Computational Scientist

About Tamarind Bio

We enable any scientist to access AI-powered drug discovery. Thousands of scientists from large pharma companies, top biotechs, and academic institutions use Tamarind to design protein drugs, improve industrial enzymes, and create cutting edge molecules that weren’t feasible until now.

New AI models are quickly eclipsing physics-based tools in computational drug discovery. Scientists often struggle to fine-tune, deploy, and scale these models, leaving breakthroughs on the table. Tamarind provides a simple interface to the vast array of tools being released daily.

About the Role

We’re hiring a Computational Scientist to help curate, build, and scale Tamarind’s library of AI-powered drug discovery tools.

In this role, you’ll work closely with the founders and engineering team to operationalize cutting-edge models for structure prediction, protein design, docking, scoring, and other core biological AI workloads. You’ll help transform fragmented research tools into production-ready workflows that scientists can run reliably at scale.

You’ll collaborate directly with customers to understand their discovery challenges and help them leverage Tamarind’s platform to run real biological AI pipelines. This often involves chaining multiple tools together, troubleshooting workflows, and identifying opportunities to improve the platform.

This role sits at the intersection of computational biology, machine learning, and scientific infrastructure, and is ideal for someone excited about applying the latest advances in AI to real-world drug discovery programs.

Our techstack:

Python, PyTorch, TensorFlow, CUDA, Conda, Docker, AWS (EC2, S3, DynamoDB), molecular modeling tools, protein design frameworks, structural biology tooling, APIs and workflow orchestration.

Week in the Life:

Work with founders and engineers to integrate and deploy biological ML models on the Tamarind platform.
Build and refine workflows connecting tools like structure prediction, docking, and scoring models.
Partner with customers to troubleshoot pipelines and help them run large-scale discovery workflows.
Evaluate new research tools and integrate promising models into the platform
Contribute to improving reliability, performance, and scalability of scientific pipelines

Qualification requirements:

Strong background in computational biology, computational chemistry, bioinformatics, or related field
Familiarity with ML and physics-based tools in structural biology, molecular dynamics, protein–ligand docking, or virtual screening
Experience working with biological data such as molecular structures, compounds, sequences, and databases
Programming experience in Python and scientific computing workflows
Comfort working with cloud infrastructure and ML tooling (AWS, Docker, CUDA, Conda, PyTorch, TensorFlow)
Located in the SF Bay Area or able to relocate

Computational Research Scientist (Two-Dimensional Materials and Low-Pressure Processing Plasma)

Princeton Plasma Physics Laboratory (PPPL)

The Princeton Plasma Physics Laboratory (PPPL) is seeking to appoint a Computational Scientist to contribute to the advancement of modeling capabilities and physics research pertaining to using low-temperature plasmas for processing of two-dimensional materials and associated technologies. The primary responsibility of this position involves conducting and facilitating computational modeling of processing of 2D materials by low-temperature plasma for the purposes of scientific discovery and engineering design of these processes. The successful candidate will achieve this objective through the application of density functional theory (DFT) modeling of these processes using ab initio molecular dynamics (MD), and the utilization of machine learning potentials for MD acceleration.

The candidate should have strong practical familiarity with MD simulations and band gap structure calculations of 2D material properties and connection to experimental measurements. Furthermore, the candidate should demonstrate substantial practical knowledge of 2D material properties and techniques employed in plasma processing, coupled with a proven track record of modeling of such discharges. The results will be disseminated to the broader academic and industrial communities, necessitating strong interpersonal and communication skills to cultivate these relationships. Finally, this role will encompass the conceptualization and preparation of novel proposal ideas to secure funding for future research projects.

A U.S. Department of Energy National Laboratory managed by Princeton University, the Princeton Plasma Physics Laboratory (PPPL) is tackling the world’s toughest science and technology challenges using plasma, the fourth state of matter. With more than 70 years of history, PPPL is a leader in the science and engineering behind the development of fusion energy, a potentially limitless energy source. PPPL is also using its expertise to advance research in the areas of microelectronics, quantum sensors and devices, and sustainability sciences. Whether it be through science, engineering, technology or professional services, every team member has an opportunity to contribute to our mission and vision. Come join us!

