Lead Endpoint Researcher - Pointfive

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. 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. 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. Founders with a track record Category-defining product Hard systems problems Local AI is becoming infrastructure Early-stage leverage Frontier of AI developer tooling

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. 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. 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. Founders with a track record Category-defining product Hard systems problems Local AI is becoming infrastructure Early-stage leverage Frontier of AI developer tooling

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. 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. 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. Founders with a track record Category-defining product Hard systems problems Local AI is becoming infrastructure Early-stage leverage Frontier of AI developer tooling