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About This Role
Company Description About Mirantis
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
About the role
We are seeking a Senior Solutions Architect with deep networking expertise to join the Voyager team in the Mirantis Office of the CTO. You will own the network solutioning of k0rdent AI, our platform for building and operating GPU clouds and AI factories, from metal to model.
You will design how large GPU clusters are interconnected, how tenants are isolated, and how workloads reach the network from containers, virtual machines and bare metal nodes. You will turn that design into published reference architectures, working code and proofs of concept that run on real hardware with our customers and partners.
As part of the Office of the CTO, research is a core part of the job. You will explore designs and technologies before customers ask for them, prototype ideas that may not ship, and challenge established practice when a better approach exists. Your work will shape where k0rdent AI goes next, not only how it is deployed today.
This role combines research with hands-on, outward-facing work. You will split your time between exploring new designs, writing architecture, building and validating it in the lab, and explaining it to engineers, executives and conference audiences. You will work with Solutions Architects, Partner Management and Engineering colleagues across many countries and time zones.
Key Responsibilities
Reference architecture
- Design and publish network reference architectures and solution designs for k0rdent AI, covering data center topologies from a single rack to multi-thousand-GPU clusters.
- Define compute (east-west) fabrics on InfiniBand and on RoCEv2 Ethernet, including rail-optimised and fat-tree / Clos designs, oversubscription and failure-domain choices.
- Define front-end, storage and management networks, and how they connect to customer data centers, public clouds and hybrid environments.
- Specify multi-tenant isolation across the stack: InfiniBand partitions (PKeys), VRFs and EVPN-VXLAN on Ethernet, and Kubernetes-level network policy.
- Document scale limits and trade-offs (cost, performance, operability, vendor lock-in) with defensible reasoning a customer can follow.
Host and platform networking
- Define how Linux hosts expose NICs, DPUs and SuperNICs to workloads: PCI passthrough, SR-IOV virtual functions, IOMMU groups, NUMA and GPU-NIC affinity.
- Design Kubernetes networking for AI workloads: primary and secondary CNIs, Multus, SR-IOV and RDMA device plugins, NVIDIA Network Operator, and Dynamic Resource Allocation (DRA).
- Design networking for virtual machines on KubeVirt, including passthrough and SR-IOV for GPU and RDMA traffic.
Research and exploration
- Track and evaluate emerging technologies and standards in AI networking, such as Ultra Ethernet (UEC), SuperNIC and DPU offloads, scale-up and scale-across fabrics, and Kubernetes DRA network drivers.
- Prototype alternative designs in the lab, including ones customers have not yet asked for, and measure them against current practice.
- Challenge established or customer-preferred designs when evidence points to a better option, and make the case with data.
- Publish findings as internal research notes and design proposals and, where appropriate, as external papers, blog posts or talks.
Code and proofs of concept
- Build and run proofs of concept with customers and partners, on Mirantis lab hardware and on customer sites, and report results against agreed success criteria.
- Write automation and tooling (Python, Go, Bash, Ansible, Helm, Kubernetes manifests, Terraform) that makes the reference architectures reproducible.
- Run validation and benchmarking of fabrics and host configurations (e.g. NCCL tests, perftest, ib_write_bw) and turn findings into design guidance.
Customers, partners and community
- Act as the network subject matter expert in customer discovery, design reviews and architecture workshops, bringing thought leadership and new ideas rather than only reflecting current practice.
- Work with hardware and networking partners (e.g. NVIDIA, server OEMs, switch and network vendors) on joint designs and validations.
- Innovate and feed research results back to Product and Engineering, and help shape the k0rdent AI roadmap.
- Present at industry events, webinars and partner summits, and run hands-on workshops for technical audiences.
- Write technical content: reference architecture documents, solution briefs, blog posts and internal enablement.
Qualifications
Education and experience
- Bachelor's degree in Computer Science, Electrical Engineering, Telecommunications or a related field, or equivalent practical experience.
- 8+ years in network engineering or network architecture, with at least 3 years in data center, HPC or cloud infrastructure networking.
- Customer-facing experience as a solutions architect, pre-sales engineer, consultant or technical lead.
Required technical skills
- Data center network design: spine-leaf and Clos topologies, rail-optimised GPU fabrics, oversubscription, ECMP, and the operational challenges of scaling to thousands of nodes.
- InfiniBand: subnet management, partitioning, adaptive routing, and how NCCL / RDMA traffic behaves on the fabric.
- RoCEv2 on Ethernet: lossless and congestion-controlled designs (PFC, ECN, DCQCN), QoS, MTU and buffer tuning.
- Routing and overlays: BGP, EVPN-VXLAN, VRFs and their use for multi-tenancy.
