The Opportunity:

As a Scientist II in the Cancer Pharmacology team within the Translational Research group, in the Biomedical Discovery Research Department, you will:

• Contribute to advancing RAS(ON) inhibitor programs by applying cancer pharmacology and tumor biology expertise, with a focus on mechanistic understanding of RAS biology.

• Design, execute, and interpret in vivo efficacy and PK/PD studies (internally and via CROs) to support forward and reverse translation of RAS(ON) inhibitors

• Collaborate with the cross-functional quantitative modeling group, providing data, biological context, and critical evaluation to support translational PK/PD modeling (no requirement for hands-on mathematical model experience).

• Analyze, interpret, and communicate data clearly to internal teams and external scientific audiences.

• Maintain high-quality documentation, protocols, and data integrity.

Required Skills, Experience and Education:

• Ph.D. in pharmacology or cancer biology, or a related discipline with direct relevance to oncology drug development.

• 2+ years of relevant postdoc or industry experience in cancer drug discovery and development.

• Solid knowledge of tumor biology and RAS signaling pathways

• Demonstrated experience in designing, executing and analyzing in vivo experiments with oncology disease models.

• Working knowledge of PK and PK/PD relationships, with the ability to analyze and interpret data.

• Rigorous and detail-oriented with proven excellence in experimental design, data analysis, data management, and presentation.

• Excellent written and verbal communication skills. Able to effectively communicate results to non-specialist and specialist audiences.

• Thrives in a collaborative team setting and is driven by a desire to be innovative in a dynamic and fast-paced environment.

Preferred Skills:

• Familiarity with RAS biology and RAS-targeted therapies

• Experience with designing and executing PKPD studies in tumor models.

• An understanding of preclinical to clinical translational concepts desired.

• Strong track record of research productivity as evidenced by high-quality, impactful publications.

• A strong theoretical understanding of the core concepts, assumptions, and limitations associated with PK/PD mathematical modeling.

#LI-Hybrid   #LI-NE1

Quantum computing holds the promise of humanity’s mastery over the natural world, but only if we can build a real quantum computer. PsiQuantum is on a mission to build the first real, useful quantum computers, capable of delivering the world-changing applications that the technology has long promised.  We know that means we will need to build a system with roughly 1 million qubits that supports fault tolerant error correction within a scalable architecture, and a data center footprint.

By harnessing the laws of quantum physics, quantum computers can provide exponential performance increases over today’s most powerful supercomputers, offering the potential for extraordinary advances across a broad range of industries including climate, energy, healthcare, pharmaceuticals, finance, agriculture, transportation, materials design, and many more.

PsiQuantum has determined the fastest path to delivering a useful quantum computer, years earlier than the rest of the industry. Our architecture is based on silicon photonics which gives us the ability to produce our components at Tier-1 semiconductor fabs such as GlobalFoundries where we leverage high-volume semiconductor manufacturing processes, the same processes that are already producing billions of chips for telecom and consumer electronics applications. We also benefit from the quantum mechanics reality that photons don’t feel heat or electromagnetic interference, allowing us to take advantage of existing cryogenic cooling systems and industry standard fiber connectivity.

In 2024, PsiQuantum announced two government-funded projects to support the build-out of our first Quantum Data Centers and utility-scale quantum computers in Brisbane, Australia and Chicago, Illinois. Both projects are backed by nations that understand quantum computing’s potential impact and the need to scale this technology to unlock that potential. And we won’t just be building the hardware, but also the fault tolerant quantum applications that will provide industry-transforming results.

Quantum computing is not just an evolution of the decades-old advancement in compute power. It provides the key to mastering our future, not merely discovering it. The potential is enormous, and we have the plan to make it real. Come join us.

There’s much more work to be done and we are looking for exceptional talent to join us on this extraordinary journey!

