Andromeda

• Own the reliability of Andromeda's infrastructure end to end • Lead top-customer training run responses and write the postmortem • Ensure the health of thousands of GPUs across providers • Build telemetry, GPU health checks, and automated remediation • Define on-call processes like rotations and escalation • Be the reliability voice in customer incident reviews • Collaborate closely with the product team on SLOs • Partner with providers and data center teams on physical design • Make other engineers better through mentorship

• Lead financial planning and performance management across Andromeda’s GPU cluster portfolio — including NPV/IRR modeling, scenario analysis, and ROI evaluation for procurement decisions • Own the financial architecture of major infrastructure deals — modeling time-value-of-money trade-offs across long-term contract structures and build/lease/route decisions • Serve as Andromeda’s primary point of contact with lenders and debt partners — managing covenant reporting, draw schedules, and ongoing lender communications • Structure and raise financing packages that align debt terms with cluster deployment timelines and revenue ramp • Build and maintain multi-year capex forecasting models across procurement, energy, and operations • Work with the finance team to set capital allocation priorities and establish financial controls to manage spend at scale

• Build and own the full partnership lifecycle — from identifying and sourcing partners to negotiating terms, contracting, onboarding, and ongoing relationship management • Create distribution partnerships with GPU cluster providers who need access to Andromeda's demand network — helping them find their first customers while expanding our supply • Develop partnerships with VC funds and accelerators to channel their portfolio companies’ compute needs to Andromeda • Identify, structure, and close strategic agreements with AI labs, OEMs, data centers, and other key stakeholders in the compute ecosystem • Define compelling value propositions for each partner type and tailor engagement models accordingly • Own KPI frameworks for each active partnership, tracking revenue contribution, utilization, and growth • Drive cross-functional coordination with infrastructure, sales, and technical teams to bring partnership agreements to life • Build the processes and playbooks for a scalable partnerships function

• Design and evolve multi-provider, multi-region GPU compute clusters optimized for large-scale training • Serve as the primary technical point of contact for customers running large-scale training workloads • Define SLOs and error budgets that account for the unique failure modes of GPU infrastructure • Ensure the health and performance of high-speed interconnects • Build deep visibility into GPU utilization, memory pressure, interconnect throughput • Build production-grade automation for cluster provisioning, GPU health checks, job scheduling • Lead incident response for complex failures spanning hardware, networking, orchestration

• Match incoming leads from our sales team with internal capacity and external capacity in the market • Maximize utilization of our compute resources • Source and onboard new compute suppliers across the globe • Source capacity based on customer needs and market trends • Solve customer and supplier problems in a fast-moving, dynamic market • Understand technical and commercial differences between suppliers to optimize our capacity funnel • Develop a proactive compute strategy informed by market intelligence • Negotiate cost with suppliers and other vendors • Create and implement processes around capacity planning

• Manage a sales pipeline and forecast revenue effectively, with strong discipline around deal progression, qualification, and close planning. • Own the full sales cycle for AI-native accounts—from outbound prospecting and first meetings through technical validation, commercial negotiation, and close. • Interact with prospects and customers via Zoom, phone, and email with a high attention to customization, speed, and detail. • Consistently achieve quarterly and annual revenue quotas across new logo and expansion opportunities. • Diligently update and maintain clean pipeline data in CRM, including next steps, stakeholders, technical requirements, and forecast accuracy. • Quickly learn and deeply understand our infrastructure stack (hardware + software), communicate our value proposition clearly, and handle objections in competitive situations. • Develop a deep understanding of customer goals, pain points, and timelines (e.g., fundraising milestones, model training schedules, product launches, capacity constraints) and communicate priorities across the company. • Partner closely with Solutions Engineers to run technical evaluations, demos, and POCs that align to customer success criteria. • Build trusted relationships with technical and executive stakeholders, including founders, infrastructure leaders, ML engineers, and finance/procurement teams. • Develop a deep understanding of target markets and industry dynamics, including GPU supply/demand trends, model lifecycle needs, and competitive infrastructure options. • Travel up to 35% to meet customers and prospects, attend industry events, and participate in on-site meetings.

• If you have firsthand experience working in AI infrastructure but don't see a role currently posted that you are qualified for, feel free to submit your resume here. • If there is a future opportunity that we think aligns with your background, we will reach out.

• Define and drive the product vision • Own Andromeda’s product vision across multiple areas • Spend significant time with AI labs, enterprises, and providers • Own the product roadmap end-to-end • Partner closely with Engineering • Shape how the product is positioned and sold

• Conduct end-to-end profiling of training workloads to identify bottlenecks across GPU kernels, NCCL communication, and storage I/O • Collaborate with systems engineers to improve scheduling efficiency, collective communication performance, and kernel execution • Build and maintain high-fidelity tooling to monitor and visualize MFU, throughput, and cluster uptime • Design technical processes that help the team operate effectively and avoid repeating performance regressions

• Provision, configure, and operate Kubernetes-based clusters for customers across multiple providers • Build automation and tooling to streamline cluster deployments and integrations • Debug customer issues across networking, storage, scheduling, and system layers • Improve reliability and scalability of both training and inference infrastructure • Design and implement monitoring, alerting, and observability for critical systems • Collaborate with engineering and product teams to plan and deliver infrastructure for new services • Participate in on-call and incident response, leading postmortems and reliability improvements