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About This Role
About DualEntry
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Founded in 2024, DualEntry is one of the world’s fastest\-growing AI startups.
At DualEntry, the future of finance is being written today. ERP is one of the largest fintech markets in the world ($220,000,000,000\+). Yet, tens of thousands of companies are still using on\-premise systems, and the industry has not seen new entrants in more than 30 years.
Our AI\-native ERP lets accounting teams achieve more in less time. $5M\-ARR businesses to NYSE\-listed companies trust DualEntry to automate away manual data entry work with AI. We’re finally making the one\-person finance team a reality and putting the pain of legacy ERPs from the 1990’s in the past.
We operate with urgency and ownership. We move fast.
Why This Role Matters Now
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Since starting 18 months ago, we’ve raised $100,000,0000\+ from world\-class investors such as Lightspeed Venture Partners, Khosla Ventures, Contrary Ventures and Google Ventures, as well as more than 20 angel investors who’ve built, scaled, and exited some of the most impactful companies of the last decade.
We got there by moving incredibly fast and hiring an exceptionally sharp, hard\-working and deeply committed team from leading tech and accounting companies \- Ramp, Meta, Microsoft, Lyft, PwC, Deloitte, J.P. Morgan, Bloomberg, Sage, Xero and Intuit. And some of us don't have a fancy logo on our resume and are here for a shot to prove ourselves.
We’re a small team, growing fast with huge momentum \- join early.
As our Applied AI Engineer, you'll design and ship production AI systems that transform how finance and accounting teams operate. This team sits at the intersection of LLMs, ERP workflows and financial data, building the intelligence layer that makes fundamentally different from legacy ERP. You'll own high\-impact features end\-to\-end, from model integration to infrastructure.
This is an intense, hands\-on role with full ownership. We expect you to push for excellence and raise the bar.
Location: New York City HQ (EST)
Where you'll create impact
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- Own AI features end\-to\-end from designing agentic workflows and RAG pipelines to the infrastructure that runs them in production at scale
- Work on genuinely hard problems financial data is structured, high\-stakes and unforgiving, making it one of the more interesting domains to apply LLMs to.
- Build the evaluation frameworks and experimentation loops that turn good models into reliable, production\-grade systems.
- Partner directly with product and domain experts to push the frontier of what AI can do inside an ERP, not just what's been done before.
What sets you up for success
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- Hardcore work ethic and high agency
- 3\+ years in a technical role with a strong foundation in backend systems, APIs, and cloud infrastructure
- 2\+ years shipping production AI systems with real users and real stakes, not research or prototypes
- Hands\-on experience with production LLM applications, RAG pipelines, agentic systems or structured extraction
- Proficiency in Python and comfort working across the full stack to deliver end\-to\-end features
Bonus Points
- Background in fintech, ERP, or accounting software
- Experience with fine\-tuning or training models, not just inference
- Familiarity with Python, Kotlin, Java, or TypeScript
Experience building AI systems that operate on structured financial or transactional data
How You Operate
===================
- Pragmatic: you like to move forward and make decisions based in reality (not theory). We don't debate if Cassandra has the most theoretical scalability \- we use Postgres until it breaks
- Hard working: 'You can work long, hard, or smart, but \[here] you can't choose two out of three" \- Jeff Bezos. We wrote this job post during the weekend
- Curious: you love to learn, are highly curious about new frameworks and solutions to engineering problems
- Fast\-moving: you deploy daily, iterate quickly, and never wait for permission
Why you’ll thrive here
==========================
- Significant equity ownership in one of the top AI companies in the world
- You’re joining early and will grow with DualEntry
- Your feedback shapes the product directly
- High\-speed culture
- High\-trust environment with high expectations
- Ambitious mission \- ERP is one of the largest B2B software markets in the world ($220bn per year), this is a once in a lifetime opportunity to build the next generation ERP in an industry that has not seen new entrants in 30\+ years
Compensation \& Benefits
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- Equity: $35,000 \- $50,000 USD
- Base Salary: $110,000 \- $160,000 USD
- Time Off: 27 PTO days (incl. public holidays)
- Early\-stage role with high autonomy and real long\-term upside
- Enjoy a learning \& development budget for courses, certifications, and language learning to keep growing your skills
We hire the best, expect the best, and give you the masterclass of your career \- an archaic and huge industry like ERP only goes through a restructure like this once in a lifetime. It’s hard, it’s intense, and it’s the most rewarding work you’ll ever do.
If you’re hungry, driven, and ready to build something massive, climb aboard!
*At DualEntry, we believe great products come from diverse teams. We’re an equal opportunity employer, and we’re committed to a culture where everyone feels included, supported, and able to do their best work.*
Compensation Range: $110K \- $160K
Salary Context
This $35K-$160K range is in the lower quartile 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 DualEntry, 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. Senior-level AI roles across all categories have a median of $230,000. This role's midpoint ($97K) sits 55% below the category median. Disclosed range: $35K to $160K.
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.
DualEntry AI Hiring
DualEntry has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in US. Compensation range: $160K - $160K.
Location Context
AI roles in Austin pay a median of $214,343 across 87 tracked positions.
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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