Job Summary:

Are you eager to revolutionize life sciences using the transformative power of quantum computing? As a Quantum Life Sciences Chemist, you will lead efforts at the intersection of quantum computing, computational chemistry, and biology, solving critical challenges in drug design, biomolecular modeling, and biological systems simulation. By integrating quantum algorithms with established life sciences methodologies, you will pioneer new approaches to address complex problems in pharmaceuticals, biomaterials, and bioinformatics. Join our interdisciplinary team to advance quantum-enabled innovations and shape the future of life sciences.

At PsiQuantum’s Quantum Solutions team, your role will focus on bridging the gap between fault-tolerant quantum computing (FTQC) and established computational chemistry and life sciences tools. You will integrate these approaches with machine learning to explore novel workflows for quantum-informed drug discovery and biomolecular modeling. Your expertise in theoretical and computational chemistry will enable you to connect quantum-computed molecular insights with complex biological systems, driving advancements in drug design, biomaterials, and other critical areas within the life sciences.

This position requires a PhD in computational chemistry, biophysics, or a closely related field, preferably with postdoctoral research experience (although postdoc experience is not mandatory). We are looking for a curious, creative, and interdisciplinary thinker with a strong foundation in computational methodologies applicable to life sciences. The ideal candidate should be an avid reader of scientific literature, possess expert-level hands-on coding experience (e.g., Python, Fortran, C++), and have demonstrated skills in developing and applying computational techniques. Experience contributing to scientific software or modeling workflows is a plus. While prior knowledge of quantum information and fault-tolerant quantum computing is highly preferred, it is not required.

Responsibilities:

• Conduct innovative research, literature analysis, problem solving, and quantum workflow design in the areas of quantum-informed biomolecular modeling, drug discovery, and computational chemistry.
• Collaborate with quantum algorithm experts to identify areas where quantum computing can have the greatest impact in computational chemistry, biology, and life sciences.
• Contribute expertise in conventional (non-quantum-computing) algorithms to the development of in-house quantum algorithms. Serve as a technical lead in customer projects by collaborating with customers’ teams to integrate quantum computing-produced computational outputs into conventional life sciences workflows.
• Serve as a liaison between partner teams and PsiQuantum’s quantum information experts for innovative algorithm selection, development, and prioritization relevant to life sciences.
• Develop computational workflows that combine best-in-class conventional approaches (e.g., through high-performance computing (HPC)) with the breakthrough computational abilities of FTQC to reshape how quantum workflows are designed for life sciences applications.
• Serve as a subject matter expert in computational quantum chemistry and biomolecular modeling for PsiQuantum’s Quantum Solutions team, staying updated on recent academic literature, trends, and tools in life sciences.
• Foster collaboration across teams to maximize the impact of quantum algorithms and quantum computing-generated data in life sciences applications.
• Help shape external-facing materials that champion the applications of FTQC in the pharmaceutical, biotechnology, and life sciences industries (for key opinion leaders, media, and partners).
• Build and maintain external partnerships and collaborations on the topic, which can include meetings, group problem-solving sessions, or drafting research proposals, among others.
• Create organized internal reports and thoroughly document progress on assigned tasks.

Experience/Qualifications:

Required:

• Ph.D. in computational chemistry, biophysics, bioinformatics, or a closely related field, with a strong focus on computational methodology development, and 0 to 6 years of post-PhD (postdoctoral or industrial) experience.
• Hands-on experience with modeling and simulation of biological systems, such as protein-ligand interactions, enzymatic reactions, or biomolecular dynamics.
• Enthusiasm for working in a collaborative, interdisciplinary, and dynamic team environment.
• Expert-level Python coding skills with experience in scientific libraries relevant to life sciences.
• Proven ability to stay updated on recent academic literature and trends in computational chemistry and biomolecular modeling.

