Head of LLM Research
No salary listed
Expert
Tel Aviv-Yafo, Israel
Hybrid
AI & Machine LearningAI Research
About the job
Requirements
- Deep understanding of modern large language models and transformer-based architectures.
- Strong hands-on experience evaluating and experimenting with both frontier and open-weight models.
- Strong understanding of inference behavior, including prefill, decoding, KV caches, context windows, batching, memory usage, and token generation performance.
- Experience with model optimization techniques such as quantization, distillation, LoRA, fine-tuning, or model compression.
- Experience building evaluation frameworks for LLM quality and behavior.
- Strong Python proficiency and familiarity with the modern machine-learning ecosystem.
- Ability to read, understand, and reproduce ideas from current machine-learning research papers.
- Ability to bridge research and production, assessing both whether an approach works and whether it is practical to deploy.
Responsibilities
- 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 LLM research team over time.
Desired Qualifications
- Experience with open-weight models such as Llama, Qwen, DeepSeek, Mistral, Gemma, GLM, or similar model families.
- Experience with frontier model APIs including OpenAI, Anthropic, Google, and other leading providers.
- Experience with inference frameworks such as vLLM, SGLang, llama.cpp, MLX, TensorRT-LLM, Ollama, or ONNX Runtime.
- Deep understanding of quantization techniques including FP8, INT8, INT4, AWQ, GPTQ, GGUF, and related approaches.
- Experience with GPU performance optimization, CUDA, Metal, ROCm, or DirectML.
- Familiarity with distributed inference and multi-GPU serving.
- Experience with model routing, mixture-of-model systems, cascades, or adaptive inference.
- Experience with reinforcement learning, preference optimization, DPO, GRPO, or related post-training techniques.
- Familiarity with agentic systems, coding agents, tool-use models, and computer-use models.
- Experience building or evaluating coding benchmarks and software-engineering agents.
- Experience with synthetic data generation, dataset curation, and automatic evaluation.
- Research publications or meaningful contributions to open-source machine-learning projects.
- Experience leading a small applied research or machine-learning research team.
About the company
PointFive provides an enterprise cloud cost optimization platform focused on cloud efficiency through Cloud Efficiency Posture Management (CEPM) for multi-cloud environments (AWS, GCP, Azure) to reduce wasteful spend. It works by DeepWaste detection, scanning cloud infrastructure (such as DynamoDB, EKS, and S3) to uncover misconfigurations and idle resources, and delivers workflows and contextual insights that empower engineers to implement cost-saving measures continuously. The platform differentiates itself by combining granular, proprietary waste-detection with engineering-facing remediation and proactive optimization across multiple clouds, informed by the founders’ cybersecurity background, and it monetizes through enterprise software licenses plus partnerships with VARs, MSPs, and GSIs. Its goal is to help enterprises lower and control cloud spend over time by shifting from reactive fixes to an ongoing, automated cost-optimization program integrated with engineering teams.
Company Size
51-200
Company Stage
Growth Equity (Venture Capital)
Total Funding
$96M
Headquarters
New York City, New York
Founded
2023
Simplify's Take
What believers are saying
- June 2026 Series B raised $60 million from Accel, Salesforce Ventures, and Index Ventures.
- PointFive says ARR grew 6x from 2024 to 2025, signaling strong enterprise demand.
- Named customers Nubank, E.ON, Hertz, Fanatics, Swiss Post, and NICE validate broad adoption.
What critics are saying
- CloudZero, Finout, and Apptio are packaging AI spend controls, shrinking PointFive's differentiation by 2027.
- AWS, Azure, and GCP can bundle native AI cost analytics, commoditizing PointFive's core workflow.
- If enterprise AI budgets normalize after 2026's spending surge, PointFive's $500 million valuation compresses fast.
What makes PointFive unique
- PointFive's DeepWaste engine ties cloud waste directly to engineering remediation, not dashboards, since 2026.
- Founders Alon Arvatz, Gal Ben-David, and Amir Hozez sold IntSights to Rapid7 in 2021.
- June 2026 AI Efficiency OS and TokenShift extend PointFive across cloud, data, and AI.
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Benefits
- Remote Work Options
- Company Equity