Interested in this AI Software Engineer role at Brillian?
Apply Now →Skills & Technologies
About This Role
About Our Company
Backed by two Fortune 300 firms, Brillian is a technology company focused on helping small to medium sized business owners achieve their goals and the trusted financial institutions that want to better service them. Brillian is a fast\-moving, mission\-driven team that's building something genuinely new and beginning a roll out to a million SMB owners in 2026\. If you want your work to matter and your expertise to have real impact, you'll fit right in here.
Small to medium sized business owners are the primary engine of wealth creation in the modern economy. The asset they build — their business — is almost always their largest, most valuable, and most consequential financial holding. Yet the entire financial services infrastructure — advisors, banks, wealth managers, planning tools — was built around personal assets. The infrastructure to serve the most important asset was never built.
Brillian delivers the kind of strategic business intelligence that McKinsey charges $250K to millions to provide — to SMB owners who have never had access to it, through the financial institutions that already serve them. Our launch is behind us and we're now making hires 8, 9, and 10\.
About the role
We are seeking gifted software engineers to help build the next generation of AI products for our financial platform. This is a significant opportunity to be a founding member of a growing team and help shape the way we design, build, evaluate, deploy, and operate production\-quality AI systems. In this role, your work will make a significant impact on our customers, stakeholders, and ability to continue to scale.
You will work across product engineering, applied data science, and machine learning to create AI\-powered workflows that help business owners and financial advisors answer complex questions, understand business performance, and take useful next steps. The work will include backend services, data retrieval and transformation, LLM integrations, agentic workflows, evaluation systems, and user\-facing product experiences.
Why Our Company?
Our engineers love building products, emphasizing efficiency and developer happiness. We deploy at\-will, many times a day, coordinated via Slack, GitHub, and Linear. As a remote\-friendly team, we will always prefer asynchronous processes. As a result, our morning standup is the only recurring meeting and we Write Things Down™. Through collaboration, we prioritize the best answer — regardless who comes up with it — and believe "done" is better than "perfect."
We want to work with curious, self\-motivated engineers who enjoy learning new technologies. So, instead of looking for people with "X years doing Y," we're glad to find those with opinions on a variety of languages and approaches. We're excited to know there's always more to learn; if you feel the same, let's work together!
We currently use NextJS with GraphQL/Relay on the frontend and use Elixir/Phoenix, Python, INTERCAL, and NodeJS to power our backend and AI infrastructure. Our core systems store data in PostgreSQL. We deploy on Google Cloud, provisioned through Terraform.
What you'll do
- Build AI\-native product features that combine strong software engineering with applied data science and machine learning techniques.
- Create and refine automated AI agent systems written primarily in Python.
- Design agentic workflows that can reason over business and financial data, use tools safely, retrieve relevant context, and produce useful outputs within clear guardrails.
- Develop backend services, APIs, data pipelines, and integrations that connect our AI products to internal and external data sources.
- Build evaluation, observability, and feedback loops for LLM\-powered systems, with attention to accuracy, reliability, latency, cost, and user experience.
- Partner with product, design, and business stakeholders to translate ambiguous customer needs into practical technical systems.
- Support existing products and features, while helping design and build new ones.
- Collaborate regularly with other stakeholders and partners to solicit requirements, seek feedback, and provide updates.
#### Qualifications
Candidates do not need prior experience with every technology in our stack \- we are looking for candidates who are excited to learn new frameworks. That said, strong Python experience, applied data science and machine learning experience, and experience building agents are needed.
- Strong professional experience building production software systems.
- Strong Python experience, including writing robust, testable, maintainable production code.
- Applied data science and machine learning experience, including comfort working with structured data, model outputs, evaluation methods, and probabilistic systems.
- Experience building AI agents, agentic workflows, LLM\-powered automation, or similar systems that interact with tools, APIs, documents, or databases.
- Strong relational database fundamentals, including SQL and practical data modeling.
- Demonstrable proficiency and a track record of professional success coding in at least two programming languages.
- Excellent written and verbal communication skills.
- Enjoy working in a collaborative environment where engineers are expected not only to build great technology, but also to define project vision, specify technical strategy, and always be learning.
- Experience presenting to audiences and communicating effectively and empathetically with clients and other stakeholders.
Strong preference will be given to candidates who have experience:
- Building AI products that reached real users, especially in accuracy\-sensitive or data\-rich domains.
- Designing LLM evaluation workflows, test sets, prompt iteration processes, model selection approaches, and production monitoring for AI systems.
- Working with retrieval\-augmented generation, embeddings, vector search, structured extraction, classification, ranking, or other applied ML techniques.
- Quickly learning new languages, frameworks, and technologies, including best practices.
- With relational databases and query optimization.
- Deploying on and configuring Google Cloud.
- Writing production\-ready code in Elixir, TypeScript, JavaScript, Go, or Ruby.
- Working across the full product surface, including frontend technologies like TypeScript, React, GraphQL, and Relay.
#### Compensation / Benefits
In addition to a salary range of $200k – $240k for this position, we provide equity grants as well as a competitive benefits package (health insurance, dental insurance, vision coverage, home office setup, unlimited paid leave, 401k starting this year, and more).
This is a role that can be filled for either remote candidates or those based in New York, NY. We are not able to provide visa sponsorship at this time.
Salary Context
This $200K-$240K range is above the median for AI Software Engineer roles in our dataset (median: $190K across 219 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 3,823 AI roles we're tracking, AI Software Engineer positions make up 7% of the market. At Brillian, 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 $232,000 based on 797 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($220K) sits 5% below the category median. Disclosed range: $200K to $240K.
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.
Brillian AI Hiring
Brillian has 1 open AI role right now. They're hiring across AI Software Engineer. Based in New York, NY, US. Compensation range: $240K - $240K.
Location Context
AI roles in New York pay a median of $211,000 across 2,643 tracked positions. That's 5% above the national 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 3,823 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).
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 3,823 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
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.