LLM Researcher

Posted 5 Days Ago
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Tel Aviv, ISR
Hybrid
Senior level
Artificial Intelligence • Cloud • Software • Infrastructure as a Service (IaaS)
The Role
Design and run rigorous evaluations and large-scale experiments for API-based coding agents. Build benchmarks, research infrastructure, and optimization techniques for context, retrieval, memory, and tool interactions. Translate research into production improvements and publish findings.
Summary Generated by Built In
About PointFive

PointFive is the AI Efficiency OS. From the cloud to the coding agent, we're the only platform that manages AI spend everywhere it happens.

Engineering and FinOps teams use PointFive to make their organizations more efficient and their cloud and AI more effective. We don't just show what you spend. We show what you're wasting, and we fix it autonomously. NuBank saw ROI in 10 days. Customers average 1,200%+ ROI and a 4.9 rating on G2.

Founded by the team behind IntSights (acquired by Rapid7), PointFive recently closed a $60M Series B led by Accel, with participation from Entrée Capital and Salesforce Ventures.

About the Role

We're looking for a Research Scientist to design the next generation of evaluation methodologies for API-based coding agents. This role combines systems research, benchmarking, and applied machine learning to answer fundamental questions about how coding agents can become more accurate, efficient, and cost-effective without requiring access to model weights — with immediate impact on real production workloads.

What You'll Do
  • Design rigorous evaluation methodologies for API-based coding agents, with and without access to external tools.

  • Build statistically sound benchmarks that measure cost, latency, accuracy, reliability, and developer productivity.

  • Develop novel techniques for optimizing agent context, retrieval, memory, and tool interactions while preserving correctness.

  • Design and analyze large-scale experimental campaigns using production APIs across multiple foundation models.

  • Build reusable research infrastructure, datasets, simulators, and evaluation frameworks for agentic systems.

  • Publish technical reports and contribute research findings that influence both product direction and the broader AI community.

  • Collaborate closely with engineering teams to translate research into production-ready optimization systems.

What We're Looking ForMust-have
  • PhD or equivalent research experience in Computer Science, Machine Learning, Mathematics, Statistics, or a related quantitative field.

  • Strong background in experimental design, statistical analysis, and empirical evaluation.

  • Excellent programming skills in Python and/or Go, with experience building research infrastructure.

  • Demonstrated ability to conduct independent research and communicate findings through publications, technical reports, or open-source projects.

  • Deep understanding of modern LLMs, retrieval systems, or AI agents.

Nice to have
  • Experience working with API-based foundation models such as Claude, GPT, Gemini, or similar systems.

  • Publications in machine learning, systems, information retrieval, software engineering, or AI evaluation.

  • Experience designing benchmarks, datasets, or evaluation frameworks.

  • Familiarity with developer tools, coding assistants, or software engineering workflows.

  • Experience with distributed experimentation and large-scale data analysis.

Our Tech Stack

Go, Python, ONNX Runtime, PyTorch, Transformers, Claude Code, Codex, Cursor, OpenAI API, Anthropic API, Gemini API, Kubernetes, Docker, AWS, GitHub Actions, PostgreSQL

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 — Building AI systems that reduce engineering waste and improve the efficiency of software development through measurable optimization.

  • Research with real-world impact — Your work will be validated on production-scale workloads, influence shipping products, and shape how next-generation coding agents are evaluated.

  • Opportunity to define a new research field — Help establish the benchmarks, methodologies, and scientific standards for evaluating and optimizing API-based coding agents — an area that is only beginning to emerge.

Equal Opportunity Statement

PointFive is proud to be an equal opportunity employer. We celebrate diversity and are committed to creating an inclusive environment for all employees. We welcome candidates from all backgrounds, experiences, and perspectives to apply.

Skills Required

  • PhD or equivalent research experience in Computer Science, Machine Learning, Mathematics, Statistics, or related quantitative field.
  • Strong background in experimental design, statistical analysis, and empirical evaluation.
  • Excellent programming skills in Python and/or Go, with experience building research infrastructure.
  • Demonstrated ability to conduct independent research and communicate findings through publications, technical reports, or open-source projects.
  • Deep understanding of modern LLMs, retrieval systems, or AI agents.
  • Experience working with API-based foundation models such as Claude, GPT, Gemini, or similar systems.
  • Publications in machine learning, systems, information retrieval, software engineering, or AI evaluation.
  • Experience designing benchmarks, datasets, or evaluation frameworks.
  • Familiarity with developer tools, coding assistants, or software engineering workflows.
  • Experience with distributed experimentation and large-scale data analysis.
Am I A Good Fit?
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The Company
Year Founded: 2023

What We Do

PointFive is an AI Efficiency OS and cloud cost optimization platform designed for FinOps, platform, and engineering teams. Using its proprietary DeepWaste™ Detection Engine, the company provides deep waste detection and auditable remediation across cloud, data, and AI infrastructure. PointFive transforms cloud efficiency from a reporting exercise into an operational discipline, helping enterprises reduce wasteful computing expenses and improve engineering accountability at scale.

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