Interested in this AI/ML Engineer role at AIcadium?
Apply Now →Skills & Technologies
About This Role
Aicadium's Applied Research team is looking for a senior computer vision / ML scientist to tackle open-world visual understanding with limited labels across 2D recognition, multimodal inspection, and 3D/4D perception. You will bring anomaly detection, zero- and few-shot recognition, and semantic segmentation to applied systems used across multiple industries, building on 2D and 3D vision foundation models and vision-language models (VLMs). You will also help shape the team's research roadmap, with a focus on 3D/4D reconstruction, spatial perception, and world models. This is a hybrid role based in San Diego, where our U.S. headquarters is located. The ideal candidate has deep hands-on experience in modern computer vision, has taken models beyond the prototype stage, and can own problems end-to-end while supporting the development of junior scientists.
Responsibilities:
- Own applied research on vision problems (anomaly detection, zero- and few-shot recognition, and semantic segmentation) end-to-end, from problem framing and baselines through validated models.
- Adapt and extend 2D and 3D vision foundation models and VLMs to industrial domains through fine-tuning, harness optimization, and distillation.
- Develop multimodal (vision-language) methods for industrial inspection tasks.
- Conduct applied research in 3D/4D reconstruction, spatial perception, world models, and physics-based simulations, from proof-of-concept prototypes to reusable internal capabilities.
- Design data and evaluation strategy for low-label, long-tail settings: annotation plans, active learning, and evaluation protocols that translate to stronger performance in the field.
- Systematically analyze and improve deployed vision systems. Collaborate with engineering and product teams to bring new models to production, and maintain and improve model quality after launch.
- Provide technical guidance to junior scientists and communicate methods, results, and trade-offs to technical and non-technical stakeholders.
Skills & Requirements:
- PhD or Master's in Computer Science, Electrical Engineering, Robotics, Machine Learning, or a related field with 4+ years of hands-on computer vision / ML experience; doctoral research counts toward this requirement.
- Expertise in at least two of the following: anomaly detection, zero- and few-shot (open-vocabulary) recognition, semantic segmentation, or multimodal (vision-language) learning.
- Experience adapting vision foundation models (e.g., DINOv3, JEPA-family models
, SAM series) and VLMs (e.g., the Qwen-VL series) to new domains.
- Hands-on experience in 3D/4D reconstruction and spatial perception, including modern 3D foundation models (e.g., VGGT, MASt3R, Depth Anything 3), grounded in multi-view geometry and camera models.
- Ability to independently define research problems and turn recent research into working prototypes, with rigorous experimental practice: strong baselines, ablations, and sound evaluation on real-world data, including rare-event and heavily imbalanced problems.
- Strong proficiency in Python and PyTorch, with a solid foundation in applied mathematics (linear algebra, geometry, probability, statistics, optimization).
- Clear written and verbal communication skills, including presenting research to technical and non-technical audiences and stakeholders.
Nice to Have:
- Publications at top-tier venues (CVPR, ICCV, ECCV, NeurIPS, ICLR) or notable open-source contributions in related areas.
- Experience bringing computer vision models from prototype to production or customer-facing deployment.
- Experience generating synthetic training data with generative models or simulation, particularly for defects, anomalies, and rare or data-deficient scenarios and classes.
- Domain experience in industrial inspection, manufacturing, aerospace, food processing, medical imaging, or other fields with similar rare-defect or visual verification problems.
- Experience with world models, such as V-JEPA 2, NVIDIA Cosmos, or spatially grounded VLMs.
Experience with VLAs and transformer-based action planners.
About Us:
Aicadium is a global technology company delivering AI-powered industrial computer vision products into the hands of enterprises. With offices in Singapore and San Diego, California, and an international team of data scientists, engineers, and business strategists, Aicadium is operationalizing AI within organizations where advanced machine learning innovations were previously out of reach.
Team
Join a growing team of data scientists, machine learning, and software engineers in an agile development environment. Work together with some of the best in the field to tackle challenging projects and operationalize the solutions you develop across a variety of industries and use cases.
Culture
We work in a casual and collaborative startup environment. Every member of the team plays a key role in shaping the solutions we develop and creating positive business value for the companies we work with. We are building a hub of the best talent in San Diego, CA, but we are open to working with people all over the U.S.
Benefits
Aicadium has a great benefits package to come with your salary. Benefits include PTO, health, vision, and dental insurance, life and AD&D, 401k with matching, and more!
Salary Context
This $140K-$175K range is below the median for AI/ML Engineer roles in our dataset (median: $178K across 2239 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 11,520 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At AIcadium, 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. Senior-level AI roles across all categories have a median of $230,000. This role's midpoint ($157K) sits 26% below the category median. Disclosed range: $140K to $175K.
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.
AIcadium AI Hiring
AIcadium has 2 open AI roles right now. They're hiring across AI/ML Engineer. Based in San Diego, CA, US. Compensation range: $110K - $175K.
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
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.