Responsibilities

Application of ab initio molecular dynamics to low-temperature plasmas for processing of two-dimensional materials and associated technologies. The capabilities will be deployed for optimization of processing techniques of these materials. The candidate will be responsible for contributing to ongoing project on processing of two-dimensional materials based on transit metal dichalcogenides (50%). Defining and delivering on A.I. projects for low-temperature plasmas (20%). Proposal ideation and preparation (10%). Modeling plasma processing for industry partners (20%). Publishing scientific results and dissemination at major international conferences (10%).

Qualifications

Ph.D. in Physics, Engineering or a related field with core training in low-temperature plasma physics and high-performance computing.
Minimum 3 years of professional experience in an academic, scientific, or R&D environment.
A proven track record of publishing original results in peer-reviewed scientific journals.
Demonstrated collaborative experience within academia and with industry.

Knowledge, Skills, And Abilities

Extensive practical experience using first-principles methods to study materials relevant to microelectronics, quantum devices, and plasma assisted processing, including ab initio molecular dynamics and classical molecular dynamics simulations. Strong ability to connect atomistic simulation results to experimentally relevant materials properties and processing outcomes, including defect formation, surface functionalization, selective etching, plasma-induced damage, optoelectronic properties, surface cleaning, and modification of 2D materials
Practical knowledge of low-temperature plasma processing of materials, including plasma-assisted etching, ion/surface interactions, fluorination, oxygen and hydrogen surface chemistry, plasma activated desorption, and highly selective or self-limiting processes relevant to nanofabrication of microelectronics and quantum-device materials. Ability to develop computational workflows that support optimization of plasma processing conditions and interpretation of experimental observations
Advanced experience with major electronic structure, molecular dynamics, and quantum chemistry software packages, including VASP, CP2K, Gaussian, Quantum ESPRESSO, LAMMPS, and related atomistic modeling tools.
Strong record of scientific publication and conference presentation in electronic structure calculations, 2D materials, defects, and plasma-assisted processing of 2D materials. Ability to communicate complex computational results clearly to scientific, engineering, experimental, academic, and industrial audiences.
Demonstrated ability to work collaboratively in multidisciplinary research environments involving theory, computation, experiment, and industry-relevant materials processing applications. Ability to contribute to proposal ideation, preparation of research plans, development of new computational capabilities, and dissemination of results through peer-reviewed publications, talks, and major international conferences.
Experience in Molecular Dynamics Study of Plasma related materials like Liquid Lithium (and Boron) Interaction with H, D, and Impurities.

Computational Scientist

CFD Research Corporation

About CFD Research: Since its inception in 1987, CFD Research has delivered innovative technology solutions within the Aerospace & Defense, Biomedical & Life Sciences, Intelligence & Sensing, and Energy & Materials industries. CFD Research has earned multiple national awards for successful application and commercialization of innovative component/system technology prototypes, multi-physics simulation software, multi-disciplinary analyses, and expert support services. Based in Huntsville, Alabama where laboratory facilities and headquarters are located, CFD Research also has office and laboratory facilities in Dayton, Ohio, prototyping test and evaluation facilities in Hollywood, Alabama, and office facilities in Fort Walton Beach, Florida. CFD Research is an ISO9001:AS9100D registered company and is appraised at CMMI Level II for Services.

CFD Research is a 100% ESOP (employee-owned company) recognized in Inc. Magazine's Inc5000 as a top growing company for four of the last five years. Learn more at www.cfd-research.com

CFD Research is seeking a highly skilled Computational Scientist (Multiphysics Simulation) to join our Biotechnology and Life Sciences team, where they will play a central role in developing and extending our in-house multiphysics simulation platform to solve complex, real-world problems. The successful candidate will combine expertise in numerical methods and mathematical modeling with strong scientific software development skills in C++, implementing, testing, and validating solvers within a large, established codebase. In addition to core software development, the candidate will develop, apply, and support computational models and simulations for a range of biological systems and related applications. This role offers the opportunity to work at the intersection of theory, high-performance computing, and application in a collaborative, fast-paced environment focused on innovation and technical excellence.

