Diffractive Labs

Build with us

• Research Scientist, Materials (Computational) →

  London · San Francisco · Remote

  What We're Looking For

  We are seeking Founding Computational Materials Scientists to lead our drive of multi-scale simulations across a wide spectrum of materials phenomena, from electrons to microstructures, from solids to interfaces. You'll develop computational workflows, connect theory with experiment, and contribute to the creation of intelligent, self-improving discovery systems. As a Founding Member of our Technical Staff, you will have substantial control over the direction of the company.

  What You'll Do

  • Design and execute first-principles, atomistic, thermodynamic, mesoscale, and continuum simulations across a diverse set of materials and environments.
  • Develop and deploy automated, high-throughput workflows that integrate seamlessly with AI and experimental data streams.
  • Build and validate interatomic and coarse-grained models (e.g., MLIP, ReaxFF, EAM, DP) linking structure and property across scales.
  • Compute, for example, electronic, mechanical, optical, and thermochemical properties using appropriate methods (e.g., DFT, MD, CALPHAD, FEM, KMC).
  • Maintain and optimize HPC workflows for performance, reproducibility, and data provenance.
  • Collaborate across machine learning, simulation, and experimental teams to prioritize impactful materials challenges.

  Qualifications

  • Ph.D. (or M.S. + experience) in Materials Science, Physics, Chemistry, Chemical Engineering, or a related field.
  • Depth in at least one of the following, and/or familiarity with several, of the following areas:
    • Electronic structure theory (e.g., DFT, DMFT GPAW, CP2K).
    • Atomistic simulations (e.g., MD, MC, KMC; LAMMPS).
    • Thermodynamics and phase modeling (e.g., CALPHAD, DICTRA, Thermo-Calc).
    • Mesoscale & continuum modeling (e.g., phase-field, MOOSE, COMSOL, Abaqus).
    • Electrochemical & transport modeling (e.g., continuum solvers, Poisson–Nernst–Planck frameworks).
    • Mechanical and deformation modeling (e.g., FEM, dislocation dynamics).
  • Strong Python skills for scripting and analysis (NumPy, pandas, ASE, pymatgen, etc.)
  • Experience with workflow automation and HPC environments.
  • Clear communication, collaborative approach, and a bias for experimentation.

  Why Join Us

  • Collaborate with world-class researchers across materials science and AI.
  • Shape how autonomous simulation and experiment interact to accelerate discovery.
  • Top of range compensation and very generous equity, full benefits, and flexible work arrangements.

  How to Apply

  Email us at [email protected]

• Research Scientist, Materials (Experimental) →

  London · San Francisco · Remote

  What We're Looking For

  We are seeking Founding Experimental Materials Scientists to design, synthesize, and characterize functional materials with tunable electronic, thermal, and structural properties. You'll develop reproducible, data-rich experimental workflows that integrate tightly with computational and AI-driven discovery pipelines—helping shape the future of autonomous materials research.

  What You'll Do

  • Design and conduct experiments exploring composition–structure–property relationships in inorganic and hybrid functional materials.
  • Employ diverse synthesis techniques, such as solid-state and solution processing, thin-film deposition (sputtering, evaporation, PLD, ALD), melt or powder routes.
  • Perform advanced characterization such as XRD, SEM/TEM, EDS/WDS, XPS, Raman/IR, DSC/TGA, optical or related techniques.
  • Measure and analyze electrical, thermal, magnetic, or mechanical properties under controlled conditions; interpret data to identify key performance drivers.
  • Develop and maintain automated, reproducible experimental workflows integrated with laboratory data systems and AI-driven analysis.
  • Collaborate closely with computational and AI teams to design experiments that validate models and guide iterative discovery cycles.
  • Contribute to the development of autonomous instrumentation and data standards.

  Qualifications

  • Ph.D. (or M.S. + significant experience) in Materials Science, Physics, Chemistry, or a related field.
  • Proven hands-on experience in materials synthesis and processing.
  • Expertise in property measurement and analysis for functional materials (e.g., electrical or thermal transport, magnetometry, dielectric response, or mechanical testing).
  • Strong grasp of structure–property relationships, phase equilibria, and thermodynamics in complex materials systems.
  • Excellent communication, documentation, and cross-disciplinary collaboration skills.

