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About This Role
Nexxais building the best AI systems for heavy industries — enabling machines, systems and operations to think, decide and act autonomously across manufacturing, large-scale infrastructure, logistics and legacy environments.
Our mission is to translate deep technical breakthroughs into operational reality, solving some of the hardest systems-level problems in industry.
Role Overview
We are looking for an AI Vision Engineer to help design, build, and deploy next-generation computer vision systems across a diverse set of real-world industrial applications.
This role is ideal for someone with a strong foundation in Computer Vision and Machine Learning who is excited about working across the full vision stack, including classical and deep-learning-based CV, vision-language models (VLMs), multimodal reasoning, and real-time inference at the edge and in the cloud.
You will work closely with our AI and engineering teams to develop production-ready vision solutions, improve model performance, and help shape the next generation of intelligent visual systems.
What You'll Do
- Design, train, evaluate, and deploy computer vision models for real-world industrial applications.
- Build and optimize CV pipelines for tasks such as object detection, segmentation, classification, OCR, tracking, and visual understanding.
- Develop and fine-tune vision-language models (VLMs) for multimodal reasoning, visual question answering, and document understanding.
- Design and optimize real-time inference pipelines for deployment on edge devices and in the cloud.
- Build scalable data pipelines for image and video collection, annotation, augmentation, training, and evaluation.
- Fine-tune and evaluate open-source vision and multimodal foundation models using modern training and inference frameworks.
- Develop robust evaluation frameworks and benchmarks to measure model accuracy, robustness, latency, and business impact.
- Optimize models for production constraints, including quantization, pruning, and hardware-accelerated inference.
- Collaborate with product, engineering, and research teams to translate business requirements into technical vision solutions.
- Contribute to architecture decisions, technical design reviews, and AI/CV best practices.
- Stay current with the latest advancements in computer vision, multimodal AI, and autonomous systems.
Required Qualifications
Education
Bachelor's degree in Computer Science, Computer Engineering, Electrical Engineering, Mathematics, Statistics, Artificial Intelligence, or a related technical field, or equivalent practical experience.
Experience & Skills
- 3+ years of industry experience in Computer Vision, Machine Learning, Applied AI, or related fields.
- Demonstrated experience independently owning and delivering computer vision projects from concept to production.
- Strong programming skills in Python.
- Hands-on experience with PyTorch and modern deep learning workflows.
- Experience developing and deploying computer vision models (detection, segmentation, classification, OCR) in production environments.
- Experience working with image and video processing libraries such as OpenCV.
- Experience with common CV/detection frameworks (e.g., YOLO, Detectron2, MMDetection, or similar).
- Experience working with VLMs, multimodal models, or Generative AI applications.
- Strong understanding of machine learning fundamentals, model evaluation, experimentation, and model deployment.
- Experience working with Hugging Face Transformers and open-source AI ecosystems.
- Familiarity with data annotation workflows, dataset curation, hyperparameter optimization, and inference optimization.
- Experience building production-grade software and AI systems.
- Strong analytical and problem-solving skills.
- Excellent communication and collaboration skills.
Preferred Qualifications
- Master's degree in Computer Science, Artificial Intelligence, Machine Learning, Computer Vision, or a related field.
- Experience with OCR, document understanding, or visual reasoning systems.
- Experience with Vision-Language Models (VLMs) and multimodal AI applications.
- Familiarity with 3D vision, SLAM, or sensor fusion (e.g., camera + LiDAR) for industrial or robotics applications.
- Experience with real-time inference optimization (TensorRT, ONNX Runtime, quantization, pruning).
- Experience deploying models on edge hardware (e.g., NVIDIA Jetson, embedded systems).
- Experience with LangChain, LangGraph, or agentic AI frameworks combining vision and language models.
- Experience with vector databases and visual/semantic retrieval systems.
- Experience deploying AI systems on AWS, GCP, or other cloud platforms.
- Experience with Docker, Kubernetes, and MLOps workflows.
- Experience with PostgreSQL and large-scale data systems.
- Experience with model serving, distributed training, and inference optimization at scale.
- Contributions to open-source projects, technical blogs, research publications, Kaggle competitions, or other demonstrable CV/AI work.
- Experience working in startup or high-growth environments.
What We're Looking For
- A builder who enjoys taking vision systems from prototype to production.
- Someone comfortable working across classical Computer Vision, deep learning, and multimodal Generative AI.
- An engineer who is curious, adaptable, and eager to learn emerging vision and AI technologies.
- A strong collaborator who can contribute across research, engineering, and product discussions.
- Someone excited about solving challenging real-world problems using computer vision.
- An individual who takes ownership, moves quickly, and thrives in an environment with significant autonomy.
Why JoinNexxa.AI?
- Innovative Environment: Play a critical role in transforming heavy industries through groundbreaking AI and automation technologies.
- Collaborative Culture: Be part of a team that values innovation, discipline, and continuous improvement.
- Professional Growth: Benefit from significant opportunities for career development and advancement.
- Competitive Compensation: Enjoy a comprehensive salary and equity package reflective of your expertise and contributions.
If you're passionate about development and eager to shape the infrastructure powering advanced AI solutions, we'd love to connect.
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 11,520 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At NEXXA, 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 $212,040 based on 13,780 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200.
Across all AI roles, the market median is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($266,050) and AI Engineering Manager ($253,461). By seniority level: Entry: $112,320; Mid: $193,200; Senior: $230,000; Director: $272,250; VP: $235,830.
NEXXA AI Hiring
NEXXA has 4 open AI roles right now. They're hiring across AI Product Manager, AI/ML Engineer. Based in Sunnyvale, CA, US.
Location Context
Across all AI roles, 6% (708 positions) offer remote work, while 10,737 require on-site attendance. Top AI hiring metros: New York (3,257 roles, $221,000 median); San Francisco (2,766 roles, $260,550 median); Los Angeles (1,580 roles, $210,000 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 11,520 open positions tracked in our dataset. By seniority: 140 entry-level, 5,630 mid-level, 4,176 senior, and 1,574 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (708 positions). The remaining 10,737 roles require on-site or hybrid attendance.
The market median for AI roles is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($266,050 median, 70 roles); AI Engineering Manager ($253,461 median, 58 roles); Research Engineer ($252,000 median, 476 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 11,520 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (8,769), AI Software Engineer (586), AI Product Manager (511). 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 (140) are outnumbered by mid-level (5,630) and senior (4,176) 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 1,574 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (708 positions), with 10,737 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 $213,480. Top-quartile roles start at $264,000, and the 90th percentile reaches $318,400. 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 $266,050 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 (2,267 postings), Aws (1,178 postings), Azure (972 postings), Rag (924 postings), Gcp (715 postings), Pytorch (586 postings), Claude (572 postings), Kubernetes (559 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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