Key Responsibilities

Develop, extend, and maintain solvers within our in-house C++ multiphysics simulation platform
Develop, apply, and support computational models and simulations across a range of biological systems and application areas, including high-resolution and multiscale simulations
Formulate and derive mathematical models to simulate complex systems and processes
Implement, analyze, and validate models using appropriate numerical methods and algorithms for solving differential equations, optimization problems, and stochastic systems
Translate real-world problems into computational frameworks and scalable solutions
Collaborate with experimental scientists and engineers to integrate modeling with empirical data
Develop high-performance computing (HPC) solutions and optimize code for efficiency and scalability
Document methodologies, code, and results
Maintain best practices in version control, reproducibility, and software development
Contribute to scientific reports, proposals, and peer-reviewed publications

Qualifications

Required

PhD with 3+ years of relevant experience (or equivalent) in Applied Mathematics, Computational Science, Physics, Engineering, or a closely related field
Demonstrated experience developing original mathematical/computational models and simulations for complex systems
Strong foundation in numerical analysis, linear algebra, differential equations, and/or stochastic modeling, including implementation of numerical solvers (e.g., finite element or finite volume methods)
Experience developing or applying computational models and simulations of physical or biological systems, with the ability to support a range of modeling and simulation applications
Familiarity with data analysis and visualization tools
Strong proficiency in C++ for scientific computing, with the ability to develop, extend, and maintain clean, well-tested code within a large existing codebase, working knowledge of additional languages (e.g., Python, MATLAB, Julia) for analysis and prototyping
Demonstrated intellectual curiosity and a willingness to learn and apply new methods, tools, and scientific domains as project needs evolve
Strong analytical, critical thinking, and problem-solving skills with attention to detail
Excellent written and verbal scientific communication skills, with the ability to convey complex mathematical concepts and collaborate effectively in multidisciplinary teams
Proven ability to work independently, manage multiple projects, and troubleshoot complex technical challenges

Preferred

Experience developing or extending large-scale multiphysics or PDE-based simulation software
Experience developing computational models of biological systems (e.g., in vitro assays, cell-based systems, or other fit-for-purpose preclinical models)
Background in a domain area such as bioinformatics, infectious disease modeling, fluid dynamics, or materials science
Experience integrating machine learning with mechanistic models
Familiarity with version control (e.g., Git) and collaborative software development workflows

Computational Scientist

Tamarind Bio

About Tamarind Bio

About the Role

We’re hiring a Computational Scientist to help curate, build, and scale Tamarind’s library of AI-powered drug discovery tools.

Our techstack:

Python, PyTorch, TensorFlow, CUDA, Conda, Docker, AWS (EC2, S3, DynamoDB), molecular modeling tools, protein design frameworks, structural biology tooling, APIs and workflow orchestration.

Week in the Life:

Work with founders and engineers to integrate and deploy biological ML models on the Tamarind platform.
Build and refine workflows connecting tools like structure prediction, docking, and scoring models.
Partner with customers to troubleshoot pipelines and help them run large-scale discovery workflows.
Evaluate new research tools and integrate promising models into the platform
Contribute to improving reliability, performance, and scalability of scientific pipelines

Qualification requirements:

Strong background in computational biology, computational chemistry, bioinformatics, or related field
Familiarity with ML and physics-based tools in structural biology, molecular dynamics, protein–ligand docking, or virtual screening
Experience working with biological data such as molecular structures, compounds, sequences, and databases
Programming experience in Python and scientific computing workflows
Comfort working with cloud infrastructure and ML tooling (AWS, Docker, CUDA, Conda, PyTorch, TensorFlow)
Located in the SF Bay Area or able to relocate

Computational Research Scientist (Two-Dimensional Materials and Low-Pressure Processing Plasma)

Princeton Plasma Physics Laboratory (PPPL)

Ph.D. in Physics, Engineering or a related field with core training in low-temperature plasma physics and high-performance computing.
Minimum 3 years of professional experience in an academic, scientific, or R&D environment.
A proven track record of publishing original results in peer-reviewed scientific journals.
Demonstrated collaborative experience within academia and with industry.

