Full-Time
Senior Sales Engineer
Parasail
11-50 employees
Enterprise-grade AI compute infrastructure provider
No salary listed
San Mateo, CA, USA
In Person
On-site in San Mateo, California.
 Sales & Solution Engineering (1) |
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- A few years of technical, customer facing experience. Roughly 4+ years where you've been the technical owner of customer relationships, in solutions or sales engineering, forward deployed engineering, applied AI, or a comparable hands on role.
- Real fluency with inference. You understand serving models at scale and the tradeoffs that come with it, and you can speak credibly about latency, throughput, and cost without hand waving.
- You write code. Python specifically, at a level where you can build, read, and debug something that runs in production, not only prototype it.
- Cloud at scale. Practical experience operating on AWS, Azure, or GCP and serving real workloads on them.
- A communicator people trust. You can lead a focused discovery call, hold your own in front of an executive, and get into the weeds with an engineer, adjusting your altitude to the room.
- Understand the problem. Run deep discovery to fully understand the customer's requirements and use case, what they've optimized for in the past, where they're investing today, and what "good" actually looks like to them, before you propose anything.
- Prove it on Parasail. Scope a focused proof of concept, agree with the customer on how success will be measured, and drive it to a working result alongside our product and engineering teams. You keep the scope defined and the timeline real.
- Tune for production. Run inference sweeps, set performance baselines for their workload, and dial in deployments that hit their specific latency, throughput, and cost numbers. Recommend the right model and serving approach, and help them stand up the evals that keep quality honest once they scale.
- Stay their technical partner. Keep the relationship healthy from the first engineer who championed you to the executive who signs off, sustain momentum through longer cycles, and turn what you see in the field, the recurring friction, the missing capability, the pattern across accounts, into concrete input for our roadmap.
- Hands on time with modern inference tooling such as vLLM, TensorRT LLM, CUDA, or Kubernetes.
- A working sense of when and how to fine tune, and the methods behind it.
- Experience moving fluidly across engineering, product, and go to market without dropping the thread.
Parasail provides scalable, enterprise-grade AI compute infrastructure designed to support diverse machine learning workloads. It offers high-performance computing resources to run AI and ML tasks, including batch processing and multimodal AI, with a focus on speed, reliability, data privacy, and security compliance. By removing traditional barriers like long-term contracts and sales intermediaries, Parasail aims to make AI compute more accessible and affordable for a wide range of customers—from startups to large enterprises. Its goal is to power the AI revolution by enabling high-throughput screening and efficient workloads with minimal engineering overhead.
Company Size
11-50
Company Stage
Series A
Total Funding
$32M
Headquarters
San Mateo, California
Founded
2023
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See people who can refer or advise you
Simplify's Take
What believers are saying
- Parasail closed a $32 million Series A on April 15, 2026.
- Parasail claimed 30% month-over-month revenue growth and 500 billion tokens daily.
- The July 7, 2026 d-Matrix deployment strengthens cost and latency advantages for customers.
What critics are saying
- AWS Bedrock and Fireworks AI already offer similar pay-per-token inference economics.
- Parasail’s 500 billion daily tokens depend on continued open-model demand and GPU supply.
- A commoditization squeeze can crush margins before 2027 if hyperscalers underprice routing layers.
What makes Parasail unique
- Parasail routes prefill to NVIDIA GPUs and decode to d-Matrix Corsair.
- Parasail runs across 40 data centers in 15 countries, not one cloud.
- Parasail’s pay-per-token model removes contracts, vendor lock-in, and infrastructure management.
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Benefits
Company Equity
Growth & Insights and Company News
6 month growth
↑ 33%1 year growth
↑ 33%2 year growth
↑ 45%Parasail is deploying d-Matrix Corsair inference accelerators alongside NVIDIA Hopper and Blackwell GPUs to deliver up to 10 times faster, more cost-efficient inference services to customers. The deployment marks one of the first commercial-scale examples of heterogeneous disaggregated inference in production. The approach combines NVIDIA GPUs for compute-intensive prefill with d-Matrix Corsair accelerators for latency-sensitive decode. Parasail's automatic kernel optimisation technology dynamically routes workloads to appropriate hardware to maximise performance across its heterogeneous fleet. D-Matrix Corsair's performance stems from its Digital In-Memory Compute chiplet architecture, which integrates compute with memory on the same silicon, enabling up to 10 times faster interactive inference and up to three times better energy efficiency versus traditional approaches. The companies plan to share detailed performance results following initial deployments across Parasail's global fleet of over 40 data centres in 15 countries.
