---
title: 'Member of Technical Staff- Research Engineer (Harness Engineering and Agentic Orchestration) at Tessl'
canonical: 'https://feeny.ai/job/member-of-technical-staff-research-engineer-harness-engineering-and-agentic-fxpqcq1fpk7s'
type: 'job'
last_seen: '2026-09-12'
---

# Member of Technical Staff- Research Engineer (Harness Engineering and Agentic Orchestration) at Tessl

- **Company:** Tessl
- **Location:** London, United Kingdom
- **Employment:** full-time
- **Work type:** hybrid
- **Posted:** 2026-05-28
- **Last confirmed live:** 2026-09-12
- **Apply:** https://jobs.ashbyhq.com/tesslcareers/f6d8711c-d3f2-4b47-9330-cfece341be4f/application

**Skills:** LLMs, Agentic systems, Dataset curation, Evaluation frameworks, Python, AI/ML product development, Coding agents, Code-generation systems, Reinforcement Learning, Bandit optimization, Synthetic data generation, Dataset infrastructure

> Join the AI Research team to build and evaluate AI coding agents. You will design agent harnesses, develop evaluation methodologies, and analyze failure modes to improve product performance through data-driven experiments.

## Job description

Tessl is a fast-growing Series A startup based in London, founded by Guy Podjarny, who previously founded Snyk. We’ve raised more than $100 million from world-class investors, including Index Ventures, Accel, GV and Boldstart. In 2025, Tessl ranked #2 in Sifted’s B2B SaaS Rising 100 and #20 in its AI 100.

We’re building the context layer for AI coding agents and a platform for AI-native software development. As an early member of the team, you’ll help shape how we build and scale a company operating at the forefront of AI and software development.

## Overview of the role

We're hiring a Research Engineer to join our AI Research (AIR) team. You'll work on the components that make the outer loop real: how agent harnesses orchestrate model behaviour, how we evaluate what's actually working, how pipelines turn production traces into the next round of improvement, and how we diagnose the failure modes that matter to real users. These aren't four separate workstreams — they're parts of one system, and we want people who see them that way. We expect you to sit close to customers — joining calls, watching sessions, reading traces — and to let real workflows shape your research priorities. You'll have meaningful autonomy and the resources to run substantial experiments where the bar for success is shipped impact. You'll report to our AI Research Lead, and collaborate closely with engineering, product, and design.

## What we're looking for

We're explicitly building coverage across four skill areas. You don't need to be strong in all of them — but you should bring depth in at least one:

- Agent harness and orchestration design — how tools, context, and control flow combine to make a useful agent.
- Agentic eval methodology — task and repo-level evals, dataset curation, the craft of measuring what actually matters.
- Outer-loop and pipeline thinking — feedback loops, training-data flywheels, bandit-style optimisation, anything that goes beyond a single agent session.
- Failure-mode analysis — instrumenting agents, reading traces at volume, surfacing patterns engineering can act on.

Essential

- 4+ years shipping AI/ML products in a startup or applied industry setting, with recent hands-on experience with LLMs and agentic systems.
- Demonstrated depth in at least one of the four skill areas above.
- Strong product and customer instincts: comfort joining customer calls, watching session recordings, and letting real workflows shape what you work on.
- Sharp evaluation judgement: benchmarks where they exist, vibes and quick prototypes where they don't, and the taste to know which is appropriate.
- Experience building datasets for evaluation or training, including the pipeline work that goes with it.
- Deeply curious about agents and excited about reshaping how software is built.

## Nice to have

- A Masters or PhD in a relevant computational field.
- Direct experience with coding agents or code-generation systems.
- Background in RL, bandits, or other outer-loop optimisation frameworks applied to LLMs.
- Experience building synthetic data, dataset infrastructure, or internal tooling that other engineers actually used.
- A project you can show us (GitHub links welcome) and a thoughtful answer to "Why Tessl?"

## What you'll do

No two weeks will look the same. A flavour:

- Sit in on a customer session, understand how their agents are failing, design an eval that captures it, and drive a fix through to shipped improvement.
- Close a piece of the outer loop end to end: production signal in, dataset out, eval scored, harness change shipped, metric moved.
- Own a slice of our eval infrastructure: dataset curation, harness configuration, runner, analysis, and the comms back to engineering.
- Prototype a new harness or context configuration and measure whether it actually moves the needle on real customer tasks.
- Dig through pages of agent traces, build the tooling you need to make sense of them, and brief the team on what you found.
- Partner with product and engineering on near-term shipping problems by bringing research rigour.
- Pull a recent paper apart, work out what's actually transferable to our platform, and turn it into a concrete experiment.

You’ll be successful if… In your first 3 months, you might have shipped a new eval suite for a real customer workflow, improved an agent harness based on trace analysis, or built a pipeline that turns production failures into reusable test cases.

