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Location
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United States Remote; Canada Remote
Employment Type
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Full time
Location Type
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Remote
Department
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Product
Compensation
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- USA Compensation RangeEstimated Base Salary $216,504 – $270,630
- Canada Compensation Range Estimated Base Salary CA$207,350 – CA$259,188
Compensation offered will be determined by factors such as location, level, job\-related knowledge, skills, and experience.
### ABOUT THE TEAM
At Mural, we're building a new layer of collaboration — one where people and intelligent systems work together in real time. The Design and Research team turns complexity into clarity, shaping how humans and AI co\-create, collaborate, and reason inside a shared visual canvas. You'll lead a small team of designers, and you'll personally own the point of view on how agents behave across Mural's platform. This role sits at the intersection of people leadership and interaction craft: you're building both a team and a discipline.
At Mural, we “drink our own champagne” and use our product daily to collaborate, ideate, and prototype together. Your design work will shape how millions of people around the world collaborate.
### YOUR MISSION
As the Director of Product Design, AI \& Agentic Workflows you'll define what agent\-native design means at Mural, then build the team and the pattern language to make it real everywhere. You’ll work across all surfaces, defining the underlying grammar for how agents disclose intent, request permission, hand off control, signal confidence, and recover from error across every part of the product.
You'll lead a small team of designers who ship against that grammar, and you'll stay hands\-on yourself — prototyping the hardest, highest\-ambiguity interactions rather than delegating all of it away. This is a working director role: management and craft are both core to the job, not one traded off for the other.
You’ll have the chance to invent how the world collaborates. You’ll design experiences that help teams think visually, work faster, and create together in ways that feel natural and expressive. You’ll partner with a deeply creative and cross\-disciplinary team that values curiosity, clarity, and craft.
### WHAT YOU'LL DO
Team Leadership:
- Hire, develop, and manage a small team of product designers working on agentic and AI\-native experiences
- Set craft standards and help run critique; give direct, specific feedback that raises the bar
- Own your team's roadmap and staffing in partnership with product and engineering leads
- Represent your team's work, capacity, and needs to the VP of Design and cross\-functional leadership
Pattern Ownership:
- Define and maintain Mural's interaction pattern language for agent behavior, including permission, disclosure, handoff, confidence, error recovery. Think of it as the same relationship a design systems team has to typography and color, applied to agent behavior instead
- Use code and rapid prototyping as a primary design tool to pressure\-test agent behaviors, not just visuals — "vibe coding" real interactions to see how they actually feel
- Establish reusable primitives (components, states, motion, copy patterns) that the broader design org and other squads build on, so agentic patterns compound instead of getting reinvented per feature
- Personally take on the hardest, least\-defined interaction problems — the role stays in the work even as it scales through a team
Strategic, Cross\-functional Leadership:
- Represent design's point of view in product strategy conversations about where and how agents show up in the platform
- Partner with research to build a shared evidence base for control, autonomy, and trust tradeoffs
- Lead workshops and design sprints to align stakeholders around ambiguous, agent\-related decisions
- Contribute to Mural's external voice on AI\-native design as a natural extension of leading the discipline internally
### WHAT YOU'LL BRING
- 15\+ years of product design experience, including recent experience managing and growing designers — ideally in ambiguous, 0 1 problem spaces
- A track record of defining or evolving a design pattern language or system — behavior\-based systems preferred over purely visual ones
- Familiarity with agent\-based or AI\-assisted experience design: memory, context windows, tool use, autonomy/control tradeoffs
- Comfort using code as a design tool — prototyping and vibe\-coding to explore interaction and system behavior, not just wireframing it
- Strong systems thinking: you understand how design decisions cascade across flows, surfaces, and journeys
- Experience partnering with engineering to evolve prototypes into scaled, shipped implementations
- Experience representing design in senior, cross\-functional strategy conversations
- Deep empathy for diverse, global users, with experience designing for accessibility and inclusivity at scale
### Also great if you bring
- Experience growing the capabilities of a design team from a small base
- Background in multi\-agent systems, orchestration, or workflow automation
- Experience with canvas\-based or spatial interfaces
- Experience designing with structured and unstructured data in the same environment
- A history of shaping 0 1 products or platform\-level capabilities
- Public writing, speaking, or thought leadership on AI\-native design
### Equal Opportunity
We will ensure that individuals with disabilities are provided reasonable accommodation to participate in the job application or interview process, to perform essential job functions, and to receive other benefits and privileges of employment. Please contact us to request accommodation.
Compensation Range: $216,504 \- $270,630
Salary Context
This $207K-$270K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $175K across 2162 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 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At MURAL, 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 in Demand for This Role
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 $214,900 based on 6,420 positions with disclosed compensation. Director-level AI roles across all categories have a median of $274,554. This role's midpoint ($238K) sits 11% above the category median. Disclosed range: $207K to $270K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
MURAL AI Hiring
MURAL has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Remote, US. Compensation range: $270K - $270K.
Remote Work Context
Remote AI roles pay a median of $180,000 across 1,196 positions. About 15% 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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 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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