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About This Role
Awardco is reimagining the workplace to be more rewarding, supportive, and fun for everyone. As one of the fastest\-growing companies in the employee experience industry, our mission is to help employees love what they do, love where they work, and get recognized for their efforts—especially our own employees! And as winners of Glassdoor’s Best Places to Work, Best in Brightest in the Nation, and Great Place to Work, we do much more than talk the talk.
Awardco is looking for a Senior Business Systems Engineer with deep Salesforce expertise and a passion for AI\-driven innovation to join our System Operations team in Lindon, Utah.
This is a high\-impact, senior technical IC role responsible for designing and scaling the systems that power how Awardco runs, especially across Salesforce, integrations, automation, and AI\-enabled workflows.
You will operate as both a builder and architect, owning end\-to\-end system design and implementation across complex, cross\-functional business processes. If you thrive on solving complex systems challenges and are excited about building the next generation of AI\-augmented business operations, we want to hear from you.
What you will do:
- Own and evolve Awardco's Salesforce architecture and integrated systems ecosystem, serving as a technical authority on design, scalability, and integration strategy.
- Design scalable, enterprise\-grade data flows, workflows, and system integrations that support multiple business functions across GTM, Revenue, and Customer Operations.
- Lead complex, cross\-system initiatives end\-to\-end — spanning Salesforce, RevOps tools, and data platforms — from scoping and architecture through delivery and documentation.
- Build and maintain automation frameworks across GTM and customer lifecycle workflows that teams can rely on without manual intervention.
- Architect and implement AI\-driven workflows and system augmentations — production\-grade solutions that surface insights, automate decisions, and reduce friction, not standalone experiments.
- Establish and uphold best practices for system reliability, observability, and governance — including monitoring, alerting, and incident response across critical business systems.
- Influence roadmap, architecture standards, and governance practices the broader team builds on.
- Partner with RevOps, Marketing Ops, Data, Product, and Engineering teams on system design and cross\-functional initiatives.
- Reduce operational complexity through deep process and systems redesign, replacing workarounds and tribal knowledge with systems that just work.
*This is not a Salesforce admin or configuration\-only role — it is a systems architecture and engineering role embedded in the business.*
#### What you will bring:
- 5\+ years in Salesforce / Business Systems / Revenue Systems Engineering
- Deep Salesforce expertise (Sales Cloud, Service Cloud; CPQ and RCA strongly preferred)
- Proven experience designing complex, multi\-system architectures
- Strong understanding of:
- + System integrations (APIs, middleware, event\-driven architecture — including emerging AI connectivity patterns such as MCP (Model Context Protocol) servers and tool\-use APIs that enable LLMs to act across business systems.)
+ Data modeling and workflow design, including semantic layers that expose business data in formats AI models can reliably query and reason over.
+ Automation at scale (Flow, Apex, or equivalent tools)
- Experience working in high\-growth SaaS or enterprise environments where systems must scale rapidly and cross\-functional alignment is critical to delivery.
- Strong systems thinking with the ability to operate at both strategic and execution levels — including knowing when to apply deterministic automation versus AI\-based reasoning, and how to evaluate AI tooling trade\-offs in a production context.
- Comfortable leading ambiguous, high\-complexity technical initiatives, including early\-stage AI integration work where best practices are still emerging.
#### Nice to Have Qualifications:
- Salesforce certifications including Platform Developer I or II; Application Architect or System Architect strongly preferred; Salesforce AI Associate or AI Specialist a plus.
- Experience with enterprise integration platforms (Workato, MuleSoft, etc.)
- Familiarity with data platforms (Snowflake, dbt, etc.)
- Exposure to AI systems, agents, or LLM\-powered workflows
- Experience designing RevOps / GTM system architecture at scale
Why Awardco:
- We have a revolutionary, client\-approved product.
- One of the fastest growing companies in the nation: 3x Inc. 500, 2x Deloitte Technology Fast 500, 2x Mountain West Capital Network Fast 100, 3x Fast 50 (Utah Business), and 3x UV50 Fastest Growing Companies (BusinessQ), to name just a few.
- Great Place to Work certified, ranked in Inc. Best Workplaces, one of the Best and Brightest companies to work for, and ranked on the Salt Lake Tribune's Top Workplaces.
- Backed by renowned investors, both local and national.
*Awardco is an Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, or protected veteran status and will not be discriminated against on the basis of disability.*
*Disclaimer:* *Please be aware that all official communication regarding your application will only come from an email address ending in @awardco.com. If you receive any communication from a different domain, it may be fraudulent, and we encourage you to report it.*
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 Awardco, 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.
Awardco AI Hiring
Awardco has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Lindon, UT, 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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