Salary and benefits Competitive salary commensurate with experience. Health insurance extending to partners and dependents, pension contributions, and the rest of what you'd expect. Our office is a couple of minutes from King's Cross — pet friendly, with regular team lunches, drinks, and socials. We're hybrid, with Monday, Tuesday, and Thursday as the primary in-office days.

Application process

- Intro call to understand "Why Tessl?" and to tell you a bit about us.
- A call with our AI Research Lead to understand your ways of working and how you use agents.
- A 4 hour technical take-home exercise extending our one-shot implementation.
- A half-day on-site session including whiteboarding and hands-on activities.
- Leadership chats with our Head of People, Head of Engineering and CEO.

We care deeply about the warm, inclusive environment we’re building at Tessl and we value diversity – we welcome applications from those typically underrepresented in tech. If you like the sound of this role but are not totally sure whether you’re the right person, do apply anyway!

Learn how we think and work

- [On Tessl, The AI Native Development Startup](https://www.tessl.io/blog/announcing-tessl-the-ai-native-development-startup)
- [Announcing skills on Tessl: the package manager for agent skills](https://tessl.io/blog/skills-are-software-and-they-need-a-lifecycle-introducing-skills-on-tessl/)
- [Podcast Episode: The End of Fragmented Agent Context, Guy Podjarny Tessl CEO](https://youtu.be/ntkM-hRblfo)

## About Tessl

## Company Overview
- **One-liner**: Tessl provides a security, visibility, and governance platform for AI agent skills, enabling enterprise teams to manage, secure, and continuously improve skills across agents like Claude Code, Cursor, and Copilot.
- **Entity Type**: Private (Series A; total raised $125M)
- **Headquarters**: London, United Kingdom (Kings Cross area)
- **Founded**: 2024
- **Founders**: Guy Podjarny (Founder & CEO)

## Core Business
- **Primary industry**: Developer tools / AI infrastructure / Agent enablement
- **Target customers**: Enterprise engineering and platform teams, security leaders; B2B
- **Mission or purpose statement**: "Skills are the new code. Treat them that way." – Tessl aims to turn chaotic, invisible skill sprawl into a governed, measurable system that scales agent adoption safely.

## Products & Services
- **Tessl Agent Enablement Platform**: The core SaaS platform that integrates with AI coding agents (Claude Code, Cursor, Copilot, Gemini) to publish, version, security-scan, and govern skills. Features include a shared skill registry, policy enforcement, audit logs, eval-driven improvement dashboards, and three-layer visibility (published, project coverage, real activation).

## Market Standing
- **Valuation/Market Cap**: Not disclosed (private)
- **Key Metric**: Total Funding – $125M (Series A, as of 2024 per CB Insights)
- **Notable Investors/Partners**: Index Ventures, GV (Google Ventures), boldstart ventures, Accel
- **Growth Signals**: Founded in 2024 by serial entrepreneur Guy Podjarny (ex-Snyk founder, scaled to $8B+ valuation); rapid Series A raise of $100M+; early category leadership in "skill management" for AI agents; active hiring across engineering, product, sales, DevRel, and marketing.

## Competitive Advantages
- **Founder-market fit**: Guy Podjarny previously founded Snyk, a multi-billion-dollar developer security category leader, and sold a prior company (Blaze) to Akamai.
- **First-mover in a new category**: Tessl defines the "agent enablement" layer that sits between developers and AI agents – a gap no other major tool addresses.
- **Depth of governance**: Security scanning, policy gating, audit logs, and eval-driven improvement are baked into the platform, making it enterprise-ready from day one.
- **Strong investor syndicate**: Backed by top-tier VC firms with deep developer-tool and infrastructure expertise (Index, GV, Accel, boldstart).

## Strategic Focus
- **Current priorities**: Scaling enterprise adoption – proving visibility, governance, and continuous improvement for agent skills; building integrations with all major AI coding agents; expanding the skills registry and community.
- **Growth direction**: Opening a San Francisco remote office (DevRel role), growing sales and marketing teams, and deepening platform capabilities in security and compliance.

## Why Work Here
- **Culture highlights**: Described as "AI Native" with operating principles like "Grab a Shovel" (get scrappy), "Embrace the Pace," "Build, then Break," and "Own It." The company values in-person collaboration and regularly hosts AI community events at its London HQ.
- **Remote/hybrid policy**: Hybrid with anchor days in the office on Monday, Tuesday, and Thursday. Some roles (e.g., DevRel) are remote-friendly (San Francisco, New York).
- **Perks**: 25 days holiday, employee stock options, private health insurance, 2 volunteer days, learning & development budget, enhanced parental leave and sick leave, Cycle to Work scheme, commuter stipend for travel outside London.

## Sources
1. [tessl.io](https://tessl.io/) – Product overview and features
2. [tessl.io/careers](https://tessl.io/careers/) – Culture, perks, and open roles
3. [tessl.io/about](https://tessl.io/about/) – Leadership, funding, and investors
4. [cbinsights.com](https://www.cbinsights.com/company/tessl) – Funding history and company data

## Other roles at Tessl

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