Parasail raises $32 million in Series A funding. Parasail has raised $32 million in Series A funding co-led by Touring Capital and Kindred Ventures to scale its serverless GPU orchestration platform for AI inference. The capital will expand global compute capacity, enhance scheduling algorithms, and accelerate adoption among startups building with open source models in a supply constrained market. Parasail, a San Francisco-based AI infrastructure company founded in 2023 by Mike Henry (former CPO at Groq) and Tim Harris, has raised $32 million in a Series A round co-led by Touring Capital and Kindred Ventures. This funding comes roughly a year after the company emerged from stealth in April 2025, following a $10 million seed round in 2024 led by Basis Set Ventures with participation from Threshold Ventures, Buckley Ventures, and Black Opal Ventures. What is Parasail's technology? The company operates a serverless AI deployment network that aggregates and orchestrates GPUs from dozens of global providers across 40 data centers in 15 countries. It delivers on-demand compute for machine learning inference workloads, emphasizing flexibility with no long term contracts, no vendor lock-in, and pay per token pricing. Key offerings include instant serverless inference that auto scales for experiments or production APIs, dedicated serverless pools for guaranteed throughput and low latency, fully reserved dedicated GPUs for custom models and enterprise privacy needs, and batch processing that delivers 80-90% cost savings for large offline jobs. The platform supports Nvidia H100, H200, A100, and RTX 4090 GPUs, dynamically matching workloads in real time while optimizing across rented capacity and liquidity markets to minimize costs and avoid peak demand bottlenecks. Parasail generates 500 billion tokens per day and focuses exclusively on inference (training is not supported). Its proprietary orchestration technology connects fragmented compute resources horizontally, enabling developers to access hardware, data centers, and optimizations with minimal engineering overhead. This approach contrasts with hyperscalers like AWS, Azure, and Google Cloud by prioritizing fluidity in a market where compute is fungible and rapidly evolving, rather than centralized control. Pricing undercuts incumbent clouds significantly while maintaining production grade reliability and simplicity for scaling open source models. The latest round reflects strong market tailwinds in AI inference demand, which investors describe as far outstripping supply amid the rise of agentic systems and open source models. Developers increasingly adopt hybrid architectures, using cheaper open models for initial tasks like high volume screening before routing to frontier models, driving what the company and its backers call "tokenmaxxing": the imperative to deliver tokens faster, cheaper, and at massive scale as agents proliferate in software. Inference is projected to account for at least 20% of future software development costs, creating opportunities for specialized brokers like Parasail that serve seed and Series B stage AI startups without enterprise style commitments. Traction includes customers such as Elicit (which processes over 100,000 scientific papers daily via high throughput screening), Weights & Biases (leveraging massive DeepSeek capacity), Rasa (deploying custom models with improved European latency), and Oumi (generating millions of responses for dataset construction via batch jobs). These deployments highlight Parasail's ability to remove bottlenecks in real time processing, reduce costs for open model strategies, and simplify coordination for large scale experiments. Strategically, the Series A capital will accelerate expansion of the global GPU footprint, enhance orchestration and scheduling algorithms, bolster engineering and go to market teams, and improve reliability across the distributed network. This builds directly on the seed round's focus and positions Parasail to capitalize on the ongoing fragmentation of AI infrastructure, where rapid hardware innovation makes ownership impractical for most builders and demand shows "literally no end." By betting on continued proliferation of open models and agent driven token consumption, the company aims to become a foundational compute layer that democratizes access and levels the playing field against hyperscaler dominance. In the broader AI infrastructure landscape, Parasail's model underscores a shift toward brokerage and optimization in a fluid, multi vendor environment. It competes with specialized inference platforms like Fireworks AI and Baseten while differentiating through extreme flexibility, cost efficiency, and a startup first customer base. The round signals investor conviction that inference will remain a high growth, supply constrained segment even as overall AI hype evolves, with Parasail's horizontal approach enabling it to scale alongside the next wave of AI software development. Please email Daily Company your feedback and news tips at hello(at)dailycompanynews.com
