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About This Role
Hiring Manager: Jeff Martin, CTO
We're looking for an AI Enablement Engineer to drive AI adoption across every team at Applause. You 'll build and maintain a monthly roadmap of AI\-powered workflows for departments like Sales, Customer Success, Marketing, Finance, and HR/TA, then actually implement them: standing up tools, wiring integrations into our core systems, training the team on how to use them, and tracking whether they're moving the needle. This is a builder role, and you'll work closely with our CTO and Chief Architect, who own the broader AI vision and governance, while you own execution.
Responsibilities:
- AI Workflow Roadmap \& Delivery
- + Build a monthly roadmap of AI workflows to design and ship, prioritized by productivity or cost impact, for every department (except Product and Engineering)
+ Identify manual, repetitive work across departments and design AI\-driven workflows to replace or accelerate it
+ Ship the workflows you scope: configure tools, build automations, and get them into daily use, not just document recommendations
+ Iterate and fine\-tune workflows post\-launch based on usage and feedback
- Systems \& Integrations
- + Support system implementations for AI and adjacent tools, partnering with the CTO and Engineering on technical requirements and data access
+ Connect AI tools into core business systems (CRM, HRIS, finance tools, Slack, etc.), so workflows run inside the systems teams already use
+ Evaluate and pilot new AI tools for specific departments and use cases, and recommend what to adopt, pause, or retire
- Adoption, Training \& ROI
- + Train department teams on the workflows and tools you build, and follow up to make sure they're actually being used
+ Troubleshoot adoption issues directly with end users rather than escalating them
+ Track usage and cost of AI tools by team and use case, and run monthly ROI reporting
+ Report progress monthly to the CTO on workflow delivery, adoption, and productivity/cost impact
+ Level up teams to build and own their own AI workflows over time, so departments grow more self\-sufficient rather than depending on you for every change
Requirements
- + Bachelor's degree in Computer Science, Data Science, Engineering, or a related technical field (or equivalent hands\-on experience)
+ 2–4 years of experience in AI/LLM implementation, workflow automation, technical business operations, or a related hands\-on technical role
+ Able to write and modify code for simple, internal business applications (even if AI writes most of it)—production\-grade or engineering\-quality code is not required
+ Demonstrated experience building integrations or automations (e.g., Zapier, Make, APIs, low\-code/no\-code platforms)
+ Working knowledge of modern AI tools Claude, Copilot, and similar) and how to apply them to business workflows
+ Comfortable working cross\-functionally with non\-technical teams (Sales, CS, Marketing, Finance, HR) to scope and deliver workflows
+ Strong communicator who can train and troubleshoot with non\-technical users
+ Detail\-oriented with strong ownership over initiatives, start to finish
+ Very effective in a remote work environment
Benefits Benefits: 100% remote w/ no office mandate \| Unlimited PTO \| 22 official company holidays \| Health care \| Life insurance \| Stock options \| Amazing colleagues \| Energetic culture that is positive and celebrates together \| Inspiring mission \& software product \| Ability to grow your career by being early in a fast\-growing tech startup
About Applause
Applause is a SaaS start\-up founded by experienced entrepreneurs and backed by the best VCs in Silicon Slopes. We’re focused on helping companies supercharge their team’s performance so they can win more lifelong customers. Learn more about our company and culture here:
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 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Applause, 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 $218,750 based on 3,817 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $200,000.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
Applause AI Hiring
Applause has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Salt Lake City, UT, US.
Location Context
Across all AI roles, 14% (508 positions) offer remote work, while 3,180 require on-site attendance. Top AI hiring metros: New York (1,045 roles, $220,000 median); San Francisco (810 roles, $277,088 median); Los Angeles (397 roles, $215,000 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 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 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 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 $300,000 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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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