Interested in this AI/ML Engineer role at InMotion Hosting?
Apply Now →About This Role
The Opportunity
We're growing the InMotion family of brands from $50M\+ to $100M\+ over the next five years, with AI central to that plan. We believe AI gives mid\-market companies the leverage to compete with much larger competitors. We want to be part of solving for that and we've already built early momentum in this direction. We're looking for someone to own what we've started and scale it further.
Market Timing
-------------
Success in this role depends on understanding where customer segments fall on the adoption curve, from early adopters to the early and late majority, and using that insight to time product decisions correctly. Part of the job is judging when a product is ready for a given segment, and when it isn't yet.
Our AI\-enabled process moves considerably faster than traditional product development, and this role needs to operate at that pace. At any given time, you'll be managing multiple groups of products moving through the pipeline in parallel: a first group ready for market testing, a second group close behind it in development, and a third group in ideation. As results come in, the expectation is to keep that pipeline moving — strong signals from group one mean advancing it to go\-to\-market, group two moves into refinement, and a new group three starts ideation to keep the cycle continuous.
Your Role
---------
You'll be taking over active, in\-motion product lines currently run by our CEO. You're joining work that's already underway, with people already engaged in it, and your job is to bring additional judgment, market fluency, and operating discipline to scale it further. Beyond what's already in motion, you'll also be responsible for developing the broader product suite and shaping what comes next, not just carrying forward what already exists.
This role has direct access to all leaders, including the Board. Your expertise, drive, and successes should raise our collective respect and understanding of how great products are created and kept fresh.
You'll report to the CRO. Onboarding into the product lines will be handled directly by our CEO, who is currently doing this product work.
What Product Ownership Means Here \- "CEO of Your Product Lines"
----------------------------------------------------------------
We use this framing deliberately, drawing on Ben Horowitz's description of what a good product manager is:
- You drive vision and own the outcomes of your product line.
- You know your market, product, and competition front to back, thus you operate from real knowledge rather than guesswork.
- You create and own the release calendar, and bring urgency to the leadership team to help you meet it on time.
- You stay close to the sale, especially in the beginning. You test the pitch yourself, hearing objections firsthand and often closing early customers directly.
- You're accountable for the right product at the right time.
- You anticipate resource constraints before they become blockers, and you're skilled at either securing what you need or reworking your plan so the product still ships without it.
The Platform
------------
Products are delivered through two brands, each targeted at a different segment. InMotion Hosting serves our core $3M–$10M ARR customer base, with an established track record in web hosting and infrastructure. InMotion Cloud is our newer offering, aimed at larger use cases and companies further up the ARR range, roughly $10M–$20M, providing fixed\-cost, flat\-fee virtual private cloud infrastructure that lets customers scale resources without unpredictable billing. You'll choose whichever brand fits a given product and customer segment best.
Core Responsibilities
---------------------
- Own release cadence and go\-to\-market timing for your product lines, from minor releases through new launches.
- Maintain a current, living product strategy.
- Drive your own market sensing, competitive analysis, and customer research, using AI tools to prototype and validate ideas.
- Stay involved in the decisions that matter: architecture and engineering tradeoffs, customer discovery, and sales.
- Sell the product yourself in the early going. This proves out your own knowledge of the product, drives the initial launch, and surfaces the direct feedback needed to tweak and refine the product to raise conversion rates.
- Track customer adoption and value realization after the sale, not just the sale itself.
- Coordinate with marketing and sales so go\-to\-market readiness is built into the release timeline.
Who You Are
-----------
- An early adopter of AI methods and tools yourself — actively using them in your own research, prototyping, and decision\-making, with genuine curiosity about emerging approaches like AI\-driven software factories, even though this is new territory for everyone.
- A product leader with a rich professional track record, including clear, demonstrated successes leading up to today.
- Comfortable working in both AI\-native and more traditional development environments, with the judgment to know which approach fits a given customer.
- Experienced owning product strategy and go\-to\-market for a product line at a growth\-stage company, ideally with direct sales involvement.
- A track record of shipping on time and owning your outcomes.
- Comfortable with real autonomy and the accountability that comes with it.
Compensation
------------
- Starting base salary: $180,000\-$220,000 based on previous successes
- Performance bonus of up to 40% of base ($72,000\-$88,000\), tied to corporate revenue results and to successful, timely product launches and their reception in the market as measured by new MRR.
B61P3dONQZ
Salary Context
This $180K-$220K range is above the median for AI/ML Engineer roles in our dataset (median: $175K across 2162 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 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At InMotion Hosting, 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 in Demand for This Role
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 $214,900 based on 6,420 positions with disclosed compensation. Director-level AI roles across all categories have a median of $274,554. This role's midpoint ($200K) sits 7% below the category median. Disclosed range: $180K to $220K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
InMotion Hosting AI Hiring
InMotion Hosting has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Remote, US. Compensation range: $220K - $220K.
Remote Work Context
Remote AI roles pay a median of $180,000 across 1,196 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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 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
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.