- Hybrid and multi-cloud connectivity: interconnects, VPN, cloud provider networking constructs, and IP address and DNS planning across sites.
- Linux networking: how the kernel and drivers manage NICs, PCI passthrough, SR-IOV, IOMMU, NUMA affinity, and tools such as iproute2, ethtool and devlink.
- Kubernetes networking: CNI plugins (e.g. Cilium, Calico, OVN-Kubernetes), Multus and secondary networks, SR-IOV and RDMA device plugins, network policy and service exposure.
- Virtualisation on Kubernetes: KubeVirt, and how VM networking, passthrough and SR-IOV are configured there.
- Programming or scripting in at least one language (Python or Go preferred), and comfortable using Git, CI and infrastructure-as-code.
How you work
We hire for these behaviours as much as for technical depth, and will ask for concrete examples of each.
- Curious. You dig into why a fabric behaves the way it does, read the specs and the source, and keep up with how AI networking is changing. You question established designs, including the ones customers are used to, when there is a better way.
- Ownership. You take a reference architecture or a POC from the first sketch to a published, validated result, and you stand behind it.
- Proactive. You spot gaps in the product, the documentation or a customer design before you are asked, and you act on them.
- Fast learner. You get productive quickly in unfamiliar hardware, software or customer environments.
- Business acumen. You connect network design choices to cost, time-to-value and risk, and explain them in those terms to decision makers.
Communication and collaboration
- Excellent written and spoken English; you write documents others can build from.
- Comfortable presenting to large audiences at conferences and events, and running hands-on workshops.
- Able to adapt the message to the audience, from network engineers to customer executives.
- Experience working in an international, distributed company, across time zones and different work cultures, with Solutions Architects, Partner Management and Engineering teams.
Nice to have
- Hands-on experience with NVIDIA networking: Quantum InfiniBand, Spectrum-X Ethernet, ConnectX SuperNICs, BlueField DPUs, UFM, and NVIDIA reference designs for AI factories.
- Experience with GPU cloud, neocloud or HPC operators, or with NVIDIA Cloud Partner (NCP) style deployments.
- Bare-metal provisioning and lifecycle: Metal3, Ironic, Redfish, PXE / iPXE, and Cluster API.
- Network automation and SDN controllers, and switch operating systems such as Cumulus Linux, SONiC or vendor equivalents.
- Global load balancing and service exposure for distributed inference (GSLB, Gateway API, ingress).
- Familiarity with Software-Defined Storage networking for AI (NVMe-oF, GPUDirect Storage).
- Experience with Mirantis products, MKE, k0s, k0rdent networking.
- Contributions to open-source networking or Kubernetes projects, or a track record of public talks and technical writing.
- Participation in standards or industry bodies (e.g. Ultra Ethernet Consortium, OCP, IETF), or published research, patents or white papers.
- Additional languages beyond English, for example a European language.
Location, travel and working model
This role is remote, open to candidates based in Europe.
- You will work with a team spread across many time zones; some meetings will fall outside standard local hours.
- Travel of up to 25% for customer engagements, partner meetings, lab work and industry events, primarily within the EU and US.
- Mirantis is an equal opportunity employer. Compensation and benefits are set according to the local market and employment arrangement.
Additional Information Why you’ll love Mirantis
- Build the observability foundation for the AI cloud era, working directly with leading GPU cloud operators, NeoClouds, sovereign clouds, and AI-first enterprises
- Collaborate with a world-class, distributed team committed to openness and technical excellence
- Shape the product narrative and influence go-to-market success
It is understood that Mirantis, Inc. may use automated decision-making technology (ADMT) for specific employment-related decisions. Opting out of ADMT use is requested for decisions about evaluation and review connected with the specific employment decision for the position applied for. You also have the right to appeal any decisions made by ADMT by sending your request to [email protected]
By submitting your resume, you consent to the processing and storage of your personal data in accordance with applicable data protection laws, for the purposes of considering your application for current and future job opportunities.
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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 9,079 AI roles we're tracking, AI/ML Engineer positions make up 76% 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 $211,950 based on 13,048 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $229,300.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
Mirantis AI Hiring
Mirantis has 3 open AI roles right now. They're hiring across AI/ML Engineer. Based in Boston, MA, US.
Remote Work Context
Remote AI roles pay a median of $188,000 across 2,316 positions. About 6% 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 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 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 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). 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 (106) are outnumbered by mid-level (4,384) and senior (3,343) 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 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 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 $213,000. Top-quartile roles start at $263,911, and the 90th percentile reaches $318,400. 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 $256,000 median, while Prompt Engineer roles sit at $141,440. 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 (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 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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