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About PointFive
PointFive finds the cost buried inside enterprise infrastructure, from the cloud to coding agents. We pioneered DeepWaste across cloud and data platforms, and we are now doing the same for AI token spend with TokenShift.
Fanatics, H&M, Hertz, Nubank, Citizens Bank and other Fortune 500 companies use PointFive to see and cut what their infrastructure and AI actually cost.
We have raised $96 million, most recently a $60 million Series B led by Accel. Engineering sits in Tel Aviv and our go-to-market team is focused on North America. PointFive Labs, our research arm, publishes original work on how infrastructure and AI actually get paid for.
CRN named us one of the 10 hottest cloud computing startups of 2026, Forbes Israel put us on its Next Billion Dollar Startups list, and Redpoint selected us for its InfraRed 100, its ranking of the private companies shaping the future of cloud infrastructure.
TokenShift is PointFive's newest product — an endpoint runtime for AI coding agents that gives organizations control, visibility, and efficiency across how agents execute commands and consume models.
As AI agents move from chat interfaces into developer machines, they increasingly need access to shells, filesystems, credentials, developer tools, and local compute. At the same time, enterprises are beginning to run models locally — on laptops, workstations, developer VMs, and managed endpoint fleets — across a wide variety of operating systems and hardware architectures.
This role exists to push that frontier forward.
You'll research how AI coding agents execute and isolate workloads, design secure sandboxing mechanisms, and build the infrastructure required to deploy and operate local models across heterogeneous endpoint environments. You'll work across macOS, Windows, Linux, WSL, developer containers, and other runtime environments, figuring out how to provide agents with powerful capabilities without giving them unrestricted access to the machine.
It's a rare opportunity to work at the intersection of operating systems, endpoint security, AI infrastructure, local inference, and developer tooling — in a product already running inside enterprise engineering environments.
Research AI coding agent runtimes — reverse-engineer how tools such as Claude Code, Cursor, Copilot CLI, Codex, and emerging agents execute commands, access files, spawn processes, and interact with the operating system.
Design agent sandboxing architectures that safely constrain filesystem access, networking, process execution, credentials, and other sensitive endpoint capabilities.
Explore and implement OS-native isolation mechanisms across macOS, Windows, and Linux, including containers, namespaces, sandbox profiles, virtualization, restricted processes, and permission models.
Build endpoint "harnesses" that allow TokenShift to intercept, control, observe, and modify agent execution flows.
Design infrastructure for deploying and running local LLMs across heterogeneous endpoint fleets, accounting for operating system, CPU architecture, GPU availability, memory constraints, and hardware acceleration.
Evaluate local inference runtimes and model formats such as llama.cpp, MLX, ONNX Runtime, Ollama, vLLM-compatible runtimes, and emerging alternatives.
Develop mechanisms for model discovery, installation, versioning, updates, health monitoring, and lifecycle management across large numbers of developer machines.
Research hardware-aware model selection and routing — deciding which models can run effectively on Apple Silicon, NVIDIA GPUs, Windows workstations, Linux machines, CPU-only environments, and other endpoint configurations.
Build benchmarks that measure model latency, throughput, memory consumption, startup time, resource contention, and developer experience on real endpoint hardware.
Investigate security boundaries between local models, cloud-hosted models, agents, MCP servers, shells, and enterprise resources.
Collaborate with the core CLI and platform teams to take endpoint research from prototype through production.
Track the rapidly evolving AI agent, sandboxing, and local inference ecosystems and proactively identify technologies PointFive should support.
Requirements
Deep understanding of operating system fundamentals: processes, permissions, filesystems, IPC, networking, signals, and process trees.
Strong understanding of sandboxing and workload isolation concepts.
Hands-on systems experience across multiple operating systems, ideally including Linux, macOS, and Windows.
Experience building or debugging software that runs directly on developer endpoints, workstations, or servers.
Comfort operating close to the metal — binary behavior, system calls, OS APIs, environment variables, config files, process injection/interception, and runtime behavior.
Investigative, reverse-engineering mindset — you enjoy taking apart systems you didn't build and figuring out how they actually work.
Ability to move comfortably between research prototypes and production-quality systems code.
Nice to have
Experience running or deploying local LLMs using frameworks such as llama.cpp, MLX, Ollama, ONNX Runtime, or similar inference runtimes.
Understanding of model quantization, KV-cache behavior, GPU memory constraints, model loading, and inference performance.
Experience with NVIDIA CUDA, Apple Metal, DirectML, ROCm, or other hardware acceleration stacks.
Experience with Linux namespaces, seccomp, cgroups, AppArmor, SELinux, eBPF, or container runtimes.
Familiarity with macOS sandboxing, Endpoint Security Framework, launchd, XPC, virtualization, or Apple Silicon.
Familiarity with Windows security primitives, Job Objects, AppContainers, Windows Sandbox, Hyper-V, WSL, ETW, or Windows process APIs.
Hands-on familiarity with AI coding agents such as Cursor, Claude Code, Copilot CLI, and Codex.
Experience with endpoint management or fleet deployment technologies such as MDM, Intune, Jamf, SCCM, or enterprise software distribution systems.
Experience with containers, microVMs, or lightweight virtualization technologies.
TypeScript experience.
Our Tech Stack
Go, TypeScript, Cloudflare, Snowflake, local inference runtimes, and OS-native endpoint technologies across macOS, Windows, and Linux.
Why PointFive
Founders with a track record
Built and sold IntSights to Rapid7. Backed by top-tier investors with deep conviction in this category.
Category-defining product
We're building the control plane for how AI agents operate across enterprise engineering environments — from model access to endpoint execution.
Hard systems problems
AI agents are becoming increasingly powerful, but giving them access to developer machines introduces an entirely new class of security, isolation, performance, and infrastructure challenges. You'll work directly on those problems.
Local AI is becoming infrastructure
As capable models increasingly run on laptops and workstations, enterprises will need a way to deploy, manage, secure, and govern them across thousands of heterogeneous machines. You'll help define how that infrastructure works.
Early-stage leverage
Your research will directly shape the architecture of the product and determine which capabilities PointFive can deliver next.
Frontier of AI developer tooling
Agents, local models, and endpoint runtimes are evolving extremely quickly. You'll be researching and integrating technologies before they become standard parts of the enterprise developer stack.