Interested in this AI/ML Engineer role at Megaport?
Apply Now →About This Role
About Megaport
We’re not your typical tech company – and we don’t want to be. Megaport is the global leader in Network as a Service (NaaS), and has transformed the way businesses connect to the cloud, data centers, and each other. We’re publicly listed on the Australian Stock Exchange and partnered with the biggest names in tech like Amazon, Microsoft, Google, Oracle, IBM, and more. Headquartered in Brisbane with a crew of over 600 people spread across Asia\-Pacific, Europe, and the Americas, our employees enjoy an environment that is collaborative, supportive, and (actually) fun.
Our Team Culture
We’re a team of problem solvers, pixel pushers, code slingers, and cloud fanatics. Culture is more than a poster on the wall here – collaboration beats hierarchy, curiosity fuels our growth, and everyone’s voice matters. We take our work seriously, but not ourselves. We work across time zones to execute on our global vision, trust each other to get things done, and never compromise our values for commercial gain. Most importantly, we place our customers at the center of everything we do.
We’re committed to increasing representation in the tech industry and welcome applicants from all backgrounds. Don’t meet every requirement? That’s okay. If you’re excited about this role, we encourage you to apply.### The Role
We are seeking an experienced Technical Project Manager to spearhead the strategy, design vetting, and end\-to\-end delivery of our next\-generation, high\-density AI Data Center footprint. In this role, you will bridge the gap between abstract capacity needs and physical execution, serving as the primary orchestrator for complex, high\-capacity GPU cluster deployments.
You will lead pre\-approval technical vetting (evaluating space, power, cooling, and network floor plans before site acquisition) and take ownership of the full deployment lifecycle through to operational handoff.
Proactive and a self\-starter, you must be highly organized with the ability to manage competing priorities. Driven and motivated, you can operate both autonomously and in a collaborative team environment, coordinating across multiple internal groups and external vendors to deliver comprehensive high\-capacity GPU cluster deployments.
### What You’ll Be Doing
- Technical Design \& Floorplan Vetting:
+ Review, design, and vet floor plans
+ Create High\-Level Designs
+ Plan power distributions and cooling architectures.
+ Work alongside engineering and vendor partners prior to purchasing or leasing data center space.
- Infrastructure Planning:
+ Conduct technical diligence on data center facilities.
+ Validate that physical infrastructure (power redundancy, floor loading, high\-density liquid/air cooling capabilities) aligns with massive AI/GPU compute requirements (multi\-MW deployments, liquid\-cooled racks at 100kW\+, NVIDIA clusters).
- End\-to\-End Project Ownership:
+ Manage the complete project lifecycle for high\-capacity GPU cluster deployments.
+ Drive progress from pre\-approval site vetting through infrastructure design, physical implementation, cabling, commissioning, documentation, and final operational handoff.
- Cross\-Functional Coordination:
+ Serve as the central hub aligning Data Center operators, Network Engineering, Hardware Systems, Facilities, Procurement, and Enterprise IT teams around unified goals and timelines.
- Milestone \& Progress Tracking:
+ Establish critical paths, schedule dependencies, and project milestones.
+ Actively monitor progress while holding internal teams and external vendors accountable for updates.
- Risk Management \& Quality Control:
+ Proactively identify spatial, power, or logistical constraints early in the planning process.
+ Mitigate post\-procurement delays, budget overruns, and technical bottlenecks.
- Reporting \& Governance:
+ Maintain accurate project documentation, inventory tracking, and transparent status reporting for executive leadership and stakeholders.
### What We Are Looking For
- Technical knowledge and a proven track record delivering complex hardware infrastructure or data center engineering projects.
- 5\+ years of technical project management experience in high\-density or infrastructure environments.
- Strong familiarity with DC facility design, power delivery systems, thermal management, and spatial planning for high\-density environments.
- Direct experience or strong foundational understanding of high\-density AI cluster architectures.
- Ability to read, interpret, and refine CAD drawings, as well as create rack elevations, single\-line diagrams, and floor plans.
- Demonstrated ability to drive accountability across technical, engineering, and third\-party vendor teams.
- Strong hands\-on proficiency with modern project management software (Jira, Confluence).
- Exceptional written and verbal communication skills, with a track record of distilling technical complexities into concise updates for leadership.
### What We Offer
- Flexible working environment – a remote\-first culture with coworking options available
- Generous leave plans – including, parental leave, birthday leave, and a purchased annual leave program
- Health and wellness support – through a wellness allowance and employee wellbeing initiatives
- Comprehensive learning support – generous study and training allowance plus paid study leave
- Creative, modern workspaces – designed to inspire when you're not working remotely, plus access to coworking spaces via our global WeWork membership if you work remotely, but like to get out of the house sometimes
- Motivated, inclusive team – work alongside industry experts and fresh talent
- Recognition programs – celebrate achievements with our Legend and Kudos award
\#LI\-DNI
If you have any questions, please reach out to Megaport's Talent Acquisition Team at [email protected]
NOTE: *All Megaport business correspondence is conducted via our business email accounts (@megaport.com). If you have any concerns, please reach out to Megaport's careers team [email protected] directly and we will verify the legitimacy of any communication. Megaport will not ask you to create an account via Microsoft teams, and does not associate with any email accounts under "@megaportau.com".*
*All applications will be treated in confidence.*
*Please see Part 2 of our Privacy Policy to see what information Megaport collects from job applicants, why, and how we store and use it. Note that you’re entitled to know what personal data of yours Megaport holds, to request updates, rectification, and in some circumstances restriction or deletion thereof if you object (you being entitled to withdraw your consent to our holding your information at any time). Please see Part 5 of our Privacy Policy for more details on this and how to contact Megaport's data protection officer if you have any further privacy\-related questions. Candidates who meet the selection criteria will be invited to attend an interview. Strictly no Recruitment Agencies.*
We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses and identifying potential inconsistencies or verification signals in application materials based on available information. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.
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 Megaport, 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. Mid-level AI roles across all categories have a median of $194,400.
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.
Megaport AI Hiring
Megaport has 2 open AI roles right now. They're hiring across AI/ML Engineer, Data Scientist. Based in Remote, US.
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.