Parasail raises $32M Series A to build the Supercloud that puts developers in control of their AI. Apr 15, 2026, 10:00 ET Touring Capital and Kindred Ventures co-lead $32M round to scale Parasail's AI Supercloud, an inference and training platform built for AI agents SAN FRANCISCO, April 15, 2026 /PRNewswire/ - Parasail, a company building an AI Supercloud, an inference and training platform for deploying and scaling AI agents, today announced it has raised $32 million in Series A funding, bringing total funding to $42 million. The funding round was co-led by Touring Capital and Kindred Ventures, with participation from Samsung NEXT, Flume Ventures, Banyan Ventures and existing investors. The new capital will be used to expand Parasail's AI Supercloud, a fabric of global compute resources, which automatically optimizes model endpoints for speed, performance, and cost. With the additional funding, Parasail will deepen the orchestration and inference optimization, accelerate go-to-market efforts and to strengthen strategic partnerships across the GPU and data center ecosystem. The world is currently rebuilding the entire cloud around AI, with trillions of dollars building data centers and filling them with GPUs. Yet, developers are still constrained by access to this infrastructure and the challenges of standing up and running AI models quickly and efficiently for their products. From vertically-focused enterprise agents to new consumer-focused personal agents to broad-based agent SDK platforms, Parasail powers inference and reinforcement learning environments for the surging wave of AI agents upending the legacy application paradigm. Parasail is delivering the AI Supercloud to satisfy the insatiable need for customized, instant, and dependable inference and continuous training. Customers can now set up massively scalable AI in less than five minutes, powered by the world's supply of GPU compute. AI as a Component for Modern Software The market for developer-controlled AI is rapidly expanding into what analysts estimate will exceed $100 billion as startups and enterprises move toward aggressive performance targets, custom models, and distributed compute ecosystems. A vast software, hardware, and data center ecosystem is forming to support that shift. Companies are discovering that AI has become a component of modern software that they can deploy themselves, leading to an explosion of open source and specialized models. To capture the potential of AI, software companies need greater control over cost, latency, and customization, so they are moving away from black-box APIs and towards AI endpoints they can operate themselves. But the infrastructure behind that shift remains fragmented: GPU supply is constrained and inconsistent, inference optimization is complex, and scaling often requires contracts, sales negotiations, and months of integration work. Parasail removes that friction and delivers a seamless customer experience for AI agent deployment with production-ready AI endpoints in minutes, scaling to massive traffic without contracts or infrastructure management. With five lines of code, developers can launch custom models, hit aggressive targets on latency, throughput, and tokens per second, and scale effortlessly through large surges in traffic when products go viral. "AI builders shouldn't have to become infrastructure experts to ship great products," said Mike Henry, founder and CEO of Parasail. "AI is becoming the core infrastructure for modern software. But the infrastructure layer itself hasn't kept up. We built Parasail so teams can deploy custom AI at massive scale without negotiating contracts, managing fragmented GPU supply, or hiring performance engineering teams." A Structural Layer for AI Inference Parasail is not another single-cloud provider. It operates a programmable deployment network that abstracts away supply fragmentation and inference optimization. Its differentiation is structural and this model allows startups and growth-stage companies to scale from zero to enterprise-grade workloads without rewriting infrastructure as demand increases: * Developer-first deployment: Production AI endpoints live in minutes with minimal code, abstracting significant backend orchestration complexity. * Economics as a first principle: Rather than competing on isolated speed benchmarks, Parasail optimizes workloads for cost efficiency at scale. * Aggregated hardware supply: The AI Supercloud connects Parasail's internal GPU fleet with a diverse set of compute providers, unlocking elastic capacity beyond any single cloud's availability and bringing new hardware platforms with disruptive performance to market quickly. * Automated performance optimization: Parasail replaces manual kernel tuning and performance engineering with automated systems that continuously optimize inference across the network. "AI infrastructure is moving beyond single-cloud models," said Samir Kumar, General Partner at Touring Capital. "As inference workloads scale, companies need flexibility across hardware, geography, and cost structures. Parasail has built the control layer that makes that possible. The team combines deep systems expertise with a clear product vision, and we believe they are well positioned to define how modern AI applications are deployed." "The main product construct of this AI wave is the agent - replacing the notion of the manually-operated application world of the last thirty years," said Steve Jang, Managing Partner at Kindred Ventures. "These agents are directed but can operate autonomously, call multiple models at runtime, and will require massive amounts of tokens. This new world and its developers need powerful customized inference and reinforcement learning capability that are flexible, instant, and dependable. Parasail offers the first