  Why Join Us

  • Collaborate with world-class researchers across materials science and AI.
  • Shape how autonomous simulation and experiment interact to accelerate discovery.
  • Top of range compensation and very generous equity, full benefits, and flexible work arrangements.

  How to Apply

  Email us at [email protected]

• Research Scientist/Engineer, LLMs →

  London · San Francisco · Remote

  What We're Looking For

  We are seeking Founding Research Scientists/Engineers in Large Language Models to advance the frontier of model adaptation, reasoning, and scientific understanding. You will design and implement methods that bridge large-scale model training with real-world reasoning — exploring mid-training adaptation, context optimization, and inference-time cognition. Your work will directly shape how our models acquire, refine, and apply scientific knowledge across discovery workflows.

  What You'll Do

  • Research and develop mid-training methods that enhance generalization, specialization, and controllability (e.g., domain adaptation, curriculum shaping, continued pre-training).
  • Advance post-training and alignment approaches such as SFT, DPO, RLHF/RLAIF, or verifier-guided optimization to improve reasoning fidelity and factual accuracy.
  • Build new reasoning paradigms — test-time compute, in-context learning, self-reflection, retrieval-augmented and tool-augmented reasoning, and structured prompting.
  • Optimize inference-time behavior for scientific workflows, integrating symbolic, numerical, and multimodal data sources.
  • Design robust evaluations for reasoning, planning, and consistency in applied scientific contexts.
  • Collaborate with scientists, engineers, and model platform teams to translate methods into scalable research infrastructure.

  Qualifications

  • Ph.D. or equivalent experience in Computer Science, Machine Learning, or related fields.
  • Demonstrated experience working with LLM training pipelines (pre-training, mid-training, or post-training).
  • Deep understanding of transformer models, optimization, and representation learning.
  • Proficiency in Python and major ML frameworks (PyTorch, DeepSpeed, Jax, etc.).
  • Experience with alignment and reasoning methods such as in-context learning, chain-of-thought, tool use, or retrieval-augmented reasoning.
  • Ability to balance research insight with engineering pragmatism to deliver scalable, high-performance systems.

  Why Join Us

  • Collaborate with world-class researchers across materials science and AI.
  • Shape how autonomous simulation and experiment interact to accelerate discovery.
  • Top of range compensation and very generous equity, full benefits, and flexible work arrangements.

  How to Apply

  Email us at [email protected]

• Research Scientist/Engineer, ML →

  London · San Francisco · Remote

  What We're Looking For

  We are seeking Founding Research Scientists/Engineers ideally with strong machine learning experience to help build the infrastructure, tools, and prototypes that power the AI Scientist. You'll work across research and engineering—turning new ideas in modeling, reasoning, and experiment automation into robust, scalable systems. This is a hands-on role at the intersection of ML research, software engineering, and scientific computing, ideal for someone who thrives in deeply technical, exploratory environments.

  What You'll Do

  • Build and maintain ML training and inference pipelines, including distributed training, data preprocessing, and model deployment workflows.
  • Develop research tooling and infrastructure that accelerates iteration across simulation, experiment, and AI-driven reasoning.
  • Implement and evaluate new ML algorithms, working closely with scientists and research teams to prototype and test ideas.
  • Optimize performance, reliability, and reproducibility across our computational stack (HPC clusters, cloud systems, experiment interfaces).
  • Contribute to data management, curation, and evaluation frameworks for both structured and unstructured scientific data.
  • Collaborate with model researchers, materials scientists, and robotics engineers to build integrated, intelligent discovery systems.

  Qualifications

  • Bachelor's, Master's, or equivalent experience in Computer Science, Engineering, Applied Mathematics, or related technical field.
  • Strong programming skills in Python and familiarity with PyTorch or equivalent ML frameworks.
  • Experience with modern ML development (training, evaluation, inference, distributed systems).
  • Solid understanding of data engineering and software design principles.
  • Comfortable working in Linux-based environments, version control (Git), and HPC or cloud platforms.
  • Strong communication skills and the ability to collaborate across disciplines.

  Why Join Us

  • Collaborate with world-class researchers across materials science and AI.
  • Shape how autonomous simulation and experiment interact to accelerate discovery.
  • Top of range compensation and very generous equity, full benefits, and flexible work arrangements.

  How to Apply

  Email us at [email protected]