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About This Role
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About the Team:
People Tech \& Analytics (PTA) builds and owns the data, AI, and technology infrastructure for Affirm's People function. The team runs like a product engineering group embedded in HR. We own the full stack: data ingestion, transformation, dashboards, AI tools, and production applications deployed on Snowflake.
About the Role:
This is a hands\-on engineering role. You will build, deploy, and maintain AI\-powered systems that serve the People function and the broader employee base. The work is taking messy business problems (fragmented knowledge, manual processes, disconnected tools) and turning them into working software: agents, APIs, applications, and infrastructure. You will work closely with partners across the People function who own domain expertise and stakeholder relationships. Your job is to turn their rough applications and processes into production systems, and to push back when a technical constraint changes what's possible. This is not a pure backend role. You will be in the room when business problems are being scoped, and you need to understand the problem well enough to make architecture decisions on your own. You also need to take what you build and get it running in production. Hosting, security, deployment, and ongoing maintenance are all part of the job.
What You'll Do:
- Build and ship AI agents, APIs, and applications on Affirm's internal platform (Snowpark Container Services / Quicksilver). You own the full lifecycle: architecture, containerization, networking, secrets, CI/CD, monitoring, and fixing what breaks.
- Turn messy business requirements from People Operations stakeholders into production systems. Integrate with Workday, Notion, and case management tools so AI surfaces real answers from governed content, not model guesses.
- Navigate Affirm's existing security and data governance infrastructure to get AI systems running safely on people data. RBACs, data classification, and access policies already exist, but connecting them across systems (Workday, Snowflake, case tools) is where it gets messy. You figure out what's allowed, build within those constraints, and make sure employee data stays where it's supposed to.
- Design reliability infrastructure for multi\-model LLM services. Structured output validation, fallback chains, circuit breakers for external APIs, and quality controls that catch hallucination before users see it.
- Work directly with non\-technical stakeholders to scope problems, make architecture decisions, and give honest assessments of what AI can and can't do. You translate in both directions.
- Contribute to the team's shared Python codebase, dbt models, and Snowflake infrastructure as part of a small, full\-stack team that ships fast.
- Own what you build. When something breaks in production, you diagnose and fix it.
What We're Looking For:
- Software engineering foundation. You have built, deployed, and maintained production applications. You understand version control (Git/GitHub), CI/CD pipelines, containerization, and what it takes to keep software running, not just written.
- Systems thinking and technical architecture. You understand how software systems fit together: databases, APIs, authentication, hosting, deployment pipelines. You can make architecture decisions, evaluate trade\-offs, and read code well enough to know when something is wrong. The team works primarily in Python, and you should be comfortable in it, but the ability to think in systems matters more than raw coding skill.
- Builder disposition. You have created something from nothing: a system, a tool, a platform, in an environment where nobody handed you a spec. You identified the problem, designed the solution, and shipped it.
- Ability to work across the technical\-business boundary. You can sit in a meeting with non\-technical stakeholders, understand the real problem behind the stated request, and come back with a solution that actually addresses it. You translate in both directions: technical constraints into business language, business needs into technical requirements.
Why You'll Love This Role:
- You will build AI systems that directly change how 2,000\+ employees interact with the People function. Visible, measurable impact.
- Small team, high autonomy. You own what you build, from architecture through production.
- Work at the frontier of AI\-powered employee experience. This is the kind of work that will define how companies operate in the next five years.
- A team that ships fast, uses AI tools as a core part of how we work, and doesn't wait for permission to build things.
Pay Grade \- O
Equity Grade \- 12
Employees new to Affirm typically come in at the start of the pay range. Affirm focuses on providing a simple and transparent pay structure which is based on a variety of factors, including location, experience and job\-related skills.
Base pay is part of a total compensation package that may include equity rewards, monthly stipends for health, wellness and tech spending, and benefits (including 100% subsidized medical coverage, dental and vision for you and your dependents.)
USA base pay range (CA, WA, NY, NJ, CT) per year: $220,000 \- $280,000
USA base pay range (all other U.S. states) per year: $195,000 \- $255,000
Please note that visa sponsorship is not available for this position.
\#LI\-Remote
Affirm is proud to be a remote\-first company! The majority of our roles are remote and you can work almost anywhere within the country of employment. Affirmers in proximal roles have the flexibility to work remotely, but will occasionally be required to work out of their assigned Affirm office. A limited number of roles remain office\-based due to the nature of their job responsibilities.
We're extremely proud to offer competitive benefits that are anchored to our core value of people come first. Some key highlights of our benefits package include:
- Health care coverage \- Affirm covers all premiums for all levels of coverage for you and your dependents
- Flexible Spending Wallets \- generous stipends for spending on Technology, Food, various Lifestyle needs, and family forming expenses
- Time off \- competitive vacation and holiday schedules allowing you to take time off to rest and recharge
- ESPP \- An employee stock purchase plan enabling you to buy shares of Affirm at a discount
We believe It's On Us to provide an inclusive interview experience for all, including people with disabilities. We are happy to provide reasonable accommodations to candidates in need of individualized support during the hiring process.
\[For U.S. positions that could be performed in Los Angeles or San Francisco] Pursuant to the San Francisco Fair Chance Ordinance and Los Angeles Fair Chance Initiative for Hiring Ordinance, Affirm will consider for employment qualified applicants with arrest and conviction records.
By clicking "Submit Application," you acknowledge that you have read Affirm's Global Candidate Privacy Notice and hereby freely and unambiguously give informed consent to the collection, processing, use, and storage of your personal information as described therein.
Salary Context
This $195K-$280K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $180K across 1841 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 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Affirm, 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 $218,750 based on 3,817 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $200,000. This role's midpoint ($237K) sits 9% above the category median. Disclosed range: $195K to $280K.
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.
Affirm AI Hiring
Affirm has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Remote, US. Compensation range: $280K - $280K.
Remote Work Context
Remote AI roles pay a median of $185,334 across 717 positions. About 14% of all AI roles offer remote work.
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 3,708 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).
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 3,708 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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