Parasail.ai

Full-stack Engineer

• Design and build the dashboards, model deployment workflows, and real-time monitoring views that our customers use to deploy and manage AI workloads at scale.
• Architect and implement the backend services that power our platform — model serving endpoints, usage metering, billing integrations, authentication, and developer-facing APIs.
• Build the frontend and API layers on top of our GPU orchestration engine, giving users intuitive control over routing, caching, scaling, and cost optimization across our global network.
• Own the end-to-end developer experience including API documentation, SDK design, CLI tools, and onboarding flows that make Parasail the easiest inference platform to adopt.
• Implement streaming interfaces (SSE/WebSockets) for real-time inference responses, live cost tracking, and deployment status updates.
• Build internal and customer-facing analytics — usage dashboards, cost breakdowns, latency distributions, and performance benchmarking tools.

Senior Software Engineer, Fullstack

Account Executive

• Build pipeline from scratch — Prospect, qualify, and drive new opportunities into the funnel.
• Own the sales cycle end-to-end — Discovery, technical validation, negotiation, and close.
• Run high-impact demos — Tailor demos and POCs to customer use cases; credibly speak to infra, APIs, and performance.
• Handle technical objections — Partner with engineering as needed, but be able to stand on your own with CTOs and dev teams.
• Define the playbook — Work with founders to codify what works, refine messaging, and build repeatable processes.
• Guide onboarding — Ensure first customers integrate smoothly and hit early milestones.
• Close the feedback loop — Surface customer insights to inform product and GTM strategy.

Operations

• Own real-time fleet utilization: identify and resolve idle capacity, inefficiencies, and demand/supply mismatches.
• Define utilization targets and operating policies that balance performance, reliability, and cost.
• Develop policies and processes for lifecycle management of vendor-sourced instances (bring-up, steady state, rebalancing, decommissioning).
• Partner with Engineering to define requirements and prioritize automations for capacity acquisition, scaling, rebalancing, failovers, and cost controls.
• Model and monitor GPU unit economics: cost per GPU-hr, marginal cost, blended vendor rates, and cost leakage.
• Partner with Finance & Product to align customer pricing with underlying vendor economics.
• Deliver monthly/quarterly reporting on supply-side cost trends and margin performance, including key drivers and recommended actions.
• Recommend improvements to pricing, contract mix, vendor allocation, and operational policies to expand gross margin.
• Build and maintain forecasting models to predict demand, burst behavior, seasonality, and reserve requirements.
• Determine the optimal mix of contract types (on-demand, committed use, short-term) to maximize flexibility and margin.
• Maintain capacity buffers and contingency plans to protect against vendor outages, degraded performance, or sudden demand spikes.
• Source, evaluate, and manage relationships with neocloud and GPU infrastructure providers.
• Negotiate pricing, SLAs, commitments, contractual flexibility, and scaling terms.
• Create and maintain vendor scorecards (pricing, reliability, latency, responsiveness, and fit).
• Identify emerging vendors, negotiate trial capacity, and assess cost–performance tradeoffs.
• Develop a multi-vendor redundancy strategy to minimize single-provider risk.
• Stand up the core dashboards and operating cadence to monitor and manage (examples): per GPU family utilization, utilization by contract type, idle capacity, blended cost per GPU hour, vendor latency / performance.
• Help define a practical tool stack for capacity planning and financial analysis.

Capacity and Infrastructure Operations Manager

Financial Planning and Analysis

• Own the financial modeling, analytics, and margin optimization of our GPU infrastructure business.
• Build comprehensive financial models for GPU unit economics: cost per GPU-hour, marginal cost, blended vendor rates, cost leakage, and margin by contract type.
• Model the financial impact of capacity decisions including contract mix changes, vendor shifts, utilization improvements, and pricing adjustments.
• Develop scenario analyses for vendor negotiations, new contract structures, and capacity expansion plans.
• Identify and quantify margin expansion levers: vendor renegotiation, contract rebalancing, utilization gains, and pricing optimization.
• Bridge the gap between fleet operations systems and finance team visibility—ensuring accurate and complete reporting on GPU spend and revenue.
• Build and maintain reconciliation processes between vendor invoices, internal usage tracking, and customer billing data.
• Produce monthly and quarterly reporting on supply-side cost trends, margin performance, and financial KPIs.

Senior Software Engineer, Fullstack

Account Executive

Capacity and Infrastructure Operations Manager

Director of Financial Operations