Head of LLM and AI Research

The role

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 Head of LLM and AI Research to lead 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 leading 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?

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Head of LLM and AI Research

PointFive, Tel Aviv

The role

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

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

What you will do

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.

Head of LLM and AI Research

The role

Head of LLM and AI Research

The role

What you will do

What you need

Nice to have

Research areas

Our tech stack

Why PointFive

About PointFive

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