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About This Role
AI Engineer – Agentic Automation
Location: Remote
Compensation: $150,000 – $210,000
Join a rapidly growing company disrupting the trucking industry! Cargomatic is the \#1 technology platform and digital marketplace for powering world\-class, local trucking. Take a look around you. Literally everything humans build, grow, or sell has spent time on a truck. Local trucking is the lifeblood of every regional economy, and yet this $82 billion industry still relies heavily on phone calls and fax machines. Cargomatic is transforming the way goods move around every local node in the supply chain by connecting shippers and commercial truck drivers with mobile technology. We are solving complex, real\-world problems every day, and giving full transparency to the shipping process.
Cargomatic is seeking a highly hands\-on AI Engineer who thrives in fast\-paced environments and is passionate about rapid prototyping and deploying standalone AI\-driven applications. This role focuses on building agentic AI systems and automation tools that integrate with our ecosystem and deliver immediate business value — working alongside a live TMS and RPA stack, with real exception\-handling and audit requirements. You will work at the intersection of AI, product development, and infrastructure — quickly turning ideas into working prototypes and scaling the most promising solutions into production.
Key Responsibilities
- Design agentic AI workflows for structured logistics operations, with clear escalation paths to human review or RPA where deterministic handling is required
- Call core platform/internal APIs to integrate automation workflows into our TMS and other internal systems, partnering with front\-end and backend engineering on execution
- Integrate LLMs, APIs, and data pipelines into reliable production services — including data engineering tie\-ins with systems like Firestore and Redshift
- Build and implement agentic AI systems capable of autonomous decision\-making and task execution
- Own evaluation frameworks — accuracy, latency, cost per call, fallback behavior, and exception handling
- Implement guardrails, monitoring, and audit trails for production AI systems
- Collaborate with Product and Ops to define success metrics before building
- Leverage modern LLM ecosystems, including Claude\-based and code\-first AI frameworks, to build intelligent agents and workflows
- Stay current with advancements in AI/ML, especially in agent frameworks and automation tooling
- Build lightweight frontend interfaces from product prototypes, and partner with full\-stack engineering to productionize and scale successful prototypes
Required
- 3\+ years in software engineering with hands\-on experience shipping AI or automation systems in production enterprise environments
- Strong grasp of agentic AI concepts — tool use, function calling, exception routing, and confidence handling at scale
- Clear understanding of where LLMs and agentic systems outperform RPA — and where they don't
- Comfortable working alongside RPA developers — understands how to hand off structured outputs into downstream deterministic workflows
- Experience with LLM APIs (Claude, OpenAI) including prompt design, orchestration, and evaluation
- Workflow orchestration experience — n8n, LangChain, Temporal, or similar
- Familiarity with no\-code/low\-code automation tools including Zapier, with the ability to know when to move beyond them
- Experience building rules\-based automation with structured inputs, exception handling, and audit trails
- Familiarity with RAG and vector databases
- Experience integrating with TMS or ERP platforms, including calling core platform/internal APIs to execute production actions
- Backend proficiency in Node.js; comfort with React for building lightweight interfaces; comfortable partnering with full\-stack and data engineering teams to ship into production systems
- AWS experience (Lambda, ECS, S3\)
- Familiarity with databases and data warehouses such as MongoDB, RDS (PostgreSQL/MySQL), Firestore, and Redshift
- Experience integrating third\-party APIs and building microservices
- Active GitHub or demonstrated track record of shipping AI systems in production beyond the prototype stage
Benefits \& Perks
Competitive compensation
Medical, dental, and vision benefits
401K company match program
Flexible paid time off (PTO) and paid holidays
Equal Opportunity Employer
Cargomatic is an equal opportunity employer. We celebrate diversity and are committed to creating an inclusive environment for all employees.
Salary Context
This $150K-$210K range is above 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 Cargomatic, 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 ($180K) sits 18% below the category median. Disclosed range: $150K to $210K.
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.
Cargomatic AI Hiring
Cargomatic has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Remote, US. Compensation range: $210K - $210K.
Remote Work Context
Remote AI roles pay a median of $185,334 across 717 positions. About 14% of all AI roles offer remote work.
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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