You are the first wet-lab hire for our polymer AI lab. You will lead the physical lab: reactor procurement, characterization stack, synthesis protocols, and the ramp from first experiment to steady-state throughput. You collaborate daily with the Head of Polymers and the CTO. A computational chemist is being hired in parallel to lead the compute track. You two are the two co-architects of this new segment.

Your Responsibilities

• Lead lab buildout end-to-end: site preparation, equipment procurement, installation, commissioning
• Set up the characterization stack for molecular weight, thermal, mechanical, rheological, and chemical properties
• Design and validate synthesis protocols for block copolymer families using bio-based monomers produced by our enzyme platform
• Build the sample-to-datapoint pipeline: from reactor output through characterization to structured, ML-ready data
• Ramp throughput from first experiments to thousands of datapoints per month at steady state
• Grow and mentor the synthesis team as the lab scales
• Collaborate with the computational chemist to close the active learning loop: the model suggests what to synthesize next, you make it happen and feed back results
• Inform reactor expansion decisions based on verified yield data

Your profile

• PhD and 5+ years of experience in: Polymer chemistry, polymer physics, or chemical engineering with a strong polymer synthesis focus
• Hands-on experience with step-growth polymerization (polycondensation, polyaddition). You've synthesized polymers, not only characterized them
• Experience building or commissioning a chemistry lab, or leading a significant lab expansion. You know what it takes to go from an empty room to running experiments
• Familiarity with parallel or high-throughput synthesis platforms. Experience with multi-reactor setups is important for this role
• Working knowledge of polymer characterization techniques: GPC, DSC, DMA, rheology, FTIR, and mechanical testing. You don't need to be a specialist in all of them, but you should know what each measurement tells you and when to trust it
• Experience working with data systems (LIMS, ELN, or structured experimental records). We're building an AI training dataset, not a lab notebook collection

Strong signals:

• Experience with block copolymers specifically (phase separation, microphase morphology, structure-property relationships)
• Background in bio-based or sustainable polymers, including the monomer purity and feedstock variability challenges that come with them
• Experience with automation or semi-automated synthesis workflows
• Previous work in an environment where experimental data fed directly into ML models. You understand what "ML-ready data" means in practice
• Track record of growing a lab operation: hiring, managing throughput, optimizing cost per experiment
• Prior industry experience at a polymer company, coatings manufacturer, or materials startup

What We Offer

• Employee Stock Options
• Flexible working hours
• Learning & Development Programme for all our team members
• Build a lab from scratch with real budget and a clear mission, not a retrofit or an afterthought
• Your data trains a foundation model. Every datapoint you generate makes the AI better, and the AI tells you what to make next. This is the tightest experiment-to-model loop in polymer science
• Gym membership
• Subsidised lunch and impassioned lunch discussions
• 30 days of individual vacation plus up to 5 all-company holidays between the 25th of December and 1st of January
• Regular team events
• Be part of our journey to a future of advanced materials that outperform