Join Firebird in building the future

We are seeking a highly skilled Network Engineer to design, implement, and operate high-performance network fabrics supporting large-scale GPU clusters and AI training workloads. This role focuses on ultra-low latency, high-throughput infrastructure built on InfiniBand and high-speed Ethernet (200/400GbE) technologies.

Key Responsibilities

Network Architecture & Implementation

• •  Design and deploy leaf–spine topologies for GPU clusters (InfiniBand or high-speed Ethernet)
• •  Configure and manage switches such as Mellanox SN5600/SN4600, QM9700/QM9790, or equivalent
• •  Build and optimize RoCEv2 / InfiniBand fabrics (PFC, ECN, DCBX, congestion control)
• •  Implement network segmentation and multi-tenancy (VLANs, VRFs, VPCs, SDN overlays)
• •  Integrate BlueField-3 DPUs, SR-IOV vNICs, and networking offload solutions

Operations & Reliability

• •  Monitor fabric health (performance, congestion, drops, routing, credit starvation)
• •  Troubleshoot high-performance AI training pipelines (collectives, all-reduce, RDMA)
• •  Maintain routing and security configurations: BGP (EVPN), OSPF, MLAG/VPC
• •  Manage spine upgrades and firmware lifecycles
• •  Develop automation for network configuration (Ansible, Terraform, GitOps)

Storage Networking

• •  Deploy and optimize NVMe-over-Fabrics
• •  Integrate high-performance storage platforms (WekaFS, VAST, DDN, Pure, Ceph NVMe tier)
• •  Tune networking for ultra-low latency and high-throughput workloads (AI training & inference)

Data Center Integration

• •  Collaborate with electrical, mechanical, and compute teams on rack layouts, airflow, and cabling
• •  Validate rack bring-up: switch burn-ins, link activation, cross-connect testing Security & Compliance
• •  Implement secure multi-tenant isolation for GPU cloud environments
• •  Harden APIs, management networks, and out-of-band access

Automation & Observability

• •  Build dashboards and alerting using Grafana, Prometheus, InfiniBand Telemetry, WJH
• •  Contribute to internal tooling for fabric validation, auto-provisioning, and drift detection

Required Skills

Core Networking

• •  Deep expertise in InfiniBand (HDR/NDR) or high-speed Ethernet (200G/400G)
• •  Strong RDMA knowledge (RoCEv2, QP states, congestion management)
• •  Advanced routing and switching (BGP, EVPN, OSPF, MLAG/VPC)
• •  Experience with Mellanox/NVIDIA platforms (ONIE, Cumulus, MLNX-OS, UFM, NEO)
• •  Strong Linux networking fundamentals

AI Infrastructure

• •  Experience supporting AI training clusters (H100, B200, A100 or similar GPUs)
• •  Familiarity with BlueField DPUs, SR-IOV, VirtIO
• •  Exposure to OVN/Kube-OVN/Calico networking

Storage Networking

• •  Understanding of NVMe-oF (TCP or RDMA)
• •  Experience with WekaFS, DDN, VAST, or Pure (strong advantage)

Nice to Have skills

• •  Kubernetes networking (CNI, Cilium, Calico, Multus)
• •  OpenStack Neutron / OVN experience
• •  Understanding of GPU virtualization and MIG networking patterns

We are seeking a Storage Engineer to design, implement, and maintain enterprise-grade storage infrastructure across on-premises and cloud environments. This role focuses on delivering high-performance, scalable, and secure storage solutions that support mission-critical applications and large-scale data services.

Key Responsibilities

• •  Architect and implement scalable storage infrastructure for diverse workloads
• •  Monitor storage performance, reliability, and capacity utilization
• •  Design and execute data protection and disaster recovery strategies
• •  Collaborate with infrastructure teams to ensure seamless storage integration
• •  Participate in storage-related incident response, troubleshooting, and resolution

Required Skills & Experience

Cloud-Native Storage

• •  Hands-on experience with Kubernetes storage solutions (Ceph, Longhorn, Linstor)
• •  Implementation and management of CSI drivers
• •  Persistent volume management and storage class design

Enterprise Storage Hardware

• •  Experience with enterprise storage platforms such as Dell PowerScale (Isilon) or equivalent
• •  Storage array configuration, tuning, and lifecycle management
• •  Hardware troubleshooting and capacity planning

File & Block Storage

• •  Design and maintenance of large-scale NFS/SMB environments
• •  High-performance storage architecture and optimization
• •  Implementation of block storage protocols (iSCSI, etc.)
• •  Storage networking and multipathing configuration

Object Storage

• •  Deployment and management of S3-compatible solutions (vendor-based or self-hosted)
• •  Experience with MinIO and Ceph Object Gateway
• •  Object storage lifecycle policies and tiering strategies

Security & Access Control

• •  RBAC-based storage security implementation
• •  Data encryption at rest and in transit
• •  Access control policies and audit compliance

Hardware & RAID

• •  Configuration and management of server-based RAID systems
• •  Storage hardware performance tuning
• •  Disk failure handling and replacement procedures

Data Protection

• •  Large-scale backup solution design and implementation
• •  Disaster recovery planning for petabyte-scale environments
• •  Data retention and compliance policy implementation

Preferred Qualifications

• •  Storage vendor certifications (Dell, NetApp, HPE, etc.)
• •  Experience with software-defined storage platforms
• •  Familiarity with storage performance benchmarking and analysis tools

We are looking for a Monitoring and Observability Specialist to design, build, and maintain end-to-end observability infrastructure across our platform. In this role, you will own the full lifecycle of monitoring, logging, and tracing systems — from architecture and implementation to custom agent development and ongoing optimization. You will work closely with engineering, DevOps, and platform teams to ensure deep visibility into every layer of our infrastructure and applications.

