Interested in this AI/ML Engineer role at Motus?
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About This Role
Motus is the industry leader in vehicle reimbursement and risk mitigation solutions for employees who drive. Combining 80 years of expertise with innovative technology, Motus enables organizations to optimize spend and increase productivity across their workforce. With solutions purpose-built to enable data-driven insights and strategic decision making, Motus is the preferred vehicle reimbursement partner to top Fortune 500 companies globally.
At Motus, we're dedicated to making WorkLife better for everyone, anywhere. Our team is the heart of our culture, and we live by our WorkLife Pillars every day – WorkHappy, WorkHealthy, WorkSmart, WorkAnywhere, and WorkTogether.
#### Position Description:
Motus is seeking a Demand Generation Specialist, Campaign Operations to own and streamline the operational engine behind our demand generation campaigns. Reporting to the VP of Demand, the position involves building, managing, and optimizing campaign and email infrastructure to enable faster, more scalable campaign launches.
This individual will serve as the connective tissue across the Demand Generation team. Partnering closely with channel owners, campaign managers, content, design, and marketing operations, you will ensure campaigns are executed perfectly and efficiently from build to launch.
The ideal candidate is highly organized, systems-minded, and motivated by creating repeatable processes that allow teams to move quickly without sacrificing quality to generate pipeline for the business.
#### Position Duties:
- Own end-to-end campaign and email operations, from intake and setup through launch and optimization
- Build and maintain scalable campaign infrastructure, including landing pages, forms, workflows, and email programs
- Manage HubSpot campaign orchestration, ensuring accurate segmentation, routing, automation, in partnership with Marketing Ops
- Partner with Demand Gen channel owners to translate campaign strategy into executable technical builds
- Collaborate closely with Marketing Operations and broader marketing stakeholders to align on priorities, timelines, and execution plans
- Develop and document repeatable processes, templates, and best practices to reduce time to campaign launch
- Ensure campaign QA, testing, and readiness across systems prior to launch
- Support ongoing optimization of campaign operations to improve efficiency, scalability, and performance
- Act as a trusted operational advisor to the Demand Gen team, proactively identifying risks and opportunities for improvement
#### Desired Skills & Experience:
- 3-5+ years of experience in demand generation, campaign operations, or marketing operations
- Practical experience using HubSpot, including email, workflows, landing pages, and campaign reporting
- Hands-on expertise with Salesforce campaign structure and creation
- Strong understanding of B2B demand generation mechanics and campaign lifecycle management
- Shown ability to manage multiple campaigns simultaneously while meeting tight deadlines
- Outstanding attention to detail and commitment to operational excellence
- Highly collaborative communicator with experience partnering cross-functionally
- Methodical mindset with a passion for building scalable systems
- Comfortable working in a fast-paced, growth-oriented environment
- Experience with Asana preferred.
Where required by law, Motus provides a reasonable range of compensation for specific roles. The pay range for this role is $85,000 - $90,000. Actual compensation will depend on a number of factors, including the candidate's relevant experience, technical skills, and other qualifications. This position is eligible for company benefits including medical, dental, and vision insurance with an employer contribution, flexible spending or health savings account, life and AD&D insurance, short-and long-term disability coverage, paid time off, employee assistance, participation in a 401k program with company match, and additional voluntary or legally required benefits.
Please see below for an outline of the Motus benefits package. Motus supports both the physical and mental health of their employees.
#### Motus Benefits:
- Medical Insurance, Dental Insurance, Vision Insurance (effective day one)
- Open Paid Time Off
- Flexible Spending Accounts & Health Savings Accounts
- Motus-Fidelity 401K Plan
- Company-paid Short/Long-term Disability & Basic Life Insurance Plans
- Family Planning and Parenting Support Benefits through Maven
- Support your mental, physical, professional and financial well-being through coaching and clinical therapy with Modern Health
- $1000 Home Office Reimbursement Program
- $2000 Internal Referral Program
- WorkAnywhere Reimbursement of Internet and Cellular Costs
- 16 weeks maternity and adoption leave
- 12 weeks paternity leave
Motus champions the power of true individuality, actively celebrating and accepting each team member. We strategically recruit and retain talent reflecting our local communities' rich diversity, fostering a culture where innovation thrives. Through dynamic learning sessions, strategic training, and our lively Employee Resource Groups, we kindle substantial dialogues, continuous learning, and ensure every voice is not only heard but celebrated.
Motus, LLC provides equal employment opportunity to all individuals regardless of their race, color, creed, religion, gender, age, sexual orientation, national origin, disability, veteran status, or any other characteristic protected by state, federal, or local law.
###### #LI-REMOTE
Salary Context
This $85K-$90K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $170K across 217 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 26,159 AI roles we're tracking, AI/ML Engineer positions make up 91% of the market. At Motus, 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 $154,000 based on 8,743 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $147,000. This role's midpoint ($87K) sits 43% below the category median. Disclosed range: $85K to $90K.
Across all AI roles, the market median is $190,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $300,688. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Safety ($274,200). By seniority level: Entry: $85,000; Mid: $147,000; Senior: $225,000; Director: $230,600; VP: $248,357.
Motus AI Hiring
Motus has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Chicago, IL, US. Compensation range: $90K - $90K.
Location Context
AI roles in Chicago pay a median of $203,250 across 214 tracked positions. That's 7% above the national 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 26,159 open positions tracked in our dataset. By seniority: 2,416 entry-level, 16,247 mid-level, 5,153 senior, and 2,343 leadership roles (Director, VP, C-Level). Remote roles make up 7% of the market (1,863 positions). The remaining 24,200 roles require on-site or hybrid attendance.
The market median for AI roles is $190,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $300,688. Highest-paying categories: AI Engineering Manager ($293,500 median, 21 roles); AI Safety ($274,200 median, 24 roles); Research Engineer ($260,000 median, 264 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 26,159 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (23,752), AI Software Engineer (598), AI Product Manager (594). 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 (2,416) are outnumbered by mid-level (16,247) and senior (5,153) 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 2,343 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 7% of all AI roles (1,863 positions), with 24,200 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 $190,000. Top-quartile roles start at $244,000, and the 90th percentile reaches $300,688. 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 $293,500 median, while Prompt Engineer roles sit at $145,600. 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: Rag (16,749 postings), Aws (8,932 postings), Rust (7,660 postings), Python (3,815 postings), Azure (2,678 postings), Gcp (2,247 postings), Prompt Engineering (1,469 postings), Openai (1,269 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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