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About This Role
AI Vision Engineer
About us:
CargoMatrix Inc. is building next\-generation AI\-powered logistics and operations platforms that combine automation, AI agents, computer vision, Realtime workflows, and enterprise integrations into one unified operational system.
We are not building basic CRUD apps.
We build:
AI\-powered logistics workflows
warehouse and parcel automation systems
OCR and Vision AI solutions
Realtime operational dashboards
AI assistants for logistics operations
integrations with enterprise logistics systems
mobile scanning and warehouse applications
intelligent workflow orchestration systems
We move fast, ship real products, and solve difficult operational problems.
What We Care About
We care more about:
what you’ve built
how you think
how fast you can execute
your ability to solve problems
…than traditional resumes or degrees.
Show us:
products
systems
prototypes
GitHub projects
AI workflows
demos
side projects
Ideal Candidate
You are:
highly technical
product\-minded
AI\-native
fast\-moving
self\-driven
obsessed with building great user experiences
comfortable with ambiguity
excited about AI transforming real industries
Please include:
Please send:
LinkedIn profile
GitHub
portfolio/projects
examples of AI systems you’ve built
anything you’re proud of building
Are you
An engineer who enjoys solving complex visual AI problems and turning emerging AI technologies into practical, reliable solutions.
Comfortable experimenting with new models and techniques while maintaining the engineering discipline required to deploy scalable, production\-ready AI systems.
Passionate about computer vision, multimodal AI, and building intelligent systems that understand the visual world, we would like to hear from you.
Position Overview
A talented and motivated AI Vision Engineer will design, develop, and deploy advanced computer vision and visual AI solutions. This role will focus on building intelligent systems capable of understanding and analyzing images and video using modern computer vision, deep learning, and multimodal AI technologies.
The ideal candidate combines strong software engineering skills with hands\-on experience developing machine learning and computer vision models. You will work on real\-world AI applications and help move solutions from research and experimentation into scalable production environments.
Key Responsibilities
- Design, develop, and optimize computer vision and visual AI solutions for image and video analysis.
- Build and train deep learning models for object detection, image classification, segmentation, tracking, OCR, anomaly detection, and other vision applications.
- Develop image and video processing pipelines using Python, OpenCV, and modern AI frameworks.
- Fine\-tune and evaluate CNN, transformer\-based, and vision\-language models for domain\-specific applications.
- Collect, prepare, analyze, and manage image and video datasets used for model development.
- Develop data augmentation and preprocessing strategies to improve model performance and robustness.
- Evaluate models using appropriate metrics, including precision, recall, F1 score, IoU, mAP, latency, and inference performance.
- Conduct detailed failure analysis and identify opportunities to improve model accuracy and reliability.
- Optimize AI models for real\-time and production inference using technologies such as ONNX, TensorRT, CUDA, and model quantization.
- Deploy computer vision models to cloud, edge, GPU, and production environments.
- Build APIs and services that integrate AI vision capabilities into enterprise applications and workflows.
- Implement model monitoring, experiment tracking, dataset versioning, and reproducible machine learning pipelines.
- Collaborate with software engineers, data engineers, product teams, and business stakeholders to translate requirements into practical AI solutions.
- Research and evaluate emerging computer vision, multimodal AI, and vision\-language technologies.
Required Qualifications
- Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Machine Learning, Data Science, Engineering, or a related technical field, or equivalent practical experience.
- Strong programming skills in Python.
- Experience with computer vision and image processing techniques.
- Hands\-on experience with PyTorch, TensorFlow, or similar deep learning frameworks.
- Experience developing deep learning models using CNNs, transformers, or related architectures.
- Knowledge of object detection, image classification, segmentation, or video analysis.
- Strong understanding of machine learning fundamentals, including training, validation, optimization, and model evaluation.
- Experience working with image and video datasets, annotation workflows, and data augmentation.
- Familiarity with Git, Linux, Docker, and modern software development practices.
- Strong analytical and problem\-solving skills.
Preferred Qualifications
- Experience with OpenCV and modern computer vision libraries.
- Experience with object detection frameworks and architectures such as YOLO, Faster R\-CNN, or similar technologies.
- Experience with vision\-language models, multimodal AI, visual embeddings, or visual search.
- Experience optimizing models using ONNX, TensorRT, CUDA, quantization, or GPU acceleration.
- Experience deploying AI models to cloud or edge computing environments.
- Proficiency in C\+\+ for high\-performance or real\-time vision applications.
- Experience with MLOps tools for experiment tracking, model versioning, dataset versioning, and production monitoring.
- Knowledge of camera calibration, 3D vision, stereo vision, depth estimation, or geometric computer vision.
- Experience developing real\-time image or video processing systems.
Technical Skills
Programming: Python, C\+\+
Computer Vision: OpenCV, image processing, video analytics, object detection, segmentation, tracking, OCR
AI \& Deep Learning: PyTorch, TensorFlow, CNNs, transformers, vision\-language models
Model Deployment: ONNX, TensorRT, CUDA, model quantization
Infrastructure: Docker, Linux, Git, cloud and edge deployment
MLOps: Experiment tracking, model versioning, dataset management, model monitoring
Pay: $73,896\.88 \- $88,994\.10 per year
Benefits:
- 401(k)
- Dental insurance
- Health insurance
- Life insurance
- Paid time off
- Vision insurance
Work Location: Hybrid remote in Hewlett, NY 11557
Salary Context
This $73K-$88K 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 CargoMatrix Inc., 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 ($81K) sits 63% below the category median. Disclosed range: $73K to $88K.
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.
CargoMatrix Inc. AI Hiring
CargoMatrix Inc. has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Hewlett, NY, US. Compensation range: $88K - $88K.
Location Context
Across all AI roles, 14% (508 positions) offer remote work, while 3,180 require on-site attendance. Top AI hiring metros: New York (1,045 roles, $220,000 median); San Francisco (810 roles, $277,088 median); Los Angeles (397 roles, $215,000 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 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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