Preferred:

• Strong foundational knowledge of quantum chemistry methods (e.g., DFT, coupled cluster theory, or wavefunction-based approaches) and molecular simulation techniques as applied to biomolecular systems.
• Experience with free energy perturbation (FEP), molecular docking, or similar computational techniques for drug discovery.
• Experience with machine learning (ML) techniques in computational drug discovery, biomolecular modeling, or structural biology.
• Experience with coding GPU-accelerated molecular simulations.
• Familiarity with hybrid quantum-classical methodologies, such as QM/MM, applied to biomolecular systems.
• Knowledge of quantum embedding theories (e.g., DMET, embedded correlated wavefunctions) and their application to large, complex biomolecular systems.
• Hands-on experience with quantum computing or quantum algorithms, especially as applied to biological or chemical systems.
• General understanding of light-matter interactions or electronic excitations relevant to biological or chemical systems, such as energy transfer or photochemical processes.
• Familiarity with advanced quantum mechanical methods (e.g., CASCI, CASSCF) and their relevance to modeling enzymatic reactions or metal-containing biomolecules.
• Demonstrated scientific communication skills through peer-reviewed publications, conference presentations, or collaboration with industry partners.
• Expert-level C++ or Fortran coding skills.

PsiQuantum provides equal employment opportunity for all applicants and employees. PsiQuantum does not unlawfully discriminate on the basis of race, color, religion, sex (including pregnancy, childbirth, or related medical conditions), gender identity, gender expression, national origin, ancestry, citizenship, age, physical or mental disability, military or veteran status, marital status, domestic partner status, sexual orientation, genetic information, or any other basis protected by applicable laws.

Note: PsiQuantum will only reach out to you using an official PsiQuantum email address and will never ask you for bank account information as part of the interview process. Please report any suspicious activity to recruiting@psiquantum.com.

We are not accepting unsolicited resumes from employment agencies.

About this opportunity:

At Freenome, we are seeking a Senior Machine Learning Scientist to help grow the Machine Learning Science team, within the Computational Science department. The ideal candidate has a strong knowledge of artificial intelligence (AI), including machine learning (ML) fundamentals and extensive experience with deep learning (DL) methods, a track record of successfully using these methods to answer complex research questions, and the ability to thrive in a highly cross-functional environment.

They will be responsible for the development of algorithms for early, blood-based detection tests for cancer. They will build on a foundation of ML/DL and statistical skills to develop models for identifying molecular signals from blood. They will also work with computational biologists, molecular biologists and ML engineers to design and drive research experiments, and will have a significant impact on the continued growth of an organization dedicated to changing the entire landscape of cancer.

The role reports to the Director, Machine Learning Science. This role can be a Hybrid role based in our Brisbane, California headquarters (2-3 days per week in office), or remote.

What you’ll do:

• Independently pursue cutting edge research in AI applied to biological problems (including cancer research, genomics, computational biology, immunology, etc.).
• Build new models or fine-tune existing models to identify biological changes resulting from disease.
• Build models that achieve high accuracy and that generalize robustly to new data.
• Apply contemporary interpretability techniques to provide a deeper understanding of the underlying signal identified by the model, ideally suggesting potential biological mechanisms.
• Work closely with ML Engineering partners to ensure that Freenome’s computational infrastructure supports optimal model training and iteration.
• Take a mindful, transparent, and humane approach to your work.

Must haves:

• PhD or equivalent research experience with an AI emphasis and in a relevant, quantitative field such as Computer Science, Statistics, Mathematics, Engineering, Computational Biology, or Bioinformatics.
• 3+ years of postdoc or post-PhD industry experience achieving impactful results using relevant modeling techniques.
• Expertise, demonstrated by research publications or industry achievements, in applied machine learning, deep learning and complex data modeling.
• Practical and theoretical understanding of fundamental ML models like generalized linear models, kernel machines, decision trees and forests, neural networks.
• Practical and theoretical understanding of DL models like large language models or other foundation models.
• Extensive experience with training paradigms like supervised learning, self-supervised learning