At a Glance
- Tasks: Join our AI Research team to innovate and improve agent behaviour and evaluation.
- Company: Dynamic tech company focused on cutting-edge AI research and development.
- Benefits: Competitive salary, health insurance, pension contributions, and a pet-friendly office.
- Other info: Hybrid work model with regular team lunches and social events.
- Why this job: Make a real impact in AI by shaping how software is built and evaluated.
- Qualifications: 4+ years in AI/ML, strong product instincts, and deep curiosity about agents.
The predicted salary is between 80000 - 98000 £ per year.
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
- 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 following skill areas:
- 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.
- 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
- 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.
Member of Technical Staff- Research Engineer (Harness Engineering and Agentic Orchestration) in City of London employer: Tessl
At Tessl, we pride ourselves on fostering a dynamic and collaborative work environment where innovation thrives. As a member of our AI Research team, you'll enjoy meaningful autonomy, competitive benefits including health insurance and pension contributions, and the opportunity to engage directly with customers to shape impactful research. Located just minutes from King's Cross, our pet-friendly office promotes a vibrant culture with regular team lunches and socials, making it an excellent place for personal and professional growth.
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We think this is how you could land Member of Technical Staff- Research Engineer (Harness Engineering and Agentic Orchestration) in City of London
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We think you need these skills to ace Member of Technical Staff- Research Engineer (Harness Engineering and Agentic Orchestration) in City of London
Some tips for your application 🫡
Show Off Your Projects:In the world of data science, your projects can speak volumes about your skills. Make sure to showcase a few key projects in your CV or portfolio, especially those that highlight your ability to work with data sets, build models, or use relevant tools like Python, R, or SQL. Don’t forget to include links to any GitHub repositories if applicable!
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Craft a Tailored Cover Letter:For a full-time role at Tessl, your cover letter should reflect your passion for data science and your excitement about the specific projects or values of the company. Dive into why you’re a good fit, how your skills align with their needs, and any unique perspectives you can bring to the team.
Stand Out with Relevant Courses and Certifications:Although experience talks, relevant courses or certifications can be your ticket to impressing hiring managers at Tessl. Mention any standout courses you've completed that equipped you with essential skills, such as machine learning certifications or data visualisation courses. This shows your commitment to continuously developing your skills in the field!
How to prepare for a job interview at Tessl
✨Brush Up on Your Statistics
For a data science role, we need to seriously sharpen our statistics skills. Get ready to tackle technical questions on probability distributions, hypothesis testing, and regression analysis. These are often the bread and butter of data science interviews, so don't just skim over them!
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Prepare a killer portfolio showcasing your data science projects. We should include details about the datasets used, the tools and techniques applied, and the impact of your findings. If we can walk them through a particularly challenging project or a cool visualisation that had real-world implications, it’ll really make us stand out!
✨Get Comfortable with Python and R
Most data science positions require us to be proficient in programming languages like Python and R. We should practice common libraries like pandas, NumPy, and scikit-learn, and be ready for live coding exercises or algorithm questions. Showing off our coding chops can really impress the interviewers at Tessl!
✨Prepare for Case Studies
Expect to encounter real-world case studies during the interview. We might be asked how we’d approach a data problem or analyse a dataset to extract insights. It's essential to think out loud and demonstrate our problem-solving process so that the interviewer can see our logical thinking in action.
