Ai Native Startup Manager Jobs

About Us: AI needs a new infrastructure layer. We're building it at Modal. Every era of computing brought new workloads that previous infrastructure couldn't support: mainframes, databases, and the cloud. Each time, the company that rebuilt the layer underneath defined the decade. AI is no different, except it touches everything instead of one slice, and the window to build the layer underneath it is open right now. Our customers include category-defining companies like Lovable , Ramp , Cognition, DoorDash, and Suno. They rely on Modal for instant GPU access, sub-second container starts, and native storage, so it's simple to serve low-latency inference, fine-tune models, and access production-ready sandboxes at scale. We recently raised a $355M Series C at a $4.65B valuation, led by General Catalyst and Redpoint Ventures. We've crossed $300M+ ARR and grown fivefold since September. Our team includes creators of popular open-source projects (e.g., Seaborn , Luig i ), academic researchers, international olympiad medalists, and experienced engineering and product leaders with decades of experience. The Role: We’re hiring an Enterprise Account Executive to accelerate Modal’s growth with the world’s most innovative AI companies. This is a high-impact role where you’ll own the full sales cycle—from building pipeline to closing large, strategic enterprise deals. You’ll partner directly with our founders, engineering, and product teams to help customers harness Modal’s infrastructure to train, deploy, and scale AI applications. You’ll be expected to operate as a builder: developing new relationships, shaping our GTM motion, and serving as the voice of the customer inside Modal. The ideal candidate is both technically curious and commercially driven—equally comfortable in a room with C-level executives and with machine learning engineers. In this role, you will: Drive new business by generating pipeline, negotiating, and closing complex enterprise deals Build deep, trusted r

About Us: AI needs a new infrastructure layer. We're building it at Modal. Every era of computing brought new workloads that previous infrastructure couldn't support: mainframes, databases, and the cloud. Each time, the company that rebuilt the layer underneath defined the decade. AI is no different, except it touches everything instead of one slice, and the window to build the layer underneath it is open right now. Our customers include category-defining companies like Lovable , Ramp , Cognition, DoorDash, and Suno. They rely on Modal for instant GPU access, sub-second container starts, and native storage, so it's simple to serve low-latency inference, fine-tune models, and access production-ready sandboxes at scale. We recently raised a $355M Series C at a $4.65B valuation, led by General Catalyst and Redpoint Ventures. We've crossed $300M+ ARR and grown fivefold since September. Our team includes creators of popular open-source projects (e.g., Seaborn , Luig i ), academic researchers, international olympiad medalists, and experienced engineering and product leaders with decades of experience. The Role: We’re looking for strong engineers with experience building developer tools that users love to work with. Our ideal candidate is someone with a demonstrated drive to build beautiful interfaces that enhance developer productivity. Requirements: 5+ years of experience developing high-quality Python libraries with broad user-bases, ideally including some experience maintaining open-source software. Knowledge of advanced Python features, especially async programming. A strong product sense that manifests as a focus on developer ergonomics and productivity. A high level of customer empathy, good communication skills, and an openness to working directly with our users to help solve their problems. Ability to participate in on-call rotation and respond to production incidents. Ability to work in-person in our NYC or Stockholm office. Any of the following would be a plus:

