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About This Role
Junior Software Developer — AI\-Enabled Product Engineering
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Build Real Products. Move Fast. Learn Fast.
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Intelligent Audit is a fast\-growing freight audit, logistics intelligence, and business analytics technology company. We help companies understand, optimize, and improve one of their most complex cost centers: transportation.
Our platform turns massive amounts of shipping, carrier, invoice, and delivery data into products that help customers move faster, spend smarter, and operate with more confidence.
We are looking for a Junior Software Developer who wants to do more than write tickets. We want someone who wants to build products, solve real problems, learn quickly, and use modern AI tools to become a stronger engineer.
About the Role
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This is a high\-paced product engineering role in a structured, supportive environment.
You will work on a team that is actively building new products, improving existing platforms, and solving meaningful business problems for customers. You will collaborate closely with experienced developers, strong product owners, and cross\-functional stakeholders through well\-developed planning, design, and workshop sessions.
You will not be thrown into chaos. You will be part of a company with clear processes, strong product direction, collaborative teams, and a culture that supports learning, ownership, and execution.
We are especially excited by developers who are curious, creative, hardworking, and eager to build products they believe in.
What Makes This Role Different
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At Intelligent Audit, junior developers are not treated like task machines.
You will have the opportunity to:
- Build real products that customers and internal teams rely on
- Work in a fast\-moving engineering environment with clear structure and support
- Collaborate with talented product owners who know how to turn business needs into strong product direction
- Participate in developed workshop sessions where ideas, requirements, workflows, and solutions are shaped collaboratively
- Use modern AI tools such as Claude, Cursor, ChatGPT, and other AI\-assisted development tools as part of your daily workflow
- Learn how to think through product problems, not just code assignments
- Contribute ideas, challenge assumptions, and help shape better solutions
- Grow quickly through hands\-on development, feedback, experimentation, and mentorship
What You’ll Do
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As a Junior Software Developer, you will:
- Build and enhance software products across our logistics, freight audit, analytics, and operational platforms
- Collaborate with developers, product owners, and business teams to turn ideas into working software
- Participate in product workshops, technical discussions, planning sessions, and code reviews
- Write clean, maintainable, well\-documented code
- Debug, test, and improve application features
- Support deployments, releases, and ongoing product improvements
- Use AI tools to accelerate development, research, documentation, testing, debugging, and workflow design
- Learn unfamiliar technologies quickly and apply them to real product work
- Bring creative ideas to the table and help improve how the team builds software
How We Use AI
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We are highly supportive of developers using tools like Claude, Cursor, ChatGPT, and other AI\-assisted development environments.
We believe AI can help great engineers move faster, learn faster, and explore better solutions. But AI does not replace engineering judgment.
The right candidate will know how to:
- Use AI tools as force multipliers
- Ask better technical questions
- Generate and evaluate code thoughtfully
- Catch flaws in AI\-generated solutions
- Improve code quality, documentation, testing, and debugging workflows
- Stay accountable for the final product, even when AI helps along the way
We want people who think like engineers first and use AI as an advantage.
What We Value
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We value people who bring:
- Brilliance — sharp thinking, strong problem\-solving, and the ability to understand complex systems
- Creativity — new ideas, curiosity, and the ability to imagine better ways to build
- Work ethic — ownership, follow\-through, and pride in doing great work
- Product mindset — a desire to build software that matters and solves real problems
- Collaboration — the ability to communicate, listen, ask questions, and work well with others
- Learning speed — comfort with new tools, new technologies, and fast\-moving priorities
We are looking for builders: people who want to create products they believe in, not just complete assigned tasks.
Current Development Environment
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You will work in a modern, collaborative engineering environment built for speed, structure, and innovation.
Our tools and technologies include:
- PHP 8\.x
- PostgreSQL
- Vanilla JavaScript
- jQuery
- PHPStorm
- Cursor
- VS Code
- Claude
- ChatGPT
- AI\-assisted coding, debugging, documentation, and workflow tools
Prior experience with every tool is not required. What matters most is your ability to learn, think clearly, work hard, and build.
Qualifications
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This role is ideal for a recent graduate or early\-career developer who wants to grow quickly in a serious product engineering environment.
- 1–3 years of software development experience, internship experience, project experience, or equivalent practical coding experience
- Bachelor’s degree in Computer Science, a related technical field, or equivalent hands\-on experience
- Familiarity with web application development
- Basic understanding of databases, debugging, version control, and software development fundamentals
- Strong communication and problem\-solving skills
- Interest in using AI tools to improve productivity, learning, and code quality
Why Join Intelligent Audit?
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At Intelligent Audit, you will build software that has real impact.
You will join a company that moves quickly, operates with structure, invests in product thinking, and encourages developers to use the best tools available. You will work with strong product owners, thoughtful engineering teams, and collaborative workshop processes that help turn ideas into products.
If you are brilliant, creative, hardworking, and excited by the idea of building products you believe in, this is a place where you can grow fast and do meaningful work.
Salary Context
This $60K-$75K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $180K across 2130 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 4,133 AI roles we're tracking, AI/ML Engineer positions make up 69% of the market. At Intelligent Audit, 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 $185,000 based on 13,200 positions with disclosed compensation. Entry-level AI roles across all categories have a median of $97,760. This role's midpoint ($67K) sits 64% below the category median. Disclosed range: $60K to $75K.
Across all AI roles, the market median is $200,700. Top-quartile compensation starts at $254,000. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Safety ($274,200) and AI Engineering Manager ($268,700). By seniority level: Entry: $97,760; Mid: $165,778; Senior: $227,400; Director: $250,000; VP: $250,000.
Intelligent Audit AI Hiring
Intelligent Audit has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Rochelle Park, NJ, US. Compensation range: $75K - $75K.
Location Context
Across all AI roles, 14% (583 positions) offer remote work, while 3,532 require on-site attendance. Top AI hiring metros: New York (2,760 roles, $211,000 median); San Francisco (2,258 roles, $253,000 median); Los Angeles (1,841 roles, $195,000 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 4,133 open positions tracked in our dataset. By seniority: 106 entry-level, 1,901 mid-level, 1,663 senior, and 463 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (583 positions). The remaining 3,532 roles require on-site or hybrid attendance.
The market median for AI roles is $200,700. Top-quartile compensation starts at $254,000. The 90th percentile reaches $307,500. Highest-paying categories: AI Safety ($274,200 median, 57 roles); AI Engineering Manager ($268,700 median, 42 roles); Research Engineer ($260,000 median, 442 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 4,133 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,865), Data Scientist (339), AI Software Engineer (313). 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 (106) are outnumbered by mid-level (1,901) and senior (1,663) 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 463 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (583 positions), with 3,532 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 $200,700. Top-quartile roles start at $254,000, 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 Safety roles lead at $274,200 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 (2,128 postings), Aws (1,324 postings), Azure (1,003 postings), Rag (916 postings), Gcp (817 postings), Pytorch (655 postings), Prompt Engineering (639 postings), Claude (571 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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