Computational Polymer Scientist – Materials Discovery


Principal Responsibilities

Cambium works at the interface of biotech and materials science to build next-generation defense products. We integrate computational chemistry, product design, and advanced manufacturing methods to solve difficult challenges like hardening UAVs against laser attack, protecting payloads during hypersonic flight, and streamlining the defense product manufacturing supply chain. Our people come from places like SpaceX, Anduril, Scaled Composites, Lockheed Martin Skunkworks, and US DoD Research Labs. We are seeking motivated and exceptional people to join our world-class team of aerospace innovators and biotech leaders.

Cambium is seeking a Computational Polymer Scientist to join our growing team in El Segundo, CA. The successful candidate will have a strategic mindset, a strong desire to help grow a company, and will help us build a work culture of open-mindedness that embraces creativity and contrarian thinking. At Cambium we’re willing to do things differently.

Cambium is seeking a collaboration-oriented, results-driven Computational Polymer Scientist to drive our materials discovery activities. Key responsibilities of this role will include developing and using machine learning models to discover compounds, leading molecular simulation efforts, and working in close concert with our chemists, materials scientists, manufacturing team, and business development experts. As Cambium experiences rapid growth, the responsibilities for this role are expected to evolve. This is an on-site position with some flexibility.

Key Responsibilities

  • Develop and use machine learning models to discover new compounds for various applications
  • Lead and carry out physics-based simulation activities, especially molecular dynamics simulations
  • Develop and expand our codebase for performing machine learning related activities
  • Maintain awareness of and engage with cutting edge open source machine learning tools, especially
  • those related to chemistry and materials science
  • Communicate and collaborate with teams to craft and execute research plans
  • Communicate clearly and concisely key concepts, research status, performance outcomes, and
  • lessons learned to senior leadership, investors, and collaborators
  • Contribute to the broader engineering team in a manner consistent with Cambium’s mission and
  • values and the growth of Cambium’s business

Key Requirements

  • US citizenship is required
  • S. or Ph.D. in chemistry, materials science, cheminformatics, or related discipline with 3-8 years relevant experience
  • Expertise in machine learning regression methods
  • Strong knowledge of cheminformatics tools, for example, Schrodinger, RDKit, CDK, MOE, PubChem API
  • Knowledge of cutting edge machine learning tools related to chemistry and materials science, especially open source tools, and the major players in that space
  • Expertise in molecular dynamics simulations on organic compounds or polymers
  • Strong Python coding skills; ability to write code that others can understand
  • Strong understanding of polymer networks, especially aerospace resins, is desired but not required
  • Ability to handle simultaneous projects and rapidly changing priorities
  • Ability to communicate effectively with scientists, engineers, and business development experts

Preferred Qualifications


Pay range: $125,000 – $160,000 per year

Your actual level and base salary will be determined on a case-by-case basis and may vary based on the following considerations: job-related knowledge and skills, education, and experience.

Cambium offers a highly competitive compensation and benefits plan. This includes excellent medical/dental/vision benefits, and a matching 401(k) plan.

Let us hear from you

If you are interested in joining our team, please submit your resume and brief pitch to:

Cambium is an Affirmative Action and Equal Opportunity Employer. At Cambium, all qualified applicants will be evaluated for employment without regard to race, color, religion, gender, sexual orientation, national origin, genetic information, age, disability, veteran status, or any other legally protected basis.


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