About Us: AI needs a new infrastructure layer. We're building it at Modal. Every era of computing brought new workloads that previous infrastructure couldn't support: mainframes, databases, and the cloud. Each time, the company that rebuilt the layer underneath defined the decade. AI is no different, except it touches everything instead of one slice, and the window to build the layer underneath it is open right now. Our customers include category-defining companies like Lovable , Ramp , Cognition, DoorDash, and Suno. They rely on Modal for instant GPU access, sub-second container starts, and native storage, so it's simple to serve low-latency inference, fine-tune models, and access production-ready sandboxes at scale. We recently raised a $355M Series C at a $4.65B valuation, led by General Catalyst and Redpoint Ventures. We've crossed $300M+ ARR and grown fivefold since September. Our team includes creators of popular open-source projects (e.g., Seaborn , Luig i ), academic researchers, international olympiad medalists, and experienced engineering and product leaders with decades of experience. The Role: Modal is seeking an experienced Forward Deployed Engineer (FDE) to partner with our sales team and drive technical sales success. As an FDE, you will be the technical voice in our sales process, working directly with Account Executives to help enterprise customers understand how Modal can transform their AI/ML infrastructure. You will: Partner with Account Executives to identify, qualify, and close strategic enterprise opportunities Lead technical discovery sessions with prospective customers to understand their current infrastructure, pain points, and requirements Design and present compelling technical solutions that demonstrate how Modal addresses customer needs Architect migration paths from existing cloud infrastructure (AWS, GCP, Azure) to Modal's serverless platform Conduct technical demos, experiments, and proof-of-concepts that showcase Modal's capabilitie

About Us: AI needs a new infrastructure layer. We're building it at Modal. Every era of computing brought new workloads that previous infrastructure couldn't support: mainframes, databases, and the cloud. Each time, the company that rebuilt the layer underneath defined the decade. AI is no different, except it touches everything instead of one slice, and the window to build the layer underneath it is open right now. Our customers include category-defining companies like Lovable , Ramp , Cognition, DoorDash, and Suno. They rely on Modal for instant GPU access, sub-second container starts, and native storage, so it's simple to serve low-latency inference, fine-tune models, and access production-ready sandboxes at scale. We recently raised a $355M Series C at a $4.65B valuation, led by General Catalyst and Redpoint Ventures. We've crossed $300M+ ARR and grown fivefold since September. Our team includes creators of popular open-source projects (e.g., Seaborn , Luig i ), academic researchers, international olympiad medalists, and experienced engineering and product leaders with decades of experience. The Role: We are looking for strong engineers with experience and interest in designing, building, and maintaining the novel, high-performance systems that make up our serverless platform. Requirements: 5+ years of experience writing high-quality production code Experience building high-performance distributed systems at a large scale (the more battle scars, the better) Strong cloud skills Strong knowledge of low-level operating system foundations (Linux kernel, file systems, containers, etc.) Experience with performance engineering (tell us a story of when you shaved off a few milliseconds!) Ability to work in-person in our Stockholm office. Prior experience with Rust is nice to have, but not required. Ability to participate in on-call rotation and respond to production incidents.

About Us: AI needs a new infrastructure layer. We're building it at Modal. Every era of computing brought new workloads that previous infrastructure couldn't support: mainframes, databases, and the cloud. Each time, the company that rebuilt the layer underneath defined the decade. AI is no different, except it touches everything instead of one slice, and the window to build the layer underneath it is open right now. Our customers include category-defining companies like Lovable , Ramp , Cognition, DoorDash, and Suno. They rely on Modal for instant GPU access, sub-second container starts, and native storage, so it's simple to serve low-latency inference, fine-tune models, and access production-ready sandboxes at scale. We recently raised a $355M Series C at a $4.65B valuation, led by General Catalyst and Redpoint Ventures. We've crossed $300M+ ARR and grown fivefold since September. Our team includes creators of popular open-source projects (e.g., Seaborn , Luig i ), academic researchers, international olympiad medalists, and experienced engineering and product leaders with decades of experience. The Role: Modal is hiring a high-impact Solutions Architect to drive technical strategy across our most strategic enterprise accounts. You will operate as the executive technical counterpart to Enterprise Account Executives, leading complex evaluations, shaping infrastructure modernization roadmaps, and driving multi-product adoption across AI/ML workloads. This role is not demo support. It is a strategic, consultative position requiring strong architectural depth, executive presence, and the ability to influence 7–8 figure infrastructure decisions. You will work directly with CTOs, VPs of Engineering, and ML platform leaders to help them rethink how AI infrastructure should be built and operated. If you thrive in high-velocity technical sales environments and want to shape the infrastructure layer powering modern AI companies, this role is for you. What You’ll Do: Own the t

