Computational Polymer Chemistry Lead

What we do

Cambrium is a molecular intelligence company engineering advanced materials that outperform. From personal care to textiles mobility to home care we design and manufacture materials that deliver real results today and for generations to come.

Our platform brings together molecular intelligence and living systems to design and produce enzymes polymers and peptides at scale unlocking performance characteristics that traditional chemistry could never access. Within polymers specifically. Our stack: we design enzymes that produce novel monomers then combine them into block copolymers that break through the performance tradeoffs of existing polymers. We have done this once already and now were making it systematic. Polymers today are where proteins were before AlphaFold: the architectures exist the training data doesnt. Were building the lab and the dataset that changes that.

We are an equal-opportunity employer and value diversity. We consider all applications equally regardless of race color ancestry religion sex national origin sexual orientation age citizenship marital status disability or gender identity. We strongly encourage individuals from groups traditionally underrepresented in tech to apply and we can help with immigration.

Your role

You will be the first hire for our new Polymer AI lab. You will lead the computational chemistry track: DFT screening machine-learned interatomic potentials (MLIPs) and the bridge between quantum chemistry and our wet-lab dataset while the polymer synthesis lab comes online in parallel. You report to the CTO and collaborate daily with the Head of Polymers.

Your role is to ensure the computational layer (DFT features MLIP physics SCFT active learning signals) is ready when the experimental data starts flowing. If you do this well within two years youre the co-architect of a polymer foundation model trained on a dataset that doesnt exist anywhere else in the world.

Your Responsibilities

• Screen monomer candidates via DFT to generate enriched representations (HOMO/LUMO partial charges reactivity polarizability) that topological fingerprints miss
• Evaluate and benchmark public universal MLIPs for fit with our monomer chemistry
• Run MLIP fine-tuning experiments on pilot wet-lab data as it arrives from the synthesis team
• Build the computational data pipeline from DFT features to training-ready representations
• Co-design the active learning loop with the CTO: which candidates should the wet lab synthesize next based on model uncertainty and DFT priors
• Set up and maintain the simulation infrastructure on cloud compute
• As the dataset grows: train an in-house MLIP run MD simulations for virtual screening of block copolymers and generate synthetic training data at scale
• Manage the compute budget and make the cloud-vs-on-prem recommendation as we scale

Your profile

• PhD and/or 5 years of experience in computational chemistry materials science chemical physics or a closely related field with a focus in soft matter physics / polymers
• Hands-on DFT experience (Gaussian ORCA VASP or CP2K) with production-scale calculations
• Working knowledge of machine-learned interatomic potentials (MACE NequIP Allegro SchNet or equivalent). Youve trained and evaluated MLIPs on real systems
• Python fluency and comfort with ML frameworks (PyTorch or JAX) including writing training loops
• Experience connecting computational predictions to experimental validation. Youve collaborated with a wet lab and understand what makes a prediction useful in practice
• Published research demonstrating independent scientific contribution

Strong signals:

• Experience with polymer or soft-matter simulations (coarse-grained MD SCFT block copolymer phase behavior polymer thermodynamics)
• Familiarity with active learning or Bayesian optimization for experiment selection
• Experience with molecular simulation engines (LAMMPS GROMACS OpenMM or equivalent)
• Background in concept bottleneck models physics-informed ML or interpretable ML for materials
• Contributions to open-source computational chemistry or ML tooling
• Prior work at an industrial R&D lab where predictions needed to translate into real decisions

What we offer

• Employee Stock Options
• Flexible working hours
• Learning & Development Programme for all our team members
• A once-in-a-career dataset problem: building a foundational dataset for polymers from scratch
• Direct access to a parallel synthesis platform generating the data you model
• Berlin HQ European open-source simulation stack
• 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