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About This Role
About Submer
Submer designs and delivers end\-to\-end AI datacenter infrastructure built around industry\-leading liquid cooling.
We help organizations scale AI beyond the limits of traditional datacenters by enabling higher density, greater efficiency and lower environmental impact – accelerating time\-to\-AI from first deployment to full production.
What impact you will have
We are seeking a Senior MEP Design Engineer \- HPC/AI Data Centers to lead the full Mechanical, Electrical, and Plumbing (MEP) design and delivery of high\-performance computing (HPC) and AI data centers. This senior role owns the integrated MEP architecture for large\-scale (100\-300 MW) facilities and modular data center (MDC) deployments that scale into 50\-300 MW HPC/AI sites. You will ensure seamless coordination across mechanical, electrical, and plumbing systems to support next\-generation GPU clusters, with a strong emphasis on NVIDIA Grace Blackwell (GB300\) and Vera Rubin (VR200\) reference architectures.
This is a hands\-on technical leadership position ideal for an engineer who can own end\-to\-end MEP design for mission\-critical, high\-density environments.
What you'll do
- Lead the holistic MEP design, specification, and optimization for hyperscale and modular HPC/AI data centers, ensuring integrated performance, reliability, efficiency, and scalability.
- Design electrical systems including MV/LV distribution, switchboards, overhead busways, circuit breakers, transformers (ML/VL), PDUs, grounding, protection, BESS, UPS, and generators.
- Oversee mechanical systems focused on high\-density liquid cooling (direct\-to\-chip, immersion, CDUs, in\-row/in\-rack solutions), CRAC/CRAH, heat rejection, airflow management, and redundancy for 100\+ kW racks.
- Manage plumbing and fluid systems, including coolant distribution, primary/secondary cooling loops, leak detection, piping, pumps, filtration, and integration with facility water systems or closed\-loop designs.
- Architect modular data center solutions (1\-2 MW containers/pods) that efficiently aggregate into larger 50\-300 MWHPC/AI sites, emphasizing standardization, rapid deployment, and future\-proofing.
- Deliver MEP designs tailored to NVIDIA GB300 (Grace Blackwell) and VR200 (Vera Rubin) Superpod/reference architectures, addressing rack power (\~120 kW\+), liquid cooling manifolds, power smoothing, high\-current delivery, and thermal management requirements.
- Perform system\-level studies (power, thermal, hydraulic, CFD) and ensure compliance with NEC, NFPA, ASHRAE, IEEE, Uptime Institute, and local codes.
- Collaborate with architecture, structural, IT, and construction teams throughout design, procurement, construction, commissioning, and handover phases.
- Optimize for energy efficiency, sustainability, capacity planning, fault tolerance, and total cost of ownership in AI factory environments.
- Develop standards, create and review drawings/specifications, troubleshoot issues, and mentor junior MEP engineers.
- Create internal and customer\-facing MEP specifications and explain/discuss with technical and non\-technical customers.
What you'll need
- Bachelor's degree (Master's preferred) in Mechanical Engineering, Electrical Engineering, or a related MEP\-focused discipline. PE license strongly preferred.
- 10\+ years of progressive experience in MEP engineering for mission\-critical facilities, with at least 5\-7 years in large\-scale data centers or equivalent high\-density environments.
- Demonstrated ability to design complete HPC/AI data centers across all three MEP disciplines (Mechanical, Electrical, Plumbing/Cooling).
- Proven track record with HPC/AI data centers at 100\-300 MW scale and modular deployments (1\-2 MW units scaling to 50\-300 MW sites).
- Strong knowledge of NVIDIA Grace Blackwell (GB300\) and Vera Rubin (VR200\) architectures, including integrated power, cooling, and fluid system requirements.
- Expertise in high\-density liquid cooling, Grid\-to\-Rack power distribution, all MEP equipment in single\-line power specification, 13\.8kV/34\.5kV to IT white spaces with 480V AC and/or 800V DC power distribution, BESS integration, MV/LV power distribution, busways, and critical infrastructure redundancy (N\+1, 2N, etc.).
- Proficiency with relevant design and analysis tools (e.g., ETAP, Revit, AutoCAD, CFD, SKM).
- Excellent leadership, communication, and cross\-disciplinary collaboration skills.
### Preferred Qualifications
- Experience with 800V DC architectures, advanced power smoothing, and sustainable cooling technologies.
- Experience with modular data center design, structural integrity, and shipping/logistics of MDCs.
- Background at hyperscale operators, AI infrastructure providers, or modular data center projects.
- Familiarity with TIA\-942, ASHRAE TC 9\.9, and high\-performance computing facility standards.
- Prior involvement in full lifecycle projects from concept through commissioning and operations.
What we offer
- Attractive compensation package reflecting your expertise and experience.
- Medical Insurance Plan.
- 401k Employee volunteer contribution Plan.
- A great work environment characterized by friendliness, international diversity, flexibility, and a hybrid\-friendly approach.
- You´ll be part of a fast\-growing scale\-up with a mission to make a positive impact, offering an exciting career evolution.
Our job titles may span more than one job level. The actual base pay is dependent on a number of factors, such as transferable skills, work experience, business needs and market demands.
Our inclusive responsibility
Submer is committed to creating a diverse and inclusive environment and is proud to be an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, gender, gender identity or expression, sexual orientation, national origin, genetics, disability, age, veteran status, or any other protected category under applicable law.
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 Submer, 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.
Submer AI Hiring
Submer has 2 open AI roles right now. They're hiring across AI/ML Engineer. Based in Houston, TX, US.
Location Context
Across all AI roles, 14% (508 positions) offer remote work, while 3,180 require on-site attendance. Top AI hiring metros: New York (1,045 roles, $220,000 median); San Francisco (810 roles, $277,088 median); Los Angeles (397 roles, $215,000 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 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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