About Us: AI needs a new infrastructure layer. We're building it at Modal. Every era of computing brought new workloads that previous infrastructure couldn't support: mainframes, databases, and the cloud. Each time, the company that rebuilt the layer underneath defined the decade. AI is no different, except it touches everything instead of one slice, and the window to build the layer underneath it is open right now. Our customers include category-defining companies like Lovable , Ramp , Cognition, DoorDash, and Suno. They rely on Modal for instant GPU access, sub-second container starts, and native storage, so it's simple to serve low-latency inference, fine-tune models, and access production-ready sandboxes at scale. We recently raised a $355M Series C at a $4.65B valuation, led by General Catalyst and Redpoint Ventures. We've crossed $300M+ ARR and grown fivefold since September. Our team includes creators of popular open-source projects (e.g., Seaborn , Luig i ), academic researchers, international olympiad medalists, and experienced engineering and product leaders with decades of experience. The Role: We're hiring a VP of Finance to build the finance function from the ground up as our first full-time finance hire. This is a high-impact role for someone who thrives at the intersection of strategic thinking and hands-on execution. We are looking for someone who can architect the systems and processes that will scale with Modal, partner closely with the founders and executive team, and grow into the company's CFO. You'll report directly to the CEO and collaborate closely with our BizOps, GTM, and Product teams. In this role, you will: Build and maintain Modal's operating model, tying financial performance to company KPIs and resource allocation Lead all budgeting, forecasting, and long-range planning processes, and develop the reporting infrastructure that gives leadership and the board clear, timely visibility into the health of the business Partner with the found

About Us: AI needs a new infrastructure layer. We're building it at Modal. Every era of computing brought new workloads that previous infrastructure couldn't support: mainframes, databases, and the cloud. Each time, the company that rebuilt the layer underneath defined the decade. AI is no different, except it touches everything instead of one slice, and the window to build the layer underneath it is open right now. Our customers include category-defining companies like Lovable , Ramp , Cognition, DoorDash, and Suno. They rely on Modal for instant GPU access, sub-second container starts, and native storage, so it's simple to serve low-latency inference, fine-tune models, and access production-ready sandboxes at scale. We recently raised a $355M Series C at a $4.65B valuation, led by General Catalyst and Redpoint Ventures. We've crossed $300M+ ARR and grown fivefold since September. Our team includes creators of popular open-source projects (e.g., Seaborn , Luig i ), academic researchers, international olympiad medalists, and experienced engineering and product leaders with decades of experience. The Role: We're looking for Forward Deployed ML Engineers who want to work at the intersection of deep technical work and direct customer impact. As an ML FDE, you'll partner with leading AI companies and foundation model labs to help them achieve state-of-the-art performance on their most demanding workloads — LLM serving, model training (SFT, RLHF), audio pipelines, scientific computing, and more. You're helping teams reach outcomes most engineers can't on their own. The FDE team today includes world-class software engineers, computational scientists, ML engineers, and former founders. We're looking for people with strong engineering fundamentals, deep curiosity across the AI stack, and energy for working directly with customers on hard problems. You will: Work hands-on with companies like Suno, Lovable, Cognition, and Meta to architect and optimize production AI workloads

