Head of LLM Research

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.

PointFive is building infrastructure for the next generation of AI-powered engineering.

As AI agents become embedded into developer workflows, the underlying model layer is changing rapidly. Organizations are no longer choosing between a handful of hosted APIs. They are increasingly operating across frontier models, open-weight models, locally deployed models, specialized models, and dynamically routed combinations of them.

We’re looking for an AI researcher to take an integral part in PointFive’s research into how these models behave, how they should be evaluated, and how they can be deployed and used more efficiently across real engineering workloads.

This is a deeply technical research role with direct product impact.

You’ll study frontier and open-source models, develop evaluation frameworks, investigate inference and model optimization techniques, explore local deployment strategies, and help answer questions such as:

Which model should handle a particular task?

How much capability do we lose when we quantize it?

Can a smaller local model replace a frontier API for a specific workload?

How should models be routed across latency, cost, privacy, and quality constraints?

How do we measure whether one model is actually better than another for agentic software engineering?

Your work will directly shape PointFive’s model strategy and the intelligence behind our AI infrastructure products.

• Define and lead PointFive’s LLM research agenda across model evaluation, inference optimization, local models, routing, and model adaptation.
• Continuously evaluate frontier and open-weight models across real-world software engineering and agentic workloads.
• Design rigorous evaluation frameworks for model quality, reasoning, tool use, code generation, command execution, summarization, and agent behavior.
• Build benchmarks that reflect actual developer workflows rather than generic academic tasks.
• Research model efficiency techniques including quantization, distillation, speculative decoding, prefix caching, KV-cache optimization, batching, and context management.
• Investigate when smaller or locally deployed models can replace expensive frontier models without materially degrading task quality.
• Evaluate model architectures, parameter sizes, quantization formats, and runtime configurations across heterogeneous hardware.
• Research and benchmark local inference stacks including llama.cpp, MLX, vLLM, SGLang, ONNX Runtime, Ollama, TensorRT-LLM, and emerging runtimes.
• Study inference performance across Apple Silicon, NVIDIA GPUs, AMD GPUs, CPUs, Windows workstations, Linux machines, and other endpoint configurations.
• Develop model-routing strategies that optimize across quality, latency, cost, privacy, context size, and hardware availability.
• Explore intelligent cascades where smaller models handle common tasks and more capable models are invoked only when necessary.
• Research model specialization through fine-tuning, LoRA, adapters, distillation, prompt optimization, and other model adaptation techniques.
• Investigate model behavior under constrained environments, including offline execution, limited memory, limited compute, and local-only inference.
• Evaluate agent-specific model behavior, including planning, tool selection, shell interaction, code editing, error recovery, and long-running task execution.
• Analyze failure modes such as hallucination, tool misuse, context degradation, reasoning collapse, excessive token consumption, and unstable agent loops.
• Design experiments that quantify the tradeoffs between model capability, inference cost, token consumption, latency, and resource utilization.
• Build internal research infrastructure for reproducible model experiments, benchmarking, dataset management, and evaluation.
• Track frontier model releases and emerging research, rapidly determining which developments are meaningful for PointFive’s products.
• Collaborate closely with engineering and product teams to translate research results into production capabilities.
• Build and lead a small, exceptional LLM research team over time.

Research Areas

Choosing the optimal model dynamically based on task difficulty, latency requirements, cost, privacy constraints, and hardware availability.

Determining which workloads can reliably move from cloud models to models running directly on developer endpoints.

Understanding how far models can be quantized, distilled, or otherwise optimized before meaningful capability is lost.

Building evaluation methods for agents that operate over codebases, shells, developer tools, and long-running workflows.

Improving throughput, latency, memory consumption, and token efficiency across different model architectures and runtimes.

Investigating whether smaller specialized models can outperform general-purpose frontier models for narrow engineering tasks.

Understanding how models behave as context grows, what information gets lost, and how context can be compressed or structured more intelligently.

Matching models and inference strategies to available GPUs, CPUs, NPUs, Apple Silicon, and other endpoint hardware.

Quantifying when additional model capability actually produces better outcomes — and when it simply produces more expensive tokens.

Our Tech Stack

Python, Go, PyTorch, Hugging Face, vLLM, SGLang, llama.cpp, MLX, CUDA, Metal, AWS, Cloudflare, Snowflake, and a rapidly evolving ecosystem of frontier and open-weight models.

Why PointFive

We operate where LLM research meets real enterprise infrastructure. The questions we work on have immediate implications for how thousands of engineers use AI every day.

Instead of optimizing against abstract benchmarks, you'll be able to study how models perform across real software engineering and agentic workflows.

This isn't a research lab disconnected from product. Successful ideas can move rapidly from experiment to production.

Enterprises will increasingly operate fleets of models across cloud APIs, private infrastructure, and developer endpoints. Deciding how those models are selected, optimized, deployed, and governed is becoming a fundamental infrastructure problem.

New models, architectures, inference techniques, and agent systems appear constantly. Your job is to understand which developments matter — and turn them into an advantage for PointFive.

Built and sold IntSights to Rapid7. Backed by top-tier investors with deep conviction in the category.

You'll define PointFive's LLM research strategy, research methodology, and eventually the team itself.