Interested in this AI/ML Engineer role at nitrogen?
Apply Now →Skills & Technologies
About This Role
WHAT WE DO
Nitrogen equips financial advisors with a suite of AI\-powered products that showcase the value of their advice so they can attract clients and keep them fearless. Since launching Riskalyze in 2011, we've been on a mission to create what we call Catalyst Moments — those powerful instances when an advisor helps a client go from confused to confident, from fearful to fearless.
Our connected suite brings together Risk Alignment, Investment Research, Income Planning, Tax Intelligence, and Legacy Planning, combining agentic AI with trusted, deterministic analytics to turn complex financial insights into persuasive visuals. The result: clearer conversations, stronger relationships, and advisors who can prove their value in every client meeting.
Trusted by tens of thousands of advisors, backed by an industry\-leading NPS of 71, and the first wealthtech company to earn ISO 42001 certification, Nitrogen is built on 13 years of advisor data and half a trillion dollars in assets on platform. We invented the Risk Number®, built on a Nobel Prize\-winning academic framework, and we champion the Fearless Investing Movement — because a world empowered to invest fearlessly starts with empowered advisors.
Nitrogen is an equal opportunity employer. We encourage people from underrepresented groups to apply. We are committed to being fair and intentional in our hiring decisions by reviewing every application thoroughly.
THE TEAM
Our Data and Services Team empowers the world to invest fearlessly by by building agentic AI systems and the data\-powered services behind them, directing fleets of AI developer tools to ship products that serve advisors and their firms across the wealth management industry.
As a Staff AI Execution Engineer on the Data \& Services team, you'll lead how we design, build, and ship AI systems into production across Nucleus and our broader platform. This means architecting agentic workflows, building agent memory and orchestration, and standing up the evaluation frameworks that make agent decisions reliable at scale. You build primarily through agentic AI and you bring real depth in building AI products, pairing both with the data fluency our domain demands. You'll own these systems across their full lifecycle, from design through production, continuously raising what one engineer can deliver.
*Staff AI Execution Engineer:*
- Advances Nitrogen's AI capabilities with your expertise designing and shipping agentic systems into production use.
- Owns the successful delivery of AI features end\-to\-end: from agent design and orchestration, through evaluation and hardening, to reliable production systems advisors depend on.
- Builds the foundations that make agents dependable: memory systems, evaluation frameworks, and the patterns that improve agent decisions at scale.
- Sets a high bar for technical productivity through deep, fluent, and consistently advancing use of agentic AI developer tools.
- Pioneers AI\-augmented engineering practices on the team, including prompts, harness configurations, and multi\-agent orchestration, and shares them so the whole team moves faster.
- Delivers high\-quality, production\-grade features at a velocity that meaningfully exceeds traditional engineering throughput, while reliably meeting commitments.
- Provides architectural leadership across AI systems and the services and data flows that support them.
- Maintains a deep understanding of what our domain\-specific data means to our customers and their product experience.
- Proactively identifies and addresses technical debt and developer experience gaps, advocating for AI\-enhanced solutions.
- Mentors and elevates the technical skills of fellow engineers, particularly in building and shipping AI systems.
- Demonstrates a continuous improvement mindset in both personal development and all technical workflows.
Requirements
- Agentic Engineering. You operate at the leading edge of AI\-augmented engineering. Tools like Claude Code, Devin, and Cursor are core to how you build, not adjuncts to a traditional workflow. You direct multiple agents in parallel and use them to amplify staff\-level judgment rather than replace it.
- AI Systems \& Products. You've shipped AI systems into production use, not just prototypes. You build agentic workflows, agent memory, and orchestration, and you know what it takes to make them reliable, observable, and safe for real users.
- Evaluation \& Quality at Scale. You deeply understand the evaluation frameworks that improve agent decisions at scale. You design evals, measure agent behavior rigorously, and turn those signals into systems that get measurably better over time.
- AI Tooling \& Frameworks. You're fluent with the frameworks that power modern AI systems, including LangChain, LangGraph, GraphQL, and MCP. You evaluate and adopt new tools faster than your peers and can articulate where each one fits.
- AI\-Native Productivity. You demonstrably ship more, with higher quality, than traditional throughput allows. You've internalized the disciplines that make agentic tooling pay off: clear specs, tight feedback loops, rigorous review of agent output, and deliberate context engineering.
- Experience. You bring 8\+ years of hands\-on engineering experience, with substantial depth building production systems and the judgment to know how data engines and pipelines work inside and out.
- Technical Leadership. You are a trusted technical authority that others turn to for solving the most challenging problems and making high\-impact decisions. You've given tech talks internally or at conferences. You actively mentor others, reduce knowledge silos, and raise the team's overall capability.
- Data Fluency. You're comfortable with SQL \& Python in Snowflake \& dbt and the realities of messy real\-world data. Experience with CDC, APIs, and services such as DMS, OpenFlow, Kafka, or similar is a strong plus.
*The expected compensation range for this role is a $200k\-$220k \+ annual bonus target.*
*Lesser experience may result in lower compensation and greater experience may result in greater compensation than the stated range.*
Benefits
Financial Benefits \& Perks
- 4% 401(k) Match. Our employees invest so much in our company and we love getting to invest in them. The company will match your contributions dollar\-for\-dollar, up to 4% of your total annual compensation.
- Free Financial Planning Services. By working at a financial technology company, you get the benefit of fantastic financial advice. This is offered to all employees wanting expert guidance on how to handle their money.
Health \& Family
- Medical, Dental \& Vision insurance plans. We want to help keep you (and your family) healthy! Comprehensive health insurance options for you \& your family.
- Health Savings Accounts (HSA) or Flexible Spending Accounts (FSA) available depending on chosen medical plan. We know that investment risk isn’t a one\-size\-fits all and neither are your health savings options!
- Generous maternity \& paternity leave for either the birth or adoption of a child. Mom's \& Dad's need time with their new family members!
- Discounted pet insurance available. Pets are family too!!
Time Away \& Culture
- 3 weeks vacation \& 1 week sick time per year.Take the time you need for fun or simply time to recover from not feeling well.
- 11 paid company holidays per year. Enjoy your time off; you deserve it!
- Remote \& in\-person team building activities help our employees stay connected and engaged. We absolutely love to hype our people up!
- Company wide meetings held by our CEO benefit all employees by keeping everyone in the loop. We are one team, and we tackle projects together.
- Employee development is our priority. From leadership training, to mentorship, to industry resources, we care about progressing you in your career.
WANT TO KNOW MORE?
While you can learn a lot from a job description, you may have more questions, and that’s totally okay! We encourage all individuals interested in working at Nitrogen to learn more about us by checking us out on our website and social media platforms:
Salary Context
This $200K-$220K range is above the median for AI/ML Engineer roles in our dataset (median: $180K across 1937 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 3,823 AI roles we're tracking, AI/ML Engineer positions make up 69% of the market. At nitrogen, 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 $181,170 based on 12,692 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($210K) sits 16% above the category median. Disclosed range: $200K to $220K.
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.
nitrogen AI Hiring
nitrogen has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in US. Compensation range: $220K - $220K.
Location Context
AI roles in Austin pay a median of $215,300 across 523 tracked positions. That's 8% above the national 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 3,823 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).
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 3,823 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.