About Us: AI needs a new infrastructure layer. We're building it at Modal. Every era of computing brought new workloads that previous infrastructure couldn't support: mainframes, databases, and the cloud. Each time, the company that rebuilt the layer underneath defined the decade. AI is no different, except it touches everything instead of one slice, and the window to build the layer underneath it is open right now. Our customers include category-defining companies like Lovable , Ramp , Cognition, DoorDash, and Suno. They rely on Modal for instant GPU access, sub-second container starts, and native storage, so it's simple to serve low-latency inference, fine-tune models, and access production-ready sandboxes at scale. We recently raised a $355M Series C at a $4.65B valuation, led by General Catalyst and Redpoint Ventures. We've crossed $300M+ ARR and grown fivefold since September. Our team includes creators of popular open-source projects (e.g., Seaborn , Luig i ), academic researchers, international olympiad medalists, and experienced engineering and product leaders with decades of experience. The Role: We’re looking for an Infrastructure Security Engineer to design and secure the core systems that power our platform. This role focuses on building security directly into our infrastructure—from container isolation and orchestration to identity and secrets management in a multi-tenant, cloud-native environment. You’ll work closely with engineering teams to define secure primitives and ensure our platform is resilient, scalable, and trustworthy by design. This is a hands-on, deeply technical role focused on real systems, not compliance or policy. What You'll Do: Platform & Runtime Security Design and improve isolation mechanisms for multi-tenant workloads (containers, sandboxing, execution environments) Strengthen boundaries between customers, workloads, and internal systems Identify and mitigate risks in distributed, dynamic compute environments Container &

About Us: AI needs a new infrastructure layer. We're building it at Modal. Every era of computing brought new workloads that previous infrastructure couldn't support: mainframes, databases, and the cloud. Each time, the company that rebuilt the layer underneath defined the decade. AI is no different, except it touches everything instead of one slice, and the window to build the layer underneath it is open right now. Our customers include category-defining companies like Lovable , Ramp , Cognition, DoorDash, and Suno. They rely on Modal for instant GPU access, sub-second container starts, and native storage, so it's simple to serve low-latency inference, fine-tune models, and access production-ready sandboxes at scale. We recently raised a $355M Series C at a $4.65B valuation, led by General Catalyst and Redpoint Ventures. We've crossed $300M+ ARR and grown fivefold since September. Our team includes creators of popular open-source projects (e.g., Seaborn , Luig i ), academic researchers, international olympiad medalists, and experienced engineering and product leaders with decades of experience. The Role: We are looking for PhD research interns with strong research experience in reinforcement learning, machine learning, and foundation models, including large language and multimodal models, to join our research team. This internship is well suited to candidates interested in improving existing methods and developing new techniques for large-scale model training, optimization, and inference, extending models to long-context and long-horizon tasks, and improving inference-time efficiency, reliability, and robustness in high-stakes real-world deployments. Preferred Qualifications: Currently pursuing a PhD in computer science, machine learning, or a related field. A demonstrated record of research in reinforcement learning, machine learning, foundation models, or related areas. Experience developing and evaluating large-scale models or machine learning systems. Familiari

About Us: AI needs a new infrastructure layer. We're building it at Modal. Every era of computing brought new workloads that previous infrastructure couldn't support: mainframes, databases, and the cloud. Each time, the company that rebuilt the layer underneath defined the decade. AI is no different, except it touches everything instead of one slice, and the window to build the layer underneath it is open right now. Our customers include category-defining companies like Lovable , Ramp , Cognition, DoorDash, and Suno. They rely on Modal for instant GPU access, sub-second container starts, and native storage, so it's simple to serve low-latency inference, fine-tune models, and access production-ready sandboxes at scale. We recently raised a $355M Series C at a $4.65B valuation, led by General Catalyst and Redpoint Ventures. We've crossed $300M+ ARR and grown fivefold since September. Our team includes creators of popular open-source projects (e.g., Seaborn , Luig i ), academic researchers, international olympiad medalists, and experienced engineering and product leaders with decades of experience. The Role: We're looking for a Growth Engineer to own the technical foundation of Modal's marketing and developer-facing web surfaces: the marketing site, docs site, growth landing pages, high-profile microsites, forms, analytics instrumentation, and the integrations that help users discover, understand, and get started with Modal. This is a frontend-heavy role for someone with strong product taste, web engineering craft, and a business-owner mindset. You'll partner with Product Engineering, Design, Data, and Growth to ship polished, measurable web experiences from high-profile projects like the GPU Glossary and LLM Engine Advisor to internal tooling that helps teams publish content faster. When this role is going well, Modal launches new pages, docs experiences, campaigns, and experiments quickly without sacrificing performance, craft, or measurement. In this role you will:

