineer roles are among the most numerous in the AI job market. Every company deploying AI needs software engineers who understand AI integration patterns. The demand is broad, spanning startups to enterprises, across every industry adopting AI capabilities.
What the Work Looks Like
A typical week includes: building API endpoints that serve model inference with caching and fallback logic, designing the data pipeline that feeds context to a RAG system, implementing streaming responses in the frontend, debugging a race condition in the async inference pipeline, and optimizing database queries for the vector search layer. It's full-stack engineering with AI at the center.
AI Software Engineer roles are among the most numerous in the AI job market. Every company deploying AI needs software engineers who understand AI integration patterns. The demand is broad, spanning startups to enterprises, across every industry adopting AI capabilities.
Skills Required
Full-stack engineering skills with AI integration experience. Python and TypeScript are the most common requirements. You'll need to understand API design, database architecture, and how to build reliable systems around probabilistic outputs. Experience with streaming, async processing, and caching patterns is increasingly important as real-time AI applications proliferate.
Knowledge of vector databases, embedding APIs, and LLM integration patterns (function calling, structured outputs, retry logic) differentiates AI software engineers from general software engineers. Understanding cost optimization (caching strategies, model routing, batched inference) is valuable since inference costs can dominate application economics.
Strong postings describe the product you'll be building, the AI integration patterns you'll work with, and the scale requirements. Look for companies that have existing AI features and need engineers to improve and expand them, not companies that are 'planning to add AI' someday.
Compensation Benchmarks
AI Software Engineer roles pay a median of $219,250 based on 424 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $200,000. This role's midpoint ($182K) sits 17% below the category median. Disclosed range: $155K to $209K.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
Fannie Mae AI Hiring
Fannie Mae has 4 open AI roles right now. They're hiring across AI/ML Engineer, AI Software Engineer. Positions span Plano, TX, US, Reston, VA, US, Washington, DC, US. Compensation range: $184K - $269K.
Location Context
Across all AI roles, 14% (508 positions) offer remote work, while 3,180 require on-site attendance. Top AI hiring metros: New York (1,045 roles, $220,000 median); San Francisco (810 roles, $277,088 median); Los Angeles (397 roles, $215,000 median).
Career Path
Common paths into AI Software Engineer roles include Software Engineer, Full-Stack Developer, Backend Engineer.
From here, career progression typically leads toward Staff Engineer, AI Architect, Engineering Manager.
If you're a software engineer, you're already 80% there. Learn the AI integration patterns: RAG, streaming inference, function calling, structured outputs. Build a project that demonstrates you can wrap an AI model in a production-quality application with proper error handling, caching, and user experience. That's the portfolio piece that gets you hired.
What to Expect in Interviews
Technical screens look like standard software engineering interviews with an AI twist. Expect system design questions about building reliable applications around probabilistic models: handling streaming responses, implementing retry logic for API failures, and designing caching strategies for LLM outputs. Coding rounds test standard algorithms plus practical integration patterns like async processing and rate limiting.
When evaluating opportunities: Strong postings describe the product you'll be building, the AI integration patterns you'll work with, and the scale requirements. Look for companies that have existing AI features and need engineers to improve and expand them, not companies that are 'planning to add AI' someday.
AI Hiring Overview
The AI job market has 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 roles).
AI Software Engineer roles are among the most numerous in the AI job market. Every company deploying AI needs software engineers who understand AI integration patterns. The demand is broad, spanning startups to enterprises, across every industry adopting AI capabilities.
The AI Job Market Today
The AI job market spans 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 $300,000 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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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