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About This Role
Intradiem is reinventing customer service for everyone by helping organizations connect people, technology, and real\-time action to create more agile operations and more human experiences.
Who We Are
Intradiem is the leader in Dynamic Workforce Orchestration. We help organizations create service agility by connecting people, technology, and real\-time data to improve customer experiences and support employees. We believe automation should empower humans, not replace them.
What We Do
Our platform turns real\-time insights into real\-time action. By orchestrating work across customer service teams, we help organizations break down silos, adapt instantly to changing demand, and operate more efficiently without losing the human touch which is essential to customer service.
How We Work
We solve real operational problems alongside our customers. Our approach is practical, collaborative, and focused on outcomes. We listen first, build with purpose, and create technology solutions that work in the real world.
Our Culture
We're proud to be a people\-first company. We treat employees with trust, respect, and flexibility because great customer experiences start with supported teams. We move fast, collaborate closely, and believe the best ideas come from empowered people working together.
Our Values
We encourage our employees to contribute time and energy to causes that help improve the people and communities in which they live and work. We are guided by three core values:
Servant's Heart—We believe in caring enough about other people to understand what their problems are and placing the needs of colleagues, customers, and others over personal objectives.
Craftsman's Attitude—We believe in taking pride in the work we do and creating solutions that actually solve the problem at hand (and trying again if the first attempt doesn't do the trick).
Growth Mindset—We believe that progress comes from curiosity, openness, and perseverance. We welcome feedback, embrace change, and push beyond comfort zones to unlock new potential for ourselves, our teams, and our customers.
Strategic Leadership \& Roadmap
- Own and drive Intradiem's internal AI transformation roadmap, translating company strategy into a prioritized, phased execution plan
- Define and manage the AI use case pipeline across intake, evaluation, prioritization, and sequencing across all business functions
- Serve as the organizational owner of Intradiem's AI operating model, driving the shift from ad hoc adoption to structured, scalable capability
- Establish enterprise AI architecture through strategic framework selection and standardized, reusable development patterns that support consistent delivery across the business
Team \& Program Leadership
- Lead and grow a dedicated AI Enablement team responsible for use case delivery, workflow automation, and business enablement
- Balance strategic direction with hands\-on involvement, willing to lean in directly on tooling, architecture decisions, and use case execution where needed
- Own the management, administration, and optimization of internal AI tools and platforms, ensuring they are well\-governed, widely adopted, and delivering measurable value
- Report directly to the VP of Technology and serve as the primary internal voice and decision\-maker for AI transformation
Investment \& Vendor Decisions
- Evaluate, select, and manage AI products, platforms, and services in partnership with the VP of Technology, with authority to move at speed and scale
- Support AI transformation budget allocation in coordination with leadership, ensuring spend is tied to measurable business outcomes
Cross\-Functional Coordination \& Governance
- Build, manage, and chair the AI Center of Excellence, Intradiem's cross\-functional steering body for AI prioritization, adoption, and accountability
- Establish and maintain AI governance frameworks covering responsible use policies, intake processes, and decisioning in partnership with Legal, Security, Compliance, and the People Team
- Serve as the designated internal spokesperson for the AI program, collaborating across teams and with strategic partners to communicate progress and maintain alignment
Enablement \& Change Management
- Lead change management efforts to move the business through the AI transformation, building fluency, confidence, and sustainable adoption at all levels
- Design and deliver role\-based, in partnership with Learning Services, AI enablement programs, workshops, and learning initiatives tailored to technical and non\-technical teams
- Partner with functional leaders to identify, develop, and deploy AI solutions that reduce manual work, accelerate decisions, and create competitive leverage
- Foster a culture of experimentation, innovation, and AI fluency across the organization
Measurement \& Accountability
- Develop and own key AI KPIs tracking adoption, active use cases, time\-to\-value, and business impact (including ROI and cost/efficiency savings)
- Drive quarterly goal accountability for the AI program, reporting progress to leadership and the steering committee
- Stay current on emerging AI technologies, regulatory developments, and industry trends to continuously sharpen strategy and decision\-making
What We're Looking For
- 10\+ years of experience in SaaS, enterprise software, technology enablement, or transformation roles.
- 3 to 5\+ years leading AI adoption initiatives, automation programs, or enterprise change management efforts
- Hands\-on experience with AI and GenAI platforms such as OpenAI, Anthropic, Microsoft Copilot, etc…, and a working understanding of LLMs, APIs, and AI\-native tooling
- Proven ability to lead organizational change and drive adoption across distributed, cross\-functional teams
- Strong communication and executive presentation skills, with the ability to make complex technical concepts clear and actionable
- Experience building and managing high\-performing teams in fast\-paced environments
- Familiarity with AI governance, ethical AI frameworks, data privacy standards, and evolving AI regulations
Work Authorization:
*Candidates must have the legal right to work in the country where the role is based at the time of application. Verification of identity and employment eligibility will be required during onboarding.*
*Intradiem is an equal\-opportunity employer. We evaluate qualified applicants without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, veteran status, or any other protected characteristic.*
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 Intradiem, 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. Director-level AI roles across all categories have a median of $247,800.
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.
Intradiem AI Hiring
Intradiem has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Remote, US.
Remote Work Context
Remote AI roles pay a median of $170,000 across 1,926 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 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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