About Us: AI needs a new infrastructure layer. We're building it at Modal. Every era of computing brought new workloads that previous infrastructure couldn't support: mainframes, databases, and the cloud. Each time, the company that rebuilt the layer underneath defined the decade. AI is no different, except it touches everything instead of one slice, and the window to build the layer underneath it is open right now. Our customers include category-defining companies like Lovable , Ramp , Cognition, DoorDash, and Suno. They rely on Modal for instant GPU access, sub-second container starts, and native storage, so it's simple to serve low-latency inference, fine-tune models, and access production-ready sandboxes at scale. We recently raised a $355M Series C at a $4.65B valuation, led by General Catalyst and Redpoint Ventures. We've crossed $300M+ ARR and grown fivefold since September. Our team includes creators of popular open-source projects (e.g., Seaborn , Luig i ), academic researchers, international olympiad medalists, and experienced engineering and product leaders with decades of experience. The Role: Most of the value of owning a model shows up at serving time. We're building a platform that covers the whole life of an LLM -- train it, deploy it, observe it -- and inference is where teams feel the difference every day. We already run elastic inference, sandboxes, distributed volumes, and multi-node training, and we control the infrastructure underneath, so the serving stack is ours to shape rather than something we resell. You will do hands-on inference research at Modal, working with the research lead to pick high-impact bets and owning them end to end. The bets that matter most are the ones that move cost per token and tail latency on the workloads our customers actually run. What you'll do: Own end-to-end inference research bets: speculative decoding, disaggregated prefill/decode, quantization (FP8, INT4), KV-cache and memory management, autoscaling for spik

About Us: AI needs a new infrastructure layer. We're building it at Modal. Every era of computing brought new workloads that previous infrastructure couldn't support: mainframes, databases, and the cloud. Each time, the company that rebuilt the layer underneath defined the decade. AI is no different, except it touches everything instead of one slice, and the window to build the layer underneath it is open right now. Our customers include category-defining companies like Lovable , Ramp , Cognition, DoorDash, and Suno. They rely on Modal for instant GPU access, sub-second container starts, and native storage, so it's simple to serve low-latency inference, fine-tune models, and access production-ready sandboxes at scale. We recently raised a $355M Series C at a $4.65B valuation, led by General Catalyst and Redpoint Ventures. We've crossed $300M+ ARR and grown fivefold since September. Our team includes creators of popular open-source projects (e.g., Seaborn , Luig i ), academic researchers, international olympiad medalists, and experienced engineering and product leaders with decades of experience. The Role: We're looking for a Detection & Response Engineer to build the systems that help us identify, investigate, and respond to threats across our platform. This is an engineering role focused on automation. You'll build detections, investigation tooling, and response capabilities that scale with our infrastructure, using AI where it meaningfully improves signal, investigation speed, and operational effectiveness. You'll work closely with infrastructure, platform, and security engineers to ensure every incident makes the platform more resilient. What You'll Work On: Detection Engineering Design and build high-fidelity detections for attacks, abuse, and anomalous behavior across our infrastructure and production systems Continuously improve detections based on telemetry, threat intelligence, and lessons learned from incidents Improve visibility across cloud infrastruc

