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About This Role
Safety Mojo is the standard for modern safety management in high-risk industries, including construction and oil & gas. Through Safety Mojo—our enterprise-grade, AI-powered platform—we empower frontline safety professionals to mitigate risk and prevent incidents in real time. We recently closed a Series B and are on an exciting trajectory. Join us!
We are seeking an experienced and highly skilled Senior Engineer with notable experience in AI workflows and models to join our team. The ideal candidate will have 6+ years of general development experience along with years of experience with N8N, and AI model integration. You will play a critical role in designing, developing, and scaling AI systems to support high-traffic applications, ensuring reliability, security, and performance.
You will manage a territory and active book of business—orchestrating comprehensive account management, rigorous pre-call planning, and strategic champion development. This role requires an entrepreneurial, consultative closer who thrives in fast-paced environments and excels at converting technical pilot programs into multi-year, enterprise-wide production contracts.
What You'll Own
- AI Processes Development: Design, develop, and maintain high-performance, high-throughput AI processes both client facing and internal facing.
- AI Quality Management: Craft systems to ensure quality output and refinement of AI systems.
- AI Research and Implementation: Research and investigate new AI tools. Come up with and execute an implementation plan
- Scalability & Performance Optimization: Architect and implement scalable systems that can handle large volumes of traffic and data while ensuring high availability and low latency
- Code Quality & Best Practices: Write clean, well-documented, and reusable code, following industry best practices, security guidelines, and standards.
- Cross-functional Collaboration: Work closely with developers, product managers, and other stakeholders to understand requirements and provide solutions that align with business goals.
- Troubleshooting & Debugging: Diagnose and resolve complex issues in a timely manner, implementing robust solutions to prevent future occurrences.
- Continuous Improvement: Stay up-to-date with emerging technologies, tools, and industry trends, advocating for improvements to technology and team processes.
What You Bring
- 6+ years of general development experience, with experience on Node.js, and React.
- 5+ years of Experience integrating with AI Models
- Understanding how to set up workflows/processes to reliably handle AI processed data to preform a talk in a quality way matching the quality of input.
- Excellent problem-solving skills and the ability to work effectively in a fast-paced, team-oriented environment.
- Strong communication skills, with the ability to convey complex technical concepts to both technical and non-technical stakeholders.
Nice To Have’s
- Experience with Neural Networks or training Custom AI Models
- Experience with Locally Running AI models and AI Models on mobile devices.
- Experience with N8N workflows
- Experience with Data Analytics or AI driven Data Analytics
More Job Details
Division: Engineering
Department: Engineering
Reports To: Director of Engineering
Location: In-Office, Sandy HQ
Role Type: Individual Contributor
Class: Exempt
Employment: Full-Time
Pay Type: Salary + Bonus
Address: 111 E Sego Lily Dr., Ste 200, Sandy, UT 84070
Compensation & Benefits
The base salary range and other applicable forms of compensation are listed below. Safety Mojo is committed to paying competitively. At the time of offer we’ll work with you to ensure you’re comfortable with actual compensation based on experience, skills, and location.
Base Pay: $120,000 to $175,000 per year
Safety Mojo offers a strong combination of medical, dental, vision, life, disability, 401(k) with match, and generous PTO benefits. Please note that our medical insurance includes two options: traditional plan and HDHP with HSA with employer contributions.
Important Notes
EEOC & Inclusion
We are an equal opportunity employer and are committed to building an inclusive environment for all employees. We do not discriminate based on race, color, religion, sex (including pregnancy, sexual orientation, and gender identity), national origin, age, disability, genetic information, or any other protected status under applicable law.
American with Disabilities Act
We are committed to providing reasonable accommodations for qualified individuals with disabilities in accordance with the Americans with Disabilities Act. If you require assistance during the application or interview process, please let us know.
At-Will Employment
Employment with Mojo AI is at-will. This means that either the employee or the company may terminate the employment relationship at any time, with or without cause or notice, subject to applicable law.
Background Check Requirement
Employment is contingent upon successful completion of a background check and verification of authorization to work in the United States.
Thank you for considering joining us at Safety Mojo as we work to create safer work environments in partnership with some of the world’s best companies!
Salary Context
This $120K-$175K range is below the median for AI/ML Engineer roles in our dataset (median: $178K across 1623 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 9,079 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Mojo AI, 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 $211,950 based on 13,048 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $229,300. This role's midpoint ($147K) sits 30% below the category median. Disclosed range: $120K to $175K.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
Mojo AI AI Hiring
Mojo AI has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Sandy, UT, US. Compensation range: $175K - $175K.
Location Context
Across all AI roles, 6% (531 positions) offer remote work, while 8,478 require on-site attendance. Top AI hiring metros: New York (3,078 roles, $221,000 median); San Francisco (2,646 roles, $261,000 median); Los Angeles (1,486 roles, $210,000 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 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 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 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). 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 (106) are outnumbered by mid-level (4,384) and senior (3,343) 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 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 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 $213,000. Top-quartile roles start at $263,911, and the 90th percentile reaches $318,400. 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 $256,000 median, while Prompt Engineer roles sit at $141,440. 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 (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 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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