{"id":2344851,"url":"https://alion.io/job/google-deepmind-materials-laboratory-scientist-science","title":"Materials Laboratory Scientist, Science","company":{"id":4,"name":"Google DeepMind","domain":"deepmind.google","url":"https://alion.io/company/deepmind","size_band":"1001-5000","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Career site","truth_index":null},"role":"Science & Research","role_family":"Science & Research","seniority":"junior","employment_type":null,"work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["London, United Kingdom"],"countries":["GB"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":49000,"max_usd":124000,"period":"year","method":"global_role_cell_scaled_by_country","sample_n":884},"experience_years_min":2,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Machine Learning","optional":false},{"name":"Python","optional":true}],"status":"live","first_seen_at":"2026-10-12T08:40:30Z","employer_posted_date":"2026-10-12","last_verified_at":"2026-10-13T11:38:24Z","board_verified":true,"closed_at":null,"days_open":1,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":1},"description":"About the job\nWe are seeking an experimental Materials Laboratory Scientist to join our team in London. In this role, you will work on experimental realization, processing, and characterization of novel materials predicted by our generative AI models. You will drive experimental workflows across laboratory synthesis and characterization, bridging traditional physical sciences with data-driven research. Working side-by-side with AI researchers, software engineers, and automation roboticists, you will help scale laboratory throughput, build automated feedback loops, and accelerate materials discovery.\nArtificial intelligence will be one of humanity’s most transformative inventions. At Google DeepMind, we are a pioneering AI lab with exceptional interdisciplinary teams focused on advancing AI development to solve complex global challenges and accelerate high-quality product innovation for billions of users. We use our technologies for widespread public benefit and scientific discovery, ensuring safety and ethics are always our highest priority.\nWe are pushing the boundaries across multiple domains. Our global teams offer diverse learning opportunities and varied career pathways for those driven to achieve exceptional results through collective effort.\nResponsibilities\nConduct structural, compositional, and physical property characterization across a variety of metrology platforms to validate synthesized materials.\nProvide systematic experimental feedback and guide active learning pipelines in close collaboration with computational and machine learning researchers.\nTranslate benchtop protocols into high-throughput automated workflows in partnership with automation engineers.\nExecute experimental synthesis and processing workflows across inorganic, solid-state, thin-film, or other chemical routes to realize target materials candidates.\nMaintain thorough data hygiene, capturing comprehensive experimental telemetry and protocols within digital laboratory systems (ELN/LIMS) to support machine learning datasets.\nQualifications\nMinimum qualifications:\nPhD in Materials Science, Physics, Chemistry, Chemical Engineering, or a related science discipline, or equivalent practical. \n2 years of experience with laboratory synthesis, preparation, or characterization of materials.\nExperience working in cross-functional or interdisciplinary technical/scientific teams (e.g., across engineering, computing, materials science, or chemistry).\nExperience troubleshooting, root-cause diagnosing, and resolving technical or experimental bottlenecks in a scientific research or laboratory environment.\nSubmission of scientific publications, conference proceedings, patents, or technical white papers in Materials Science, Physics, or a related discipline.\nPreferred qualifications:\nExperience working with high-throughput experimental platforms, laboratory automation, or robotic instrumentation.\nProficiency in programming (e.g., Python) for scientific data analysis, automation scripting, or instrument interfacing.\nFoundational understanding of computational materials science or 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