agent-focused inference and training solution which simplifies the model and compute complexity of today's dynamic generative AI market." Since launching in April 2025, Parasail processes over 500 billion tokens per day and customers include Elicit, mem0, Gravity, Kotoba, and Venice with 30% MoM revenue growth, leading the pack of the second-wave inference providers. About Parasail: Parasail is delivering the AI Supercloud - a programmable deployment network that gives AI builders production-ready endpoints in minutes, scaling to massive traffic without contracts, infrastructure management, or vendor lock-in. By aggregating global GPU supply and automating inference optimization, Parasail enables companies to deploy custom AI models with aggressive performance targets on latency, throughput, and cost. For more, visit: https://www.parasail.io/ SOURCE Parasail/BAM Communications
Parasail's AI Supercloud: A platform play aiming to monetize the next wave of AI. April 15, 2026 Parasail today revealed a 32 million Series A to build its AI Supercloud, a platform designed to put developers in control of the growing flood of AI agents and workloads. The core concept is audacious in scale and pragmatically valuable for money making: create a portable, secure, governance-friendly runtime for training and inference that works across ecosystems, enabling developers to deploy, manage and monetize intelligent agents without being trapped in a single cloud or vendor. What makes the idea technically compelling is the Supercloud's promise of interoperability and control. AI agents require orchestration, data access, model execution environments and cost governance across heterogeneous infrastructure. Parasail positions itself as the abstraction layer that lets teams train agents once and run them anywhere, with standardized interfaces and robust policy controls. If successful, this reduces vendor lock-in, speeds time to value, and lowers the total cost of ownership for enterprise AI programs. In practice, it could also accelerate the deployment of autonomous workflows in customer service, operations, supply chains and product development by removing friction between models, data sources and compute fabrics. From a money-making perspective, the opportunity is substantial. Enterprise AI budgets are projected to grow as firms scale hundreds or thousands of autonomous tasks, agents and decisioning systems. A platform that monetizes developer activity - through usage-based charges for runtime hours, data access, model hosting, and governance features - can become a recurring revenue backbone for AI programs. Additional revenue streams could include: - Tiered subscriptions for different levels of control, security, and governance capabilities. - Pay-as-you-go inference and training usage, with elastic pricing tied to workload size and latency requirements. - A marketplace for AI agents, tools and connectors that accelerates go-to-market for independent software vendors and system integrators. - Professional services and managed deployments for regulated industries such as finance, healthcare and manufacturing. The market dynamics support a platform thesis. Large enterprises are increasingly investing in multi-cloud strategies and in complex AI ecosystems that require portability and governance. Hyperscalers offer powerful infrastructure but often lock customers into their ecosystems. A True AI OS layer that coordinates across clouds could capture a broad slice of the AI operations budget, especially as firms seek to standardize agent development, testing, deployment and compliance across lines of business. Investment implications are meaningful for founders and investors. A 32 million Series A signals strong venture appetite for infrastructure plays in the AI stack, not just applications or services. If Parasail can demonstrate real multi-cloud runtimes, security, audit trails and reliable agent lifecycle management, subsequent funding rounds will likely emphasize scaling, go-to-market partnerships and ecosystem development. Strategic bets from cloud providers, AI chipmakers, or enterprise software platforms could accelerate growth or lead to beneficial collaborations. The risks are non-trivial, including competition from hyperscalers building similar portability layers, the challenge of achieving true cross-cloud performance parity, and the need to navigate data privacy and regulatory requirements across industries. For entrepreneurs, Parasail represents a blueprint of how to monetize AI infrastructure. The product opportunity sits at the intersection of developer experience, enterprise governance, and cloud-agnostic deployment. Success will hinge on quantifiable savings for customers (lower compute costs, faster deployment cycles, easier compliance) and a robust partner ecosystem that expands the Supercloud's reach beyond a single logo or use case. In the near term, expect a focus on industry-specific deployments, certification programs, and compliance-ready templates that reassure buyers in regulated sectors. Longer term, the company could evolve into a critical platform layer alongside data management and model marketplaces, becoming a recurring revenue hub for AI operations. If Parasail delivers on its promise, the AI Supercloud could transform how organizations build, run and profit from autonomous software, unlocking a new era of tech-driven wealth creation for developers and investors alike.