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About This Role
About the Job
As Splitero's Applied AI Engineer, you will own the identification, scoping, and delivery of AI\-powered capabilities across our product and operations. You'll sit within the engineering organization but operate cross\-functionally, partnering with product, operations, and business teams to identify where AI creates real leverage, then build it. Splitero sits at the intersection of financial services and property technology, and our product and engineering teams are at the center of that innovation every day. In this role, you'll work directly with our sales, processing, underwriting, closing, and servicing teams to identify, scope, and execute opportunities to streamline and automate their workflows as we scale. On the engineering side, you'll champion AI\-first development, educate the team on emerging tooling, and lead technical initiatives that drive the organization forward.
You'll spend your time shipping working AI solutions into production by automating workflows, augmenting our product experience, and helping Splitero move faster and smarter as a business. You will have a strong understanding of when supervised AI outweighs the risk of full automation. You're as comfortable standing up a low\-code n8n automation as you are writing clean code, and you know when each approach is the right one.
At Splitero, we move fast, and the ideal candidate thrives in that environment. You default to the simplest path to a working solution, prototype quickly using every tool available to you, and have the engineering discipline to know when something needs to be rebuilt properly before it goes to production.
Responsibilities:
- AI Discovery \& Strategy
- + Socialize AI capabilities, limitations, and roadmap across the org, acting as an internal resource and thought leader on what's possible.
+ Maintain a prioritized backlog of AI opportunities across the business, triaging ideas from leadership, product, and operations into scoped, deliverable workstreams.
+ Define AI initiatives end\-to\-end: problem framing, data requirements, build vs. buy decisions, and measurable success criteria.
+ Stay current on the AI tooling landscape and recommend adoption where it meaningfully improves velocity, quality, or competitive positioning.
- Building \& Delivery
- + Build, integrate, and maintain AI\-powered features and workflows, leveraging LLM APIs (OpenAI, Anthropic, etc.), RAG pipelines, and agentic frameworks.
+ Develop automation workflows using low/no\-code orchestration tools (n8n, Make) to deliver AI solutions rapidly without requiring full engineering cycles.
+ Integrate AI capabilities into existing systems and codebases in close partnership with the product and engineering teams.
+ Own the full AI development lifecycle, including prototyping, evaluation, deployment, monitoring, and iteration, with support from engineering, product, and operational teams.
- Cross\-Functional Partnership
- + Partner with product to translate user problems into AI\-driven features and experiences.
+ Embed within operations and business teams to identify workflow automation and efficiency opportunities.
+ Collaborate with engineering to ensure AI integrations meet production standards for reliability, maintainability, and security.
+ Document and communicate AI implementations clearly so the broader team can support and build on them.
Reporting Structure
- Reports directly to the VP of Product and Engineering
- Embedded within the product engineering team, works cross\-functionally across Salesforce, product, operations, and business functions
The salary range for this position is $135,000 \- $175,000\. Within the range, individual pay is determined by job\-related skills, experience, and relevant education or training.
About You
- 6\+ years in software engineering with at least 2 years of focused experience building production AI or LLM\-powered applications
- Hands\-on experience with LLM APIs, prompt engineering, retrieval\-augmented generation (RAG), and agentic workflow design. You are on the bleeding edge of the latest releases and advancements in AI platforms.
- Demonstrated experience evaluating, advocating, and operationalizing AI tooling across an organization
- Proficiency with low/no\-code orchestration platforms (n8n, Make, Zapier) to prototype and deliver AI workflows rapidly
- Comfortable with AI\-assisted development tools (Claude Code, Cursor, GitHub Copilot, etc.) to accelerate build cycles, with the judgment to write clean, maintainable code when it matters. You are not a *vibe coder*; you leverage your engineering background to balance speed with quality using AI\-based tooling.
- Strong backend or full\-stack engineering fundamentals; you integrate AI into real systems, not just prototypes
- Fluency in Python, TypeScript/Node.js, or similar; familiarity with PostgreSQL and cloud deployment platforms (AWS, Vercel, or similar)
- Experience with multi\-agent frameworks (LangChain, LlamaIndex, or similar)
- Familiarity with vector databases, embedding pipelines, and evaluation frameworks for LLM outputs
- Self\-directed and comfortable operating as a team of one. You run your own backlog and don't need a fully defined spec to get started and validate business use cases
- Strong communicator who can translate technical AI concepts for non\-technical stakeholders across the org
Preferred:
- Experience in fintech, proptech, or real estate technology — particularly products handling sensitive financial data or consumer lending.
- Knowledge of SOC 2 or data privacy frameworks relevant to financial and personal data.
About Us
Millions of homeowners in today's economy are sitting on substantial home equity they are unable to access due to rising interest rates and strict qualification requirements of traditional lenders. That means millions of homeowners can't convert their hard\-earned equity into cash to pay off debt, start a home renovation, or pad their retirement to better their lives.
Splitero co\-founders Michael Gifford and David Zvaifler witnessed the problem firsthand throughout their real estate careers. They set out to create a win\-win product that could help those homeowners access their home equity but not make monthly payments like traditional financing options. And thus, Splitero was born.
As part of our team, you will join our quest to revolutionize the real estate industry while helping families in need. We offer a suite of benefits designed to help you thrive in all areas of life.
- Fully Remote Team \- Our remote\-first culture allows us to hire the best people, regardless of location.
- OneTeam Culture \- We foster a culture of transparency, innovation, and inclusivity, where every voice is heard and every team member is valued. We believe in celebrating our successes, doing the right thing, and helping each other so we grow as a team and as individuals.
- Generous Resources \- We want to ensure you are set up to do your best work. In addition to a solid tech stack, and access to any WeWork location, you will receive a monthly cell and internet stipend.
- Premium Healthcare \- We offer comprehensive healthcare plans for you and your family, which employees are eligible for on their first day.
- Flexible PTO and Parental Leave \- We offer a generous PTO and encourage employees to take time off to recharge. We're proud to offer a parental leave policy that allows you to welcome your new addition without worry.
We are a workforce composed of individuals with diverse backgrounds, abilities, minds, and identities. In keeping with our commitment to inclusion, we will ensure that people with disabilities are provided reasonable accommodations. If reasonable accommodations are required when applying, interviewing, or performing the essential functions of this position, please contact People Operations, at peopleops@splitero.com.
Salary Context
This $135K-$175K range is below the median 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 Splitero, 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 ($155K) sits 29% below the category median. Disclosed range: $135K to $175K.
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.
Splitero AI Hiring
Splitero has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in San Diego, CA, US. Compensation range: $175K - $175K.
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/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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