Knowledge, Skills, And Abilities

Extensive practical experience using first-principles methods to study materials relevant to microelectronics, quantum devices, and plasma assisted processing, including ab initio molecular dynamics and classical molecular dynamics simulations. Strong ability to connect atomistic simulation results to experimentally relevant materials properties and processing outcomes, including defect formation, surface functionalization, selective etching, plasma-induced damage, optoelectronic properties, surface cleaning, and modification of 2D materials
Practical knowledge of low-temperature plasma processing of materials, including plasma-assisted etching, ion/surface interactions, fluorination, oxygen and hydrogen surface chemistry, plasma activated desorption, and highly selective or self-limiting processes relevant to nanofabrication of microelectronics and quantum-device materials. Ability to develop computational workflows that support optimization of plasma processing conditions and interpretation of experimental observations
Advanced experience with major electronic structure, molecular dynamics, and quantum chemistry software packages, including VASP, CP2K, Gaussian, Quantum ESPRESSO, LAMMPS, and related atomistic modeling tools.
Strong record of scientific publication and conference presentation in electronic structure calculations, 2D materials, defects, and plasma-assisted processing of 2D materials. Ability to communicate complex computational results clearly to scientific, engineering, experimental, academic, and industrial audiences.
Demonstrated ability to work collaboratively in multidisciplinary research environments involving theory, computation, experiment, and industry-relevant materials processing applications. Ability to contribute to proposal ideation, preparation of research plans, development of new computational capabilities, and dissemination of results through peer-reviewed publications, talks, and major international conferences.
Experience in Molecular Dynamics Study of Plasma related materials like Liquid Lithium (and Boron) Interaction with H, D, and Impurities.

Computational Scientist

CFD Research Corporation

CFD Research is a 100% ESOP (employee-owned company) recognized in Inc. Magazine's Inc5000 as a top growing company for four of the last five years. Learn more at www.cfd-research.com

Key Responsibilities

Develop, extend, and maintain solvers within our in-house C++ multiphysics simulation platform
Develop, apply, and support computational models and simulations across a range of biological systems and application areas, including high-resolution and multiscale simulations
Formulate and derive mathematical models to simulate complex systems and processes
Implement, analyze, and validate models using appropriate numerical methods and algorithms for solving differential equations, optimization problems, and stochastic systems
Translate real-world problems into computational frameworks and scalable solutions
Collaborate with experimental scientists and engineers to integrate modeling with empirical data
Develop high-performance computing (HPC) solutions and optimize code for efficiency and scalability
Document methodologies, code, and results
Maintain best practices in version control, reproducibility, and software development
Contribute to scientific reports, proposals, and peer-reviewed publications

Qualifications

Required

PhD with 3+ years of relevant experience (or equivalent) in Applied Mathematics, Computational Science, Physics, Engineering, or a closely related field
Demonstrated experience developing original mathematical/computational models and simulations for complex systems
Strong foundation in numerical analysis, linear algebra, differential equations, and/or stochastic modeling, including implementation of numerical solvers (e.g., finite element or finite volume methods)
Experience developing or applying computational models and simulations of physical or biological systems, with the ability to support a range of modeling and simulation applications
Familiarity with data analysis and visualization tools
Strong proficiency in C++ for scientific computing, with the ability to develop, extend, and maintain clean, well-tested code within a large existing codebase, working knowledge of additional languages (e.g., Python, MATLAB, Julia) for analysis and prototyping
Demonstrated intellectual curiosity and a willingness to learn and apply new methods, tools, and scientific domains as project needs evolve
Strong analytical, critical thinking, and problem-solving skills with attention to detail
Excellent written and verbal scientific communication skills, with the ability to convey complex mathematical concepts and collaborate effectively in multidisciplinary teams
Proven ability to work independently, manage multiple projects, and troubleshoot complex technical challenges

Preferred

Experience developing or extending large-scale multiphysics or PDE-based simulation software
Experience developing computational models of biological systems (e.g., in vitro assays, cell-based systems, or other fit-for-purpose preclinical models)
Background in a domain area such as bioinformatics, infectious disease modeling, fluid dynamics, or materials science
Experience integrating machine learning with mechanistic models
Familiarity with version control (e.g., Git) and collaborative software development workflows