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Firmus Technologies
Firmus Technologies is a global leader pioneering the development and operation of efficient AI infrastructure across Asia Pacific.
Founded in Australia in 2019, our mission is to create the most efficient AI infrastructure by combining cutting\-edge technology with a steadfast commitment to sustainability. At Firmus, we are unique in our approach. We design, build, and operate a new class of digital infrastructure – the AI Factory. Through our model\-to\-grid technology approach, we have pushed the boundaries of multi\-generational liquid cooling systems, energy management, AI software orchestration, and construction. For our customers, this approach allows us to make every watt count and deliver low\-cost AI tokens globally.
Firmus AI Cloud
Our large\-scale GPU cloud platform, Firmus AI Cloud, is purpose\-built to deliver energy\-efficient AI compute at scale to customers.
It empowers developers, enterprises, educational institutions, and government users to train and deploy AI models with unmatched efficiency and cost savings. With an ever\-growing suite of services and applications, we are committed to delivering a cloud experience that is market\-leading, proprietary, and built to scale.
Role Summary
Firmus is executing one of the most ambitious AI infrastructure build\-outs in the world — building out approximately 2 million GPUs through to 2028, creating entirely new markets for sovereign AI compute, and scaling to become one of the most prolific AI infrastructure companies globally. Alongside this build\-out, a fast\-growing population of AI\-native companies — model labs, foundation\-model startups, and inference/fine\-tuning platforms — are scaling their GPU footprints at extraordinary speed, and represent one of Firmus's highest\-growth commercial segments.
The AI Native Account Lead owns a portfolio of Firmus's AI\-native GPU capacity relationships — fast\-moving, high\-growth customers with GPU spend today ranging from single\-digit millions to tens of millions in ARR, many on a credible path toward significantly larger, Gigascale capacity commitments as they scale. This is a Senior Individual Contributor role, part of the AI Natives coverage team, with the mandate to close new capacity commitments, grow existing accounts, and deliver a best\-in\-class customer experience to some of the fastest\-moving companies in AI.
This is a commercial ownership role spanning account management, capacity commercialisation, and cross\-functional delivery orchestration, typically covering a portfolio of several accounts at once given the segment's pace and scale. You will translate customer AI compute roadmaps into structured GPU capacity commitments, working closely with capacity planning, network engineering, and delivery teams to ensure every commitment is met on schedule.
You will own deal structuring and negotiation for contracts ranging from single\-digit millions to tens of millions of dollars in value — with some accounts scaling well beyond that — including pricing, terms, delivery milestones, and expansion rights, while identifying and progressing growth opportunities as customer compute needs scale. This position is ideal for someone who thrives on the speed and ambiguity of working with high\-growth, founder\-led companies, and who wants exposure to the leading edge of the AI industry.
Key Responsibilities
- Own end\-to\-end account management for a portfolio of AI\-native GPU capacity customers, acting as the primary commercial point of contact.
- Translate customer AI compute roadmaps into structured GPU capacity commitments, working closely with capacity planning and network engineering teams.
- Lead deal structuring and negotiation for contracts from single\-digit millions to tens of millions of dollars, including pricing, terms, delivery milestones, and expansion rights.
- Run a regular executive and technical cadence with each account — keeping commitments, performance, and escalations visible to both the customer and Firmus leadership.
- Coordinate with the shared coverage pod — Solution Architecture, Customer Success, Technical Program Management, and Engineering — to ensure customer commitments are met on schedule and risks are surfaced early.
- Own the customer feedback loop into Firmus engineering — capturing technical issues, capacity requirements, and roadmap requests and translating them into clear input for engineering and product priorities.
- Identify and execute account growth opportunities as customer compute needs scale, including helping accounts graduate toward Gigascale, Strategic Account\-led coverage.
- Maintain clear reporting on account health, pipeline, capacity utilisation, and risk to the Head of AI Natives.
Skills \& Experience
- 5\+ years in strategic account management, enterprise/complex sales, or commercial leadership in cloud infrastructure, data centre, semiconductor, or AI compute markets.
- Direct experience selling, negotiating, or managing GPU/compute capacity contracts, ideally with a hyperscaler, neocloud, or AI infrastructure background.
- Comfort working with founder\-led, fast\-moving customers, including experience managing multiple concurrent accounts.
- Strong technical fluency in GPU infrastructure, able to speak credibly to customers on capacity planning, deployment timelines, networking architecture, and power and data centre constraints.
- Track record of operating with significant autonomy in ambiguous, high\-growth environments.
Key Competencies
- Executive presence — able to represent Firmus credibly with founders and technical leaders at AI\-native companies.
- Ownership and accountability — acts as the accountable owner for account outcomes, from commercial terms through delivery execution.
- Commercial rigour — structures and negotiates contracts efficiently, at speed, while protecting long\-term account health.
- Cross\-functional orchestration — works effectively across capacity planning, delivery, finance and legal to keep commitments on track.
- Technical fluency — speaks credibly to GPU capacity, deployment timelines and infrastructure constraints without needing to be the solution architect.
- Portfolio management — juggles multiple fast\-moving accounts without losing sight of the details that matter to each.
Career Trajectory
- AI Native Account Lead: own and grow a portfolio of AI\-native accounts, proving commercial and delivery execution.
- Senior AI Native Account Lead / Strategic Account Lead (as accounts scale): expanded portfolio or transition to Gigascale, single\-account ownership as customers graduate to $1B\+ ARR territory.
Location \& Reporting
Reporting directly to the Head of AI Natives
Location: San Francisco Bay Area, CA, US
Employment Basis
Full\-time
Diversity
At Firmus, we are committed to building a diverse and inclusive workplace. We encourage applications from candidates of all backgrounds who are passionate about creating a more sustainable future through innovative engineering solutions.
Join us in our mission to revolutionize the AI industry through sustainable practices and cutting\-edge engineering. Apply now to be part of shaping the future of sustainable AI infrastructure.
About Firmus Technologies
Firmus Technologies is a global leader pioneering the solution to AI’s energy challenge, founded in Australia in 2019 by a visionary team of entrepreneurs and engineers passionate about sustainable computing infrastructure.
Firmus builds and operates AI infrastructure across Asia\-Pacific, utilising its proprietary AI Factory platform to deliver transformative cost\-effective GPU clusters and AI cloud services for developers, enterprise, education and government users.
We are committed to building a diverse and inclusive workplace. We encourage applications from candidates of all backgrounds who are passionate about creating a more sustainable future through innovative engineering solutions.
Join us in our mission to revolutionize the AI industry through sustainable practices and cutting\-edge engineering.
How to apply for this position
If you think you're the right fit for us, we'd love to hear from you!
Location
San Francisco, California, US
Employment basis
Full time
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 Firmus Technologies, 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. Senior-level AI roles across all categories have a median of $227,400.
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.
Firmus Technologies AI Hiring
Firmus Technologies has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in San Francisco, CA, US.
Location Context
AI roles in San Francisco pay a median of $265,000 across 1,335 tracked positions. That's 23% 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 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
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