About Us: AI needs a new infrastructure layer. We're building it at Modal. Every era of computing brought new workloads that previous infrastructure couldn't support: mainframes, databases, and the cloud. Each time, the company that rebuilt the layer underneath defined the decade. AI is no different, except it touches everything instead of one slice, and the window to build the layer underneath it is open right now. Our customers include category-defining companies like Lovable , Ramp , Cognition, DoorDash, and Suno. They rely on Modal for instant GPU access, sub-second container starts, and native storage, so it's simple to serve low-latency inference, fine-tune models, and access production-ready sandboxes at scale. We recently raised a $355M Series C at a $4.65B valuation, led by General Catalyst and Redpoint Ventures. We've crossed $300M+ ARR and grown fivefold since September. Our team includes creators of popular open-source projects (e.g., Seaborn , Luig i ), academic researchers, international olympiad medalists, and experienced engineering and product leaders with decades of experience. The Role: We’re looking for an Engineering Manager to lead a group of highly experienced engineers. This is a hands-on leadership role where you’ll spend roughly half your time on technical contribution and half on people management, depending on the need. You’ll work closely with the team to set direction, remove blockers, and foster a strong engineering culture as they tackle complex systems challenges in distributed computing, large-scale data handling, and performance optimization. Who You Are: We think you are an experienced engineering leader who thrives close to the work and enjoys building alongside their team when needed. You earn trust through technical depth, communicate with clarity, and help great engineers move fast and make sound decisions. You thrive in a fast paced environment, you are pragmatic, calm under pressure, and focused on impact. Requirements: At l

About Us: AI needs a new infrastructure layer. We're building it at Modal. Every era of computing brought new workloads that previous infrastructure couldn't support: mainframes, databases, and the cloud. Each time, the company that rebuilt the layer underneath defined the decade. AI is no different, except it touches everything instead of one slice, and the window to build the layer underneath it is open right now. Our customers include category-defining companies like Lovable , Ramp , Cognition, DoorDash, and Suno. They rely on Modal for instant GPU access, sub-second container starts, and native storage, so it's simple to serve low-latency inference, fine-tune models, and access production-ready sandboxes at scale. We recently raised a $355M Series C at a $4.65B valuation, led by General Catalyst and Redpoint Ventures. We've crossed $300M+ ARR and grown fivefold since September. Our team includes creators of popular open-source projects (e.g., Seaborn , Luig i ), academic researchers, international olympiad medalists, and experienced engineering and product leaders with decades of experience. The Role: We're building a platform that covers the whole life of an LLM: training it, deploying it, and observing it in production. We already run multi-node training, elastic inference, sandboxes, and distributed volumes, and we control the infrastructure underneath. We’re looking for research depth in post-training to sit alongside our systems and product work. What you'll do: We are looking for research scientists with a strong track record in reinforcement learning, machine learning, and foundation models, including large language and multimodal models, to join our research team. This role is well suited to candidates interested in improving existing methods and developing new techniques for large-scale model training, optimization, and inference, extending models to long-context and long-horizon tasks, and improving inference-time efficiency, reliability, and robustnes

Here’s a summary of the role: Lead a strong engineering team, deliver software that matters, and help people do the best work of their careers. This is a hands-on team management role for someone who enjoys combining people leadership, technical fluency, and modern AI-enabled ways of working to build reliable products at scale. You’ll lead engineers building secure, scalable microservices and APIs using TypeScript and AWS. You’ll partner closely with Product, Security, DevOps, and peer engineering leaders to deliver roadmap outcomes, improve team effectiveness, and create an environment where engineers can grow, own their work, and build high-quality systems with confidence. Here’s a breakdown of what you’ll do, not all of it, just the important stuff: Lead, coach, and support a team of engineers, creating clarity around priorities, ownership, expectations, and growth goals. Partner with Product, Security, DevOps, and peer engineering leaders to plan and deliver roadmap commitments while balancing quality, pace, and sustainability. Create a healthy delivery environment by improving team processes, removing blockers, supporting planning and estimation, and reinforcing strong engineering practices. Maintain enough technical depth to guide design discussions, challenge risks, review trade-offs, and support the team in building secure and maintainable systems. Use AI tools and encourage responsible AI-assisted ways of working that improve productivity while protecting privacy, security, quality, and good judgment. Support hiring, feedback, performance conversations, and career development so the team continues to grow in capability and confidence. These are the essentials you’ll need to get an interview: 8 or more years of software engineering experience, including experience leading projects and supporting or managing engineers in an agile product environment. Experience managing teams that build cloud-native microservices in AWS. Strong people leadership skills, with