Oleh Jobs
Sign inMatch my CV

All jobs

Team8- Fintech Stealth Startup- Senior AI Researcher, Evaluation & Agent Performance

Team8

Where
Tel Aviv, Israel, on-site
Language, from the listing
No Hebrew mentionedA missing mention doesn't mean Hebrew isn't needed. Ask if it matters to you.
Dates
Found 7 Oct 2026
Last checked on the employer's site
3 h ago (7 Oct 2026)
Source
Employer career page (Comeet)

What they ask for

  • 7+ years in ML/NLP research or applied AI, including evaluating LLMs or agents.
  • A track record of designing benchmarks: task sampling, contamination control, scoring methods, and power analysis.
  • Deep knowledge of agent architectures: tool use, retrieval, multi-step planning, MCP or similar protocols.
  • Expert Python, and production-quality eval infrastructure you have shipped.
  • Experience leading research projects end to end, from design to publication or product.

Nice to have

  • Publications at NeurIPS, ICLR, ACL, ICSE or FSE on LLM evaluation, code intelligence or agents.
  • Experience with repo-level code benchmarks (SWE-bench-style).
  • Experience with LLM-as-judge methods and their known failure modes.
  • A background in distributed systems, observability data, or large codebases.

The full listing

Description About us We build the context layer for engineering organizations. We connect code, runtime telemetry, data lineage, and operational systems into one live model of how software runs, then give AI agents that model through chat and MCP. What you'll do • Own the evaluation function for all Cobalt agents: code Q&A, change-impact analysis, incident response, and MCP tool use by coding agents. • Design and build eval harnesses and benchmarks, with ground truth taken from merged PRs, production traces, and code. • Define and track agent performance metrics: recall and precision, cost per correct answer, tool-call efficiency, latency, and failure modes. • Run controlled comparisons across models, prompts, retrieval strategies, and baselines, with statistical rigor. • Diagnose agent failures in retrieval, reasoning, and tool design, and drive the fixes with engineering. • Build regression suites in CI that score every model, prompt, and connector change before release. • Lead external research, including ownership of research papers. • Set the research agenda and mentor future hires. Requirements Requirements • 7+ years in ML/NLP research or applied AI, including evaluating LLMs or agents. • A track record of designing benchmarks: task sampling, contamination control, scoring methods, and power analysis. • Deep knowledge of agent architectures: tool use, retrieval, multi-step planning, MCP or similar protocols. • Expert Python, and production-quality eval infrastructure you have shipped. • Experience leading research projects end to end, from design to publication or product. Nice to have • Publications at NeurIPS, ICLR, ACL, ICSE or FSE on LLM evaluation, code intelligence or agents. • Experience with repo-level code benchmarks (SWE-bench-style). • Experience with LLM-as-judge methods and their known failure modes. • A background in distributed systems, observability data, or large codebases. Why join • Enterprise ground truth: thousands of repos, live traces and production incidents. • Your evals decide what ships. • A founding role in the research function.