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About This Role
Material Bank is the world's largest material marketplace for the architecture and design industry. Operating in 37 countries, our platform has become the standard for design professionals around the globe. Every day, Material Bank connects thousands of designers with tens of thousands of materials from leading brands. Material Bank is the fastest and most powerful way for design professionals to search, sample, and specify materials.
We're looking for a Senior Data \& AI Engineer to lead the design, development, and operation of AI agents that power intelligent experiences across the Material Bank platform. This role sits at the intersection of data engineering, applied AI, and platform innovation, with the opportunity to shape how AI is embedded into the core of our business and customer experience. You'll be the technical lead defining how we build AI agents, with direct access to the teams interfacing with Snowflake with room to influence architecture decisions, and the chance to work across the full AI stack from data modeling and semantic layers to agent orchestration and production operations.
This is an exciting opportunity for someone who is, at their core, a passionate data engineer with deep curiosity about AI and significant experience building strong data foundations before expanding into applied AI and agent based systems. We are looking for someone who enjoys solving complex technical problems, experimenting with emerging technologies, and turning ambiguous ideas into scalable, production ready solutions. Working hands\-on with Snowflake Cortex as our primary AI platform, you will help push the boundaries of what modern AI systems can do in an enterprise environment while helping define the future of intelligent experiences at Material Bank.
What You'll Do
- Design, build, and operate production grade AI agents, owning the full lifecycle from prototyping and evaluation through deployment, monitoring, and continuous improvement.
- Lead the development of scalable AI and data services, including MCP servers and REST APIs that expose intelligent capabilities to products, applications, and internal teams.
- Serve as our internal expert on Snowflake Cortex, going deep on Cortex Agents, Cortex Analyst, and Cortex Search while partnering directly with Snowflake's account and product teams to influence capabilities and shape how we apply the platform.
- Apply modern agent architecture patterns including RAG, tool use, orchestration, memory, and evaluation frameworks to build reliable, accurate, and cost efficient AI systems.
- Partner closely with Analytics \& Insights team to design and maintain semantic and metrics layers that create consistent business definitions across AI, analytics, and reporting use cases.
- Build and maintain scalable data pipelines, transformations, and models that power AI workloads using Snowflake, dbt, and Airflow.
- Collaborate across data, product, analytics, and engineering teams to translate ambiguous business problems into well designed AI and data solutions.
- Establish engineering standards and best practices for agentic systems, including observability, evaluation, prompt management, governance, and operational guardrails.
What You'll bring:
- Deep experience and genuine passion for data engineering, with strong instincts around data modeling, pipeline architecture, scalability, data quality, and building reliable platforms. Strong data foundations are core to this role.
- 5\+ years of experience in data engineering, AI/ML engineering, or related fields, including recent hands on experience building and shipping LLM powered applications or AI agents into production environments.
- Experience building production APIs and services, including MCP servers and REST based architectures.
- Strong understanding of modern agent development patterns including RAG, vector search, prompt engineering, tool/function calling, and frameworks such as LangChain, LangGraph, or LlamaIndex.
- Deep expertise in Snowflake, including performance optimization, warehouse architecture, and scalable data modeling approaches such as dimensional modeling or Data Vault.
- Production experience with dbt and Airflow, including building and maintaining semantic or metrics layers.
- Strong Python engineering skills and solid experience working within AWS environments including services such as S3, IAM, Lambda, ECS, or similar.
- Hands on experience using AI powered engineering tools such as Claude Code or similar development accelerators as part of real world engineering workflows.
- Excitement about specializing deeply in Snowflake Cortex and helping define our long term AI platform strategy.
Nice to Have
- Hands on experience working with Snowflake Cortex in production environments.
- Experience with LLM evaluation, tracing, and observability platforms such as LangSmith, Arize, or Langfuse.
- Experience partnering closely with analytics or BI teams to operationalize business metrics and semantic models.
- Experience with Go, or a demonstrated ability to quickly learn and apply new technologies and programming languages.
*What you'll get from us:*
- *Our people**: We are a growth\-driven team that values efficiency, builds smart automation, operates in small empowered teams, and moves quickly from idea to execution.*
- *Relaxation and Celebrations**: Flexible PTO, Sick Days, Paid National Holidays, and even more (ask us about this when we connect).*
- *Health Benefits**:* *We* *contribute* *to your medical, dental, vision and short\-term/long\-term* *disability plans* *and have a strong employee assistance program.*
- *Plan for your Retirement**:* *401(k)* *eligible* *after your first 90 day's employed!*
- *Giving Back**: We sponsor multiple events throughout the year to help out our communities.*
- *Growth**: We'll help you take your career to the next level. We want you to be creative and take initiative which will allow you to grow and create within the company. Most importantly, be the best at what matters!*
- *Flexible Work Schedules: With business units and employees across the globe, Material Technologies has embraced a* *hybrid working*** *model allowing department leaders to decide on the best approach for their respective teams, whether that be remote, in person, or a little of both.*
Material Bank is proud to be an equal opportunity employer. We value diversity, and all applicants will be considered for employment without attention to race, color, religion, sex, sexual orientation, gender identity, age, national origin, veteran or disability status or other status protected under any applicable federal, state or local law.
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 3,823 AI roles we're tracking, AI/ML Engineer positions make up 69% of the market. At Material Bank, 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 $181,170 based on 12,692 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
Material Bank AI Hiring
Material Bank has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Fort Lauderdale, FL, US.
Location Context
Across all AI roles, 15% (590 positions) offer remote work, while 3,217 require on-site attendance. Top AI hiring metros: New York (2,643 roles, $211,000 median); San Francisco (2,168 roles, $253,000 median); Los Angeles (1,792 roles, $191,580 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 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 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 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). 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 (112) are outnumbered by mid-level (1,798) and senior (1,516) 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 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 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 $200,100. Top-quartile roles start at $253,500, 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 Engineering Manager roles lead at $275,000 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 (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 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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