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About This Role
*MaintainX is the world's leading AI\-powered maintenance and asset management platform, serving 13,000\+ customers including Duracell, Shell, Cintas, and Brenntag. We raised* *$150M in Series D funding* *led by Bessemer Venture Partners and Bain Capital Ventures, bringing our total funding to $254M. We were named to the* *Forbes 2025 Cloud 100**, the definitive ranking of the top 100 private cloud companies in the world. We're growing fast and hiring the talent to match.*
About MaintainX
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MaintainX is the world's leading mobile\-first work execution platform for industrial and frontline teams. We help over 13,000 customers, including Duracell, Shell, Marriott, McDonald's, Volvo, DHL, and AB InBev, reduce unplanned downtime and run more efficient operations.
In July 2025 we closed a $150M Series D led by Bessemer Venture Partners, bringing total funding to $254M at a $2\.5B valuation. We were named to the 2025 Forbes Cloud 100, Forbes America's Best Startup Employers 2025, and ranked \#1 in EAM and CMMS on G2's Summer 2025 report.
The market is at a turning point. Manufacturing will need to fill 3\.8 million vacant jobs through 2033\. Technicians spend roughly 60% of their time on paperwork and research instead of actual wrench work. Most manufacturers expect to adopt AI\-driven maintenance at scale by 2027\. Our AI Platform is how we make that transition real for the frontline.
The Role
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Our AI Platform is the catalog of Agents, Skills, and Tools that every product engineer at MaintainX assembles to ship AI\-powered features. CoPilot, Root Cause Analysis, the Parts Agent, and the Scheduling Agent all run on it. As Principal Engineer, you will own the technical vision for the AI platform and scale it to match our ambition.
This is a senior individual contributor role reporting to VP of Engineering. You will lead through architecture, high\-leverage technical decisions and products that produce long\-term company outcomes. You will set direction across multiple divisions, align executives on platform strategy, and hold leaders accountable for engineering\-wide adoption.
What you will own:
- Define the Agent / Skill / Tool architecture. Evolve the platform so agents can reason, plan, and collaborate, skills are discoverable and reused across workflows, and tools expose structured, permission\-aware access to operational data. Design for progressive autonomy as trust, reliability, and governance mature.
- Scale the context graph. Architect the retrieval and knowledge systems that turn 14,000\+ digitized equipment manuals, 370,000\+ procedures created yearly, and 27M\+ annual work orders into customer\-specific intelligence with cross\-asset reasoning.
- Build the developer experience. Own agent orchestration, an MCP tool registry, reproducible dev environments, and the observability layer that makes agents trustworthy at scale. Engineers should define agent behavior through schemas and prompts while the platform handles routing, validation, evaluation, and observability.
- Build the evaluation and feedback loop. Design offline and online evaluation systems so every user interaction (accept, refine, override) improves agent performance.
- Drive cross\-division alignment. Partner with product engineering teams across Plant Setup, Maintenance Planning, Maintenance Execution, Reliability Engineering, Parts \& Purchasing, and Reporting. Represent MaintainX engineering internally and externally.
You have
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- 10\+ years of software engineering experience, with significant depth in backend systems, distributed architecture, or platform / infrastructure engineering.
- 3\+ years building ML or LLM\-powered systems in production, with real operating experience around evaluation, latency, cost, and reliability.
- A track record of company\-wide technical leadership at the Principal level. You have defined architecture for systems used by multiple teams, influenced engineering strategy beyond a single division, and held leaders accountable for adoption.
- Internal platform experience where your customers are other engineers. You treat developer experience as a product: adoption, documentation, enablement, and ergonomics are first\-class.
- Strong Python backend expertise, with experience designing APIs, services, and data pipelines.
- The ability to write a clear design document, facilitate a technical decision across teams, and represent engineering externally with credibility.
Nice to have:
- Production agentic systems: multi\-agent orchestration, tool use, skill registries.
- MCP, LangGraph, LlamaIndex, plus evaluation and observability platforms like Langfuse, LangSmith, or Braintrust.
- RAG pipelines, vector databases, embedding strategies, and knowledge graphs at scale.
- B2B SaaS, industrial software, IoT / OT, or other domains where AI must operate with high reliability and domain\-specific context.
We offer
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- Competitive base, equity, and variable comp aligned to role and location.
- Equity in a high\-growth, post\-Series D company.
- Day\-1 health, dental, and vision coverage.
- Unlimited PTO, which we actually take.
- Flexible token limits.
- Work from our San Francisco, Toronto, or Montreal hubs, or remote across the United States and Canada.
How we work
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We hire for what you have shipped, not what your title was. We promote quickly when people deliver and have honest conversations early when fit isn't right. Output wins. If you would rather shape the architecture that every other engineer builds on than manage headcount, this is the right room.
MaintainX is committed to building a diverse and inclusive team. We welcome applications from people of all backgrounds. If you're excited about this role but don't meet every qualification, we encourage you to apply.
\#LI\-Remote
*Our mission is to deliver one platform for maintenance, repair \& operations teams to keep the physical world running. We believe the greatest asset in any organization is the people. That’s why we built an intuitive, mobile\-first solution to help boost productivity and collaboration across teams and locations.*
*MaintainX is committed to creating a diverse environment. All qualified applicants will receive consideration for employment without regard to race, colour, religion, gender, gender identity or expression, sexual orientation, national origin, genetics, disability, age, or veteran status.*
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 MaintainX, 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. Senior-level AI roles across all categories have a median of $230,000.
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.
MaintainX AI Hiring
MaintainX has 2 open AI roles right now. They're hiring across Data Scientist, AI/ML Engineer. Based in US.
Location Context
AI roles in Austin pay a median of $214,343 across 87 tracked positions.
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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