What we offer

• •  An open, feedback-driven culture that supports continuous professional growth, whether as a technical leader or people manager.
• •  A distributed, hybrid work environment with teams across multiple time zones.
• •  Competitive compensation and well-being benefits.
• •  The opportunity to shape observability strategy from the ground up and pilot modern tooling and practices.
• •  A collaborative environment where your expertise directly impacts platform reliability and performance.

What you will do

• •  Design and architect end-to-end observability solutions covering metrics, logs, traces, and synthetic monitoring across on-premise and cloud environments.
• •  Build, maintain, and extend custom monitoring agents and exporters tailored to internal services and infrastructure components.
• •  Develop and maintain high-fidelity dashboards, alerts, and runbooks that provide actionable insights to both operations and engineering teams.
• •  Own the full lifecycle of the monitoring stack — deployment, scaling, performance tuning, and capacity planning.
• •  Implement and maintain distributed tracing pipelines to enable deep visibility into service-to-service interactions.
• •  Define and enforce observability standards and best practices across teams.
• •  Participate in incident response, leveraging observability data to accelerate root-cause analysis and resolution.
• •  Evaluate and integrate new monitoring and observability tooling as the ecosystem evolves.
• •  Collaborate closely with platform, networking, and application teams to ensure comprehensive coverage and minimal blind spots.

About you

• •  You are a hands-on engineer with deep expertise in building and operating observability platforms at scale.
• •  You have a strong understanding of Linux internals and networking, and you are comfortable writing production-grade code.
• •  You thrive in complex, multi-layered infrastructure environments and can translate raw telemetry data into meaningful operational intelligence.

Must-have

• •  5+ years of experience in monitoring, observability, or SRE roles with hands-on ownership of observability infrastructure.
• •  Deep expertise in at least two of: Prometheus / VictoriaMetrics, Elastic Stack (Elasticsearch, Logstash, Kibana, Filebeat), Grafana, Loki, Jaeger / Tempo / OpenTelemetry.
• •  Strong experience writing and maintaining custom monitoring agents or exporters (Go, Python, or similar).
• •  Solid Linux administration and internal knowledge (networking stack, kernel tuning, process management).
• •  Strong networking fundamentals — TCP/IP, DNS, VLANs, routing, traffic analysis, firewall rules.
• •  Experience deploying and operating monitoring systems in Kubernetes environments.
• •  Proficiency in IaC and GitOps tooling (Terraform, Ansible, Flux, ArgoCD, or similar).
• •  Scripting and automation skills (Python, Go, Bash). Experience with log aggregation pipelines and understanding of log management at scale.
• •  Strong incident response experience and ability to build alerting strategies that reduce noise and improve MTTR.

Nice to have

• •  Experience with distributed tracing systems (Jaeger, Tempo, OpenTelemetry) at scale.
• •  Familiarity with time-series database internals and query optimization (PromQL, MetricsQL, KQL).
• •  Experience with service mesh observability (Istio, Linkerd telemetry).
• •  Knowledge of APM and synthetic monitoring approaches.
• •  Familiarity with streaming platforms (Kafka) as part of log/metrics pipelines.s
• •  Experience with cloud-native monitoring in multi-cloud or hybrid environments (AWS, GCP, Azure).
• •  Background in capacity planning and performance engineering.

We are looking for a Procurement Specialist to manage sourcing, negotiation, and delivery processes across multiple vendors. This role is key to ensuring cost-effective, timely, and reliable procurement in a fast-paced environment, requiring strong project management and organizational capabilities.

In addition, the role involves coordinating the full logistics cycle, ensuring timely payments, and keeping all involved parties aligned to guarantee smooth and efficient procurement operations.

Key Responsibilities

• •  Manage end-to-end procurement processes
• •  Develop and execute vendor sourcing strategies
• •  Build and maintain strong relationships with suppliers
• •  Lead price negotiations and optimize commercial terms
• •  Structure agreements including pricing, payment terms, and delivery conditions
• •  Monitor supply chain timelines and manage lead times
• •  Coordinate procurement activities as projects, ensuring timelines and deliverables are met
• •  Collaborate with internal teams to align procurement with business needs
• •  Ensure compliance with contracts, policies, and regulatory requirements
• •  Oversee logistics and delivery coordination

Requirements

• •  Proven experience in procurement, sourcing, vendor or project management
• •  Strong negotiation and communication skills
• •  Excellent English knowledge
• •  Solid project management skills with the ability to manage multiple priorities
• •  Excellent organizational skills and attention to detail
• •  Ability to manage multiple vendors and complex procurement processes
• •  Strong problem-solving skills
• •  Ability to work cross-functionally with both technical and business teams

Nice to have

• •  Experience working in fast-paced or scaling environments
• •  Familiarity with contract management
• •  Experience working with international vendors

About the Role

We are looking for an experienced Project Manager to lead the end-to-end execution of large-scale AI infrastructure projects. In this role, you will coordinate cross-functional teams across engineering, operations, construction, and external vendors to ensure the successful delivery of complex, mission-critical infrastructure in a fast-paced, high-growth environment.

