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About This Role
Company Description
Mirantis, an IREN company, is the Kubernetes\-native AI infrastructure company, enabling organizations to build and operate scalable, secure, and sovereign infrastructure for modern AI, machine learning, and data\-intensive applications. By combining open source innovation with deep expertise in Kubernetes orchestration, Mirantis empowers platform engineering teams to deliver composable, production\-ready developer platforms across any environment—on\-premises, in the cloud, at the edge, or in sovereign data centers. As enterprises navigate the growing complexity of AI\-driven workloads, Mirantis delivers the automation, GPU orchestration, and policy\-driven control needed to manage infrastructure with confidence and agility. Committed to open standards and freedom from lock\-in, Mirantis ensures that customers retain full control of their infrastructure strategy. https://www.mirantis.com/
Job Description Why this role exists
K0rdent AI is the orchestration layer that turns raw, disaggregated GPU infrastructure into a multi\-tenant, production\-ready AI cloud — without locking companies into a single hyperscaler or hardware vendor. We sell accelerated compute: GPU clusters, bare metal, and managed AI infrastructure to Neoclouds, AI\-native startups, enterprise AI teams, research labs, and sovereign/regulated buyers. These are technical, high\-value, long\-cycle deals where the sale is won or lost on credibility: whether we can architect the right cluster, model the real TCO, prove performance, and de\-risk a customer's move onto our platform.
This person owns the technical win. They build and lead the sales engineering function that turns "interested" into signed, multi\-year committed\-capacity contracts, and they set the pre\-sales bar as we scale headcount and deal volume.
This is not a demo\-jockey role. We need someone who has genuinely stood up training and inference workloads, argued interconnect topology with a customer's ML infra lead, and closed large deals with cycles measured in quarters, not weeks.
What you'll own
Lead and build the SE / Solutions Architect team
- Hire, coach, and retain a team of sales engineers and solutions architects; define the pre\-sales operating model as the org scales.
- Build the reusable machinery: discovery frameworks, reference architectures, TCO/benchmark models, POV playbooks, demo and benchmark environments, RFP response libraries.
- Set and hold a technical quality bar across the team; run enablement so every SE can speak credibly to GPU architecture, networking, and orchestration.
Own the technical win in large, complex deals
- Partner with Account Executives as the technical lead on strategic and enterprise opportunities from discovery through technical close.
- Run qualification with a real methodology (MEDDPICC or equivalent) — surface the economic buyer, decision criteria, and the technical champion, and build the win plan around them.
- Architect solutions across compute, networking, storage, and orchestration; produce sizing, capacity plans, and TCO comparisons vs. hyperscalers and self\-build.
- Design and drive POCs/POVs: define success criteria up front, run benchmarks, and convert results into commercial momentum.
Be the Technical voice of the Customer internally
- Feed structured product and capacity requirements back to product, platform, and supply/capacity planning.
- Work alongside the NVIDIA field and partner ecosystem (Cloud Partner program, reference architectures, joint pursuits) to strengthen deals.
- Influence roadmap and packaging based on what you learn in the field.
Qualifications Must\-have qualifications:
Real, hands\-on AI/ML infrastructure experience
- You have actually run or stood up ML workloads — distributed training and/or production inference — not just talked about them.
- Practical fluency in the training and inference lifecycle: data pipelines, distributed training (multi\-node/multi\-GPU), fine\-tuning, and serving; you understand where bottlenecks actually live (interconnect, memory bandwidth, I/O, scheduling).
- Comfortable in the frameworks and tooling customers use — PyTorch and the surrounding ecosystem (e.g., NCCL, CUDA\-level concepts, containers, schedulers).
Deep knowledge of the NVIDIA platform and GPU products
- Current on the NVIDIA compute stack across the Hopper and Blackwell generations (e.g., H100/H200, GB200 NVL72 / B200\-class systems, Grace\-Hopper superchips) and the reference\-system families (DGX, HGX, MGX); aware of what's coming next\-generation.
- Networking fluency: NVLink/NVSwitch domains, InfiniBand (Quantum) vs. Spectrum\-X Ethernet fabrics, RDMA/RoCE, DPUs — and why fabric choice makes or breaks large training clusters.
- Software and platform layer: NVIDIA AI Enterprise, NIM, NeMo, Triton / TensorRT\-LLM, Base Command, Run:ai / GPU orchestration, and the NGC ecosystem.
- Understands the NVIDIA Cloud Partner motion and how to co\-sell with NVIDIA.
Enterprise sales engineering on long, high\-value cycles
- Track record supporting complex B2B deals with cycles of 6–18\+ months and large ACV/TCV, ideally including multi\-year committed\-capacity or reserved\-capacity structures.
- Skilled at multi\-stakeholder navigation — ML/infra leads, platform engineering, procurement, finance, security, and executive sponsors.
- Can build and defend a TCO/ROI model against hyperscaler and on\-prem alternatives, and translate performance benchmarks into commercial value.
Proven team leadership
- Has hired, developed, and led a sales engineering / solutions architecture team (or clearly demonstrated the readiness to), including building process and enablement from a light or greenfield starting point.
- Player\-coach mindset: still credible in the room on the hardest deals, while scaling others to do the same.
Strongly preferred
- Experience selling GPU cloud, HPC, or specialized infrastructure — ideally at a NeoCloud / GPU\-cloud provider, hyperscaler AI org, or accelerated\-hardware vendor.
- Hands\-on with cloud\-native and cluster orchestration for AI: Kubernetes (and GPU operators / device plugins), Slurm, and multi\-cluster management approaches; familiarity with virtualized GPU / KubeVirt\-style patterns is a plus.
- Storage\-for\-AI literacy — high\-throughput parallel/object storage and its role in training pipelines.
- Experience with data center economics and constraints: power, cooling, rack density, and how capacity availability shapes deals.
- Exposure to sovereign, regulated, or government AI buyers.
What good looks like
First 90 days: deep on our platform and differentiators; embedded as technical lead on the top active opportunities; a clear read on the current team, gaps, and the pre\-sales process to fix first.
6 months: a repeatable POV and TCO framework in use across the team; measurable improvement in technical\-win rate and POC\-to\-close conversion; a hiring plan (or hires) closing the biggest coverage gaps.
12 months: a scaled, high\-credibility SE org that AEs actively pull into strategic deals; SE involvement correlated with larger deal size, faster technical close, and higher win rate on the deals that matter most.
Compensation \& logistics
Structure: competitive base \+ variable tied to team bookings/attainment, plus equity.
- Indicative OTE: senior people\-leader band for AI\-infra pre\-sales, strong candidates in this space command a premium.
- Location / travel: remote or. hub\-based, expect meaningful travel to customers, data centers, and NVIDIA/partner events.
Additional Information What does Mirantis offer you?
- Work with an established Silicon Valley leader in the cloud infrastructure industry;
- Work with exceptionally passionate, talented and engaging colleagues, helping Fortune 500 and Global 2000 customers implement next\-generation cloud technologies;
- Be a part of cutting\-edge, open\-source innovation;
- Thrive in the high\-energy environment of a young company where openness, collaboration, risk\-taking, and continuous growth are valued;
- Professional development and training;
- Attend conferences and working groups;
- Company outings, happy hours, hackathons, and tech talks;
- Receive a competitive compensation package with a strong benefits plan.
We are a Leader for Container Management in G2 (\#2 after AWS)!
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 Mirantis, 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 $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.
Mirantis AI Hiring
Mirantis has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in San Jose, CA, US.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 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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