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.

Director of Financial Operations

• Add support for new LLMs, working across the stack from low-level GPU kernels to Kubernetes-based deployments.
• Contribute to cutting-edge open-source LLM engines such as vLLM or SGLang to extend their capabilities and performance (e.g. use Python technologies to improve API servers or request schedulers).
• Operate closer to the hardware, focusing on building and integrating solutions to boost performance and hardware utilization. For example, improve attention backends like FlashAttention or FlashInfer by contributing to their development and optimization, or by integrating their solutions into vLLM.
• Improve LLM performance using advanced algorithmic solutions such as speculative decoding, quantization, or other state-of-the-art techniques. Understand the impact of such techniques in model quality.

Senior Software Engineer, LLM Performance

AI Engineer

• Rapidly prototype AI-powered workflows and tools, incorporating LLMs, embeddings, real-time audio/voice, API integrations, and prompt engineering
• Lead experimental build cycles from hypothesis to working proof-of-concept to actionable insights
• Collaborate closely with product and engineering to translate prototypes into viable product features
• Ensure alignment with platform architecture; seek guidance before integrating prototypes into production systems
• Navigate open-ended engineering problems with creative, experimental solutions
• Share learnings and code artifacts to accelerate team-wide innovation

Senior AI Engineer

• Ensure seamless deployment, scalability, and security of the AI platform.
• Create robust infrastructures supporting complex AI workloads.
• Collaborate closely with AI-focused performance and solutions engineering teams.