Computational Scientist - AI & Materials
Quantum Formatics · Cambridge
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Jul 12, 2026
Cambridge
Jul 20, 2026
What this job asks for AI summary
A computational scientist role focused on building and improving an AI-driven superconductor discovery pipeline. Core work involves developing and training graph neural network models for materials property prediction, integrating machine learning interatomic potentials, and creating benchmarks validated against experimental data. The role suits a researcher with a background in computational materials science or AI for materials discovery who is comfortable working across the full loop from simulation to experimental feedback.
Mid level · 3+ years · Boston-Cambridge-Newton, MA-NH · Full-time
“or” means any one of them counts — you don't need all of them.
We read this from the posting text with AI. Skim the description below before ruling yourself out.
How this req sits in the market our data
Most people in this occupation already list these. Still required — just not what gets you shortlisted.
What the occupation pays Median $134,944 (middle half $104,979–$169,773).
Estimated from BLS employment for this occupation and area, per-skill prevalence across our listing corpus, and published wage benchmarks — as of Jul 28, 2026. It is a model, not a headcount.
Why we read it this way (8)
The Ph.D. requirement is framed as 'strongly preferred' and the posting explicitly allows 'relevant research experience' as an alternative, so it is not treated as a hard degree gate.
The 3-year experience floor is stated under the 'About you (strongly preferred)' heading, which is itself framed as preferred rather than required; however, it is the only experience signal in the posting and is treated as the role-level minimum for sizing purposes.
SOC classification is Medium confidence: the role is primarily scientific modeling and ML research (15-2051 Data Scientists), but the emphasis on implementing and training GNN models and developing software pipelines also has a strong 15-1252 Software Developers character.
Compensation is described only as 'competitive salary based on experience level and cost-of-living in Cambridge, MA' — no figures are given.
The role is hybrid (on-site at The Engine, Kendall Square, Cambridge, MA), not fully remote.
Density functional theory and molecular dynamics are listed under the 'strongly preferred' block as 'familiarity with', but they sit in the primary qualifications section alongside hard-gated skills; treated as required given their centrality to the role's stated day-to-day work.
Generative model architectures (flow matching, diffusion models) are listed under 'Pluses' and are preferred; the primary named option is diffusion models with flow matching as an alternative.
Fortran and C++ appear together under 'Pluses' as interchangeable options and are preferred.
Read the full posting
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