What You'll Do

• • Support the planning and execution of AI infrastructure projects from initiation through deployment
• • Coordinate cross-functional stakeholders, including construction, electrical, mechanical, networking, and operations teams
• • Manage project timelines, milestones, budgets, risks, and dependencies
• • Oversee vendor coordination with external contractors and infrastructure partners
• • Ensure alignment between technical requirements, operational readiness, and business objectives
• • Track project progress and provide regular status updates to leadership
• • Support deployment planning for GPU clusters, networking, cooling, and power infrastructure
• • Ensure compliance with safety, operational, and quality standards
• • Contribute to process improvement and project management best practices

What We're Looking For

• • Strong project management experience in infrastructure, construction, telecom, cloud, or other mission-critical environments
• • Strong understanding of infrastructure components including power, cooling, networking, and physical systems
• • Experience coordinating large-scale technical or infrastructure projects
• • Proven ability to manage multiple stakeholders and vendors simultaneously
• • Strong organizational, communication, and problem-solving skills
• • High sense of ownership and accountability, with the ability to drive projects forward proactively
• • Ability to operate effectively in fast-changing and high-growth environments

Nice to Have

• • Experience working with hyperscale or enterprise infrastructure environments
• • Familiarity with NVIDIA GPU infrastructure ecosystems
• • Exposure to construction or facility buildout projects
• • Experience in startup or rapidly scaling technology companies

About the Role

We are looking for an experienced ML Engineer to help build, deploy, and optimize machine learning systems in production. In this role, you will focus on model serving, inference performance, and scalable deployment of large language models, working closely with platform and infrastructure teams to deliver reliable and efficient AI solutions.

What You'll Do

• • Design, deploy, and optimize large language model (LLM) inference systems for production environments
• • Evaluate, benchmark, and improve serving performance across latency, throughput, memory utilization, and cost-efficiency metrics
• • Implement and optimize model-serving solutions using frameworks such as vLLM, TensorRT-LLM, SGLang, Triton Inference Server, or similar technologies
• • Develop and maintain model conversion, deployment, and evaluation workflows across the inference stack
• • Apply and validate quantization techniques to improve performance while preserving model quality
• • Profile GPU workloads, identify bottlenecks, and drive performance improvements using industry-standard observability and profiling tools
• • Collaborate with Platform, Infrastructure, and Product Engineering teams to deploy and operate scalable AI services
• • Contribute to architecture decisions related to distributed inference, model serving, caching strategies, and resource utilization
• • Improve reliability, monitoring, and operational excellence of production inference systems
• • Stay current with emerging advancements in LLM serving, inference optimization, and AI infrastructure technologies, and help evaluate their adoption

What We're Looking For

• • 3+ years of experience in ML engineering with a strong focus on model deployment, serving, and inference optimization
• • Hands-on production experience with modern LLM serving frameworks such as vLLM, TensorRT-LLM, SGLang, or similar technologies
• • Deep understanding of transformer architectures and LLM inference internals, including attention mechanisms, KV caching, autoregressive generation, prefill vs. decode phases, and performance bottlenecks
• • Experience implementing and validating model quantization techniques (FP8, INT8, AWQ, GPTQ, or similar), with a strong understanding of accuracy, latency, and hardware trade-offs
• • Strong performance engineering and profiling skills, including experience with tools such as Nsight Systems, DCGM, nvidia-smi, or equivalent
• • Advanced Python programming skills for benchmarking, model conversion pipelines, evaluation frameworks, and automation
• • Working knowledge of PyTorch and model export workflows, including ONNX, TorchScript, and TensorRT
• • Proven ability to collaborate effectively with platform, infrastructure, and product teams in production environments

Nice to Have

• • Understanding of GPU architecture and performance characteristics, including HBM bandwidth, tensor cores, NVLink, and compute throughput
• • Experience working with modern NVIDIA accelerators such as B200, H100, L40S, or similar
• • Hands-on experience with Triton Inference Server
• • Familiarity with Kubernetes and deploying ML workloads in containerized environments
• • Knowledge of distributed inference systems, including tensor parallelism, NCCL tuning, RDMA/InfiniBand, and GPUDirect
• • Experience implementing speculative decoding approaches such as Medusa, EAGLE, or draft-model architectures
• • Operational experience with prefix caching strategies and multi-tenant serving environments
• • Experience serving and managing LoRA adapters, including multi-adapter hot-swapping configurations