Job Opportunities in the Molecular Sciences

LOCATION – Hybrid

OFFICE LOCATION – New York, NY, USA

HOW TO APPLY

Machine learning researchers and engineers with impressive records of academic and professional achievements sought to join our interdisciplinary team in New York City.

This is a unique opportunity to collaborate with our chemists, biologists, and computer scientists to expand the group’s efforts applying machine learning to drug discovery, biomolecular simulation, and biophysics. Areas of focus include generative models to help identify novel molecules for drug discovery targets, predict PK and ADME properties of small molecules, develop more accurate approaches for molecular simulations, and understand disease mechanisms. Ideal candidates will have strong Python programming skills. Relevant areas of experience might include deep learning techniques, systems software, high performance computation, numerical algorithms, data science, cheminformatics, medicinal chemistry, structural biology, molecular physics, and/or quantum chemistry, but specific knowledge of any of these areas is less critical than intellectual curiosity, versatility, and a track record of achievement and innovation in the field of machine learning.

D. E. Shaw Research (DESRES) develops and uses advanced computational technologies to understand the behavior of biologically and pharmaceutically significant molecules at an atomic level of detail, and to design precisely targeted, highly selective drugs for the treatment of various diseases. Among its core technologies is Anton, a proprietary special-purpose supercomputer that DESRES designed and constructed to vastly accelerate the process of molecular dynamics simulation. DESRES uses Anton machines and high-speed commodity hardware, together with machine learning methods and other computational techniques, in both internal and collaborative drug discovery programs. For more information, visit http://www.deshawresearch.com/.

We are looking to add innovative contributors who share our commitment to fostering a stimulating, positive, and collaborative work environment.

To submit an application, please use the link provided below:
https://apply.deshawresearch.com/careers/Register?pipelineId=597&source=Molecular+Sciences+Software+Institute

The expected annual base salary for this position is $300,000–$800,000. Our compensation package also includes variable compensation in the form of sign-on and year-end bonuses, and generous benefits, including relocation and immigration assistance. The applicable annual base salary paid to a successful applicant will be determined based on multiple factors including the nature and extent of prior experience and educational background. We follow a hybrid work schedule, in which employees work from the office on Tuesday through Thursday, and have the option of working from home on Monday and Friday.

D. E. Shaw Research, LLC is an equal opportunity employer.

SALARY – Min: $300,000 | Max: $800,000

EMAIL – careers@DEShawResearch.com

A faculty position has opened at CCQ for an individual with strong interests and expertise in AFQMC software and the development of QMC methodologies.

One of the unique features of the Flatiron Institute is their faculty focus on software development. These positions have the same employment status as faculty positions more focused on research, with some flexibility in defining how much software vs research in their efforts, and correspondingly adjustment in their assessment/evaluation for promotion etc.

The successful candidate is expected to devote a significant amount of time to the open-source AFQMC software libraries that they’re developing. Additionally, they will have the option and many opportunities for research in method development and applications in many areas of quantum physics and chemistry. They will be embedded in the vibrant and collaborative research environment at CCQ and the Flatiron Institute more broadly. This is a wonderful career opportunity for candidates with the appropriate interests and backgrounds.

📩 Interested? Link HERE for additional information!

CHIRALNANOMAT is a multi-disciplinary DN project between 7 laboratories and 6 companies in 8 European countries. The network merges physics, chemistry, and biosciences into a unique combination of advanced synthesis and characterization of chiral metal nanoclusters, as well as their applications in catalysis and biosensing. CHIRALNANOMAT’s innovative approach overcomes the current state-of-the-art through: (A) production of chiral atomically precise metal nanoclusters and their assemblies, (B) extensive characterization of their chiral properties, structure and optical properties; both in solution phase and on solid supports, (C) catalytic and biomedical applications followed by the prototyping of the most promising enantioselective catalysts and bioprobes, and (D) development of new computational tools to study chiroptical, catalytic properties and interface of nanomaterials and biomolecules and integration of computational and experimental studies to understand their functionalities.

CHIRALNANOMAT will train 13 Doctoral Candidates who will become experts in chiral nanomaterials and will be able to answer to the future needs of academic and industrial sectors in Europe. By acquiring skills in chemical synthesis, spectroscopic methods, nonlinear surface optics, surface science imaging and scanning probes, bio-functionalization, bio-imaging, and computational electronic structure and molecular dynamics methods as well as machine learning for modeling, and being exposed to an intersectoral environment (through secondments and network-wide training) they will be competitive in future job markets in critical areas of nanotechnology having impact, among others, on synthesis for production of drugs and fine chemicals and novel biotechnology enabled personalized nanomedicine.

We’re looking for 13 talented and motivated PhD candidates to join an interdisciplinary research program at the crossroads of chemistry, physics, materials science, and computation.

🗓️ PhD recruitment: November 2025
📌 informal contact encouraged now!
📅 PhD starts : April 2026

Who can apply?
✅ Master’s degree by late 2025 / early 2026
✅ Not already holding a PhD
✅ Compliant with Marie Curie mobility rule: must not have resided or worked in the host country for more than 12 months in the past 3 years

📍 Host institutions include:
🇮🇹 CNR, Pisa Alessandro Fortunelli
🇵🇱 Wrocław University of Science and Technology, Joanna Olesiak-Banska, NONAgroup
🇦🇹 TU Wien, Noelia Barrabés Rabanal, ClusCAT Lab nanocluster catalysis research group
🇧🇪 KU Leuven, Ewald Janssens, Thierry Verbiest
🇮🇹 University of Trieste, CNR Pisa, Mauro Stener, Alessandro Fortunelli
🇫🇷 CNRS Lyon, Rodolphe Antoine
🇳🇱 SCM Amsterdam, Sergio Lopez Lopez
🇨🇭 University of Geneva, Thomas Bürgi

📩 Interested? Check out their LinkedIn page for additional information!

There are two openings for PhD students in Generative AI and Machine Learning for Materials Science, formally based at the Department of Computer and Information Science, Linköping, Sweden. Please see their website for full details: https://liu.se/en/work-at-liu/vacancies/26718

Application deadline: May 26, 2025

Contact persons: Fredrik Lindsten, Professor in Machine Learning, fredrik.lindsten@liu.se and Fredrik Heintz, Professor in AI, fredrik.heintz@liu.se

OpenADMET – Research Software Engineer

The Open Molecular Software Foundation (OMSF) is seeking a Research Software Engineer for the OpenADMET project (OpenADMET.org). This role is intended for a skilled computer programmer and debugger with a strong grounding in computer science. Experience with small molecules, drug discovery, or ADMET (Absorption, Distribution, Metabolism, Excretion, and Toxicity) is strongly preferred. The Research Software Engineer will work closely with both technical and scientific teams to build and optimize software tools that accelerate open-source ADMET model development and deployment. This fully remote, grant-funded position has a minimum duration of 2 years, with the potential for extension based on funding availability.

The Research Software Engineer will collaborate with the distributed OMSF and OpenADMET teams, primarily based in Europe, Australia, and the United States, and may require occasional travel. This role will report directly to the tech or science lead.

About OpenADMET

OpenADMET (OpenADMET.org) is a new collaborative effort between the Open Molecular Software Foundation (OMSF), UCSF, Octant, and MSKCC to generate high-quality open ADMET datasets and open source models to accelerate small molecule drug discovery. Given that >90% of molecules synthesized in a small molecule discovery program fail to meet ADMET objectives, OpenADMET aims to produce open source models capable of greatly reducing time, cost, and failure rates in drug discovery and development programs due to ADMET issues. In addition to curating public ADMET data in ML-ready formats, OpenADMET will generate large targeted open experimental datasets (Octant) and X-ray/cryo-EM structural data (UCSF) that will power open source models (OMSF) and blind community challenges seeking to advance the state of the art. OpenADMET is funded by a variety of sources, including ARPA-H and the Gates Foundation.

Key Duties and Responsibilities

Software Development and Debugging

• Write clean, efficient, and well-documented code to support the development of ADMET modeling pipelines.
• Troubleshoot and debug existing codebases used for molecular data processing and machine learning applications.
• Develop new features or modules that integrate external libraries and computational tools to advance the utility and performance of ADMET workflows.

Computational Infrastructure and Tooling

• Collaborate with the ML Infrastructure and Scientific teams to ensure software tooling aligns with scientific goals and best coding practices.
• Contribute to continuous integration (CI) and deployment (CD) pipelines for automating software testing, building, and release processes.
• Assist in configuring and optimizing cloud-based or local HPC systems for large-scale computational tasks, including parallelization and job scheduling.

Data Handling and Integration

• Work with Data Curation teams to refine data ingestion and standardization workflows, ensuring high-quality data for downstream modeling.
• Implement and maintain data transformation pipelines that enable efficient training and inference for ADMET models.
• Aid in identifying and resolving data inconsistencies or integration challenges to preserve data integrity throughout the pipeline.

Scientific Collaboration

• Interact with scientists and computational researchers to translate research objectives into robust software solutions.
• Provide technical guidance on using computational frameworks and libraries (e.g., RDKit, Pytorch, DeepChem, or similar) for small molecule analysis.
• Maintain close communication with cross-functional teams, including the Tech Lead for ML Infrastructure, to align development efforts with project goals.

Documentation and Open-Source Contributions

• Produce clear, concise documentation for developed software tools, libraries, and pipelines.
• Follow open-source best practices when contributing to repositories (version control, code reviews, issue tracking).
• Collaborate with the community to address software issues, requests, and enhancements, fostering a transparent and collaborative environment.

Qualifications:

• Advanced degree in Computer Science, Software Engineering, Computational Chemistry, or a related field; equivalent practical experience also considered.
• Demonstrated experience in software development (Python strongly preferred) and debugging in a research or scientific context.
• Familiarity with best practices in version control (Git), CI/CD, containerization (e.g., Docker), and automated testing.
• Knowledge of fundamental concepts in small molecule drug discovery or computational chemistry (e.g., molecular descriptors, ADMET principles) is strongly preferred.
• Ability to troubleshoot complex workflows in distributed or cloud-based computing environments.
• Excellent communication and problem-solving skills, with an ability to collaborate effectively in a remote, interdisciplinary environment.

Preferred Qualifications

• Experience with molecular data processing and modeling libraries (e.g., RDKit, DeepChem).
• Familiarity with machine learning frameworks (e.g., PyTorch, TensorFlow) and scientific computing libraries (NumPy, SciPy, etc.).
• Background in ADMET data or drug discovery pipelines.
• Knowledge of FAIR data principles and open-source scientific software development.
• Experience contributing to or maintaining open-source repositories.

Compensation

This is a full-time, fixed contract position with an expected gross salary range of $80,000-$100,000, depending on qualifications, experience, and location. OMSF offers standard benefits, including healthcare, retirement contributions, paid time off, and other employer contributions per local regulations. Salary will be negotiated in the local currency and may vary by location due to differences in mandatory employer contributions.

Location

OMSF is a fully remote organization. For this role, we will accept only applications from candidates who are based in the US due to federal funding restrictions.

Application Process

For more information about this position and OMSF, please visit their website

Please apply using this form.

Any additional queries about the role and OMSF can be sent to careers@omsf.io.

Nurix Therapeutics is a clinical stage biopharmaceutical company focused on the discovery, development, and commercialization of targeted protein degradation medicines, the next frontier in innovative drug design aimed at improving treatment options for patients with cancer and inflammatory diseases. Powered by a fully AI-integrated discovery engine capable of tackling any protein class, and coupled with unparalleled ligase expertise, Nurix’s dedicated team has built a formidable advantage in translating the science of targeted protein degradation into clinical advancements. Nurix aims to establish degrader-based treatments at the forefront of patient care, writing medicine’s next chapter with a new script to outmatch disease.

Position

Nurix is seeking a talented, inquisitive, and passionate Machine Learning Scientist to join our cutting-edge research team and revolutionize the development of Targeted Protein Degradation (TPD) therapeutics. The successful candidate will play a pivotal role in building and applying our robust ML Platform to accelerate drug discovery. This involves collaborating closely with interdisciplinary project teams, identifying critical research challenges amenable to machine learning solutions, developing high-quality predictive models, and deploying these models to streamline the drug design process. You will leverage our extensive DNA Encoded Library (DEL) and Degrader datasets to drive impactful decisions at every stage of the drug development pipeline

Responsibilities

• Develop and deploy high-performing machine learning models to address critical research needs within the TPD drug discovery pipeline.
• Apply Nurix’s DEL ML and DEL Foundation platforms to guide Lead Identification and Lead Optimization efforts, extracting valuable insights from our experimental DEL platform.
• Design and conduct rigorous model validation and performance assessments to ensure the reliability and robustness of ML predictions.
• Collaborate effectively with cross-functional research teams (e.g., chemists, biologists, engineers) to translate research questions into actionable ML problems.
• Advocate for the adoption of machine learning solutions within the research organization, effectively communicating the value and impact of ML-driven insights.
• Maintain and enhance existing machine learning codebases, ensuring their scalability, maintainability, and performance.
• Stay abreast of the latest advancements in machine learning research and technologies, identifying opportunities to incorporate novel methods into our drug discovery efforts
• Desired Qualifications
• Recent PhD or strong experience (MS + 1-3 years or BS + 3-5 years) in Machine Learning, Bioinformatics, Computational Biology, Computer Science, Statistics, or a related field.
• Demonstrated Python proficiency and experience with modern frameworks for Machine Learning: Proven experience implementing projects with supervised and unsupervised learning algorithms using modern python packages (e.g., linear regression, logistic regression, support vector machines, random forests, clustering, deep learning, scikit-learn, PyTorch, Tensorflow).
• Data analysis and visualization: Experience with data cleaning, preprocessing, exploratory data analysis, and visualizing results (e.g., pandas, NumPy, matplotlib, seaborn)
• Familiarity with one or more cheminformatic or computational toolkits (RDKit, OpenBabel, OpenEye, Schrodinger, etc)
• Experience working in Linux/Unix environments including basic shell scripting and bash commands.
• Experience with cloud platforms (e.g., AWS, Azure), version control systems (e.g., Git), CI/CD pipelines and workload managers (e.g., Slurm)
• Experience designing experiments with scientific data: Prior research involving chemical, biological, or DNA Encoded Library datasets
• Strong communication and interpersonal skills

For more information and applying for this job, please visit the LinkedIn page