Technical Support Engineer (L2) - Compute

We need to output HTML fragment with sections: About the role, Responsibilities, Requirements, maybe Benefits, etc. Must not repeat job title. Must not include boilerplate like Privacy Policy, etc. Must include only sections with real content. We have content: About Mistral (company description) maybe as About the role? Actually "About Mistral" is company overview. Could be placed under "About the role" or maybe separate "About the company". But headings allowed: About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule. We need to map content. We have sections: "About Mistral" (company description) could go under About the role? Probably "About the role" includes description of role and team. The posting includes: "Mistral AI is building a new Compute Support team...". That's role description. So we can put that under About the role. Responsibilities: list under "Key Responsibilities" with bullet points. Requirements: "What We’re Looking For" includes required skills & experience. Could be Requirements. Qualifications maybe not separate; we could combine Requirements and Qualifications but we need to only include sections with real content. Could have Requirements and Qualifications both? Might be redundant. Safer to have Requirements only. Skills: maybe list specific skills like Kubernetes, Linux, etc. Could be under Skills. Benefits: "What we offer" includes benefits description. So Benefits section. Pay and Schedule: not provided, so omit. We must not include headings with filler. So we output:

About the role

... (company description + role description)

Responsibilities

• ...
...

Requirements

• ...
...

Skills

• ...
...

Benefits

... benefits description

We must preserve all substantive information, not invent. Must not include boilerplate like Privacy Policy, etc. We need to extract details. Let's parse the posting. About Mistral paragraph: "About Mistral Mistral provides full-stack AI solutions: from frontier models to developer tools, applications, and compute. We partner with enterprises tackling the hardest problems across high-stakes industries like finance, manufacturing, defense, healthcare, and the public sector, co-creating customized AI systems that they can run on their terms. We are a dynamic, collaborative team passionate about AI and its potential to transform society. Our diverse workforce thrives in competitive environments and is committed to driving innovation. Our teams are distributed between Europe, North America, Asia and the Middle East. We are creative, low-ego and team-spirited. Mistral AI is building a new Compute Support team to ensure the reliability, performance, and scalability of our GPU clusters in a Kubernetes environment. As one of the founding members of this team, you will play a pivotal role in shaping its processes, standards, and culture. In this hybrid L1/L2 support role, you will be the first point of contact for customers and internal teams, providing technical guidance, troubleshooting, and issue resolution for compute-related inquiries. You’ll also serve as the escalation point for complex issues, leveraging your deep systems knowledge, Kubernetes expertise, and operational debugging skills to ensure our AI workloads run smoothly at scale. This is a support-focused role that blends system administration, customer-facing communication, and compute infrastructure expertise. While coding is not the primary focus, familiarity with Go for debugging and automation is a plus." We can combine that into About the role paragraph. Responsibilities: Under "Key Responsibilities" there are subheadings: First Customer’s Point of Contact, Technical Support, Documentation & Process Improvement. Each with bullet-like sentences. We need to extract each duty as

. Let's list: First Customer’s Point of Contact: - Act as the first interlocutor for customers and internal teams, providing timely and effective responses to compute-related inquiries. - Triage and prioritize incoming requests, ensuring SLAs are met for acknowledgment, first response, and resolution. - Gather and analyze initial issue details (e.g., logs, error messages, system metrics) to diagnose problems efficiently. - Provide clear, actionable guidance to customers and internal users, including temporary workarounds where applicable. Technical Support: - Serve as the L2 escalation point for Linux, Kubernetes, and compute infrastructure issues, providing deep technical troubleshooting for: - GPU/TPU workloads (e.g., CUDA errors, memory leaks, job failures). - Kubernetes clusters (e.g., pod crashes, node failures, networking misconfigurations). - Bare metal and cloud environments (e.g., AWS EC2, GCP VMs, HPC clusters). - Diagnose and resolve performance bottlenecks, hardware failures, and resource contention in distributed systems. - Analyze system metrics, logs, and traces (e.g., dmesg, journalctl, nvidia-smi, Prometheus, Grafana) to identify root causes of issues. - Participate in on-call rotations to provide 24/7 support for critical compute systems - Nice to have: Optimize system configurations (e.g., kernel parameters, filesystem tuning, network settings) for high-performance computing (HPC) and AI workloads. - Kubernetes & Containerization: - Debug Kubernetes clusters with a focus on: - Pod and node issues (e.g., CrashLoopBackOff, OOMKilled, ImagePullBackOff). - Networking and storage (e.g., CNI plugins, PersistentVolumes, StorageClasses). - Resource management (e.g., Requests/Limits, QOS classes, node affinity). - Troubleshoot container runtime issues (Docker, containerd) such as image pull failures, OCI compliance, or runtime errors. - Collaborate with SRE teams to understand and improve existing IaC (Terraform, Ansible) and Go-based tooling. Documentation & Process Improvement: - Create and maintain runbooks, playbooks, and internal documentation for common compute issues (e.g., GPU debugging, Kubernetes troubleshooting). - Contribute to post-mortems with actionable follow-ups to prevent recurring incidents. - Train internal teams on best practices for compute infrastructure and debugging. - Help define and refine support processes as a founding member of the new Compute Support team. We need to flatten into list items; we can keep sub-bullets as separate list items but maybe keep them as separate

with details. We'll produce a

with each duty as
• . Might be long but okay. Requirements: Under "What We’re Looking For" Required Skills & Experience. List: - 5+ years of experience in system administration, technical support, or infrastructure operations, with a strong focus on compute-heavy environments (bare metal, cloud, HPC, or virtualization). - Deep Linux/Unix expertise: - Debugging kernel-level issues (e.g., OOM killer, I/O bottlenecks, CPU throttling). - Performance tuning (e.g., sysctl, ulimit, filesystem optimizations). - Networking troubleshooting (e.g., iptables, tcpdump, DNS, NFS, SSH). - Storage management (e.g., LVM, RAID, NVMe, GPU-local storage). - Hands-on Kubernetes experience is mandatory: - Debugging pods, nodes, and clusters (e.g., kubectl describe, kubectl logs, crictl). - Networking and storage (e.g., CNI plugins, PersistentVolumes). - Resource management (e.g., Requests/Limits, QOS classes). - Familiarity with containerization (Docker, containerd) and basic IaC awareness - Proficiency with monitoring tools (Prometheus, Grafana, ELK, OpenTelemetry). - Basic scripting/automation skills (preferably Go, but Bash or Python are acceptable for ad-hoc tasks). - Experience with bare metal, virtualization, or HPC environments (e.g., Fluidstack, Coreweave, Vast) is a strong plus. - Excellent problem-solving and communication skills, with the ability to explain technical issues clearly and patiently to both technical and non-technical stakeholders. - Customer-focused mindset with a passion for resolving issues efficiently and improving system reliability. - Ability to work in on-call rotations and handle high-pressure situations with a structured, calm approach. - Commitment to meeting tight SLAs for issue acknowledgment, triage, and first response. We can flatten each bullet as
• . For sub-bullets we can incorporate into same
• using semicolons or separate
• . Probably okay to keep as separate
• for each sub-point. Skills: Could list specific skills: Linux/Unix, Kubernetes, GPU/TPU, monitoring tools, scripting (Go/Bash/Python), IaC (Terraform, Ansible), containerization (Docker, containerd), networking, storage, performance tuning, etc. We'll extract from Requirements and Responsibilities. But we might not need separate Skills section if we already have Requirements covering skills. However the instruction says we can include Skills section if there is real specific content. We have skills list; we could create Skills section summarizing key skills: Linux/Unix, Kubernetes, GPU/TPU, monitoring, scripting, IaC, containerization, networking, storage, performance tuning, on-call, communication. We'll include Skills. Benefits: From "What we offer": "We offer a comprehensive benefits package designed to support your well-being, growth, and work-life balance. Benefits vary by country and may include healthcare coverage, parental leave, retirement plans, relocation support, wellness programs, meal and transportation allowances, and other location-specific perks. For the most up-to-date details on benefits available in your location, please refer to our Benefits page." We should keep that as Benefits section paragraph. We must not include Privacy Policy etc. Now produce HTML. We need to ensure no extra whitespace? It's fine. Let's craft. About the role paragraph: combine company description and role description. We'll put:

  Mistral provides full-stack AI solutions: from frontier models to developer tools, applications, and compute. We partner with enterprises tackling the hardest problems across high-stakes industries like finance, manufacturing, defense, healthcare, and the public sector, co-creating customized AI systems that they can run on their terms. We are a dynamic, collaborative team passionate about AI and its potential to transform society. Our diverse workforce thrives in competitive environments and is committed to driving innovation. Our teams are distributed between Europe, North America, Asia and the Middle East. We are creative, low-ego and team-spirited. Mistral AI is building a new Compute Support team to ensure the reliability, performance, and scalability of our GPU clusters in a Kubernetes environment. As one of the founding members of this team, you will play a pivotal role in shaping its processes, standards, and culture. In this hybrid L1/L2 support role, you will be the first point of contact for customers and internal teams, providing technical guidance, troubleshooting, and issue resolution for compute-related inquiries. You’ll also serve as the escalation point for complex issues, leveraging your deep systems knowledge, Kubernetes expertise, and operational debugging skills to ensure our AI workloads run smoothly at scale. This is a support-focused role that blends system administration, customer-facing communication, and compute infrastructure expertise. While coding is not the primary focus, familiarity with Go for debugging and automation is a plus.

  Now Responsibilities
  with each duty. We'll list each bullet as
  • . We'll keep the sub-bullets as separate
  • but maybe indent? Not needed. Let's compile list: - Act as the first interlocutor for customers and internal teams, providing timely and effective responses to compute-related inquiries. - Triage and prioritize incoming requests, ensuring SLAs are met for acknowledgment, first response, and resolution. - Gather and analyze initial issue details (e.g., logs, error messages, system metrics) to diagnose problems efficiently. - Provide clear, actionable guidance to customers and internal users, including temporary workarounds where applicable. - Serve as the L2 escalation point for Linux, Kubernetes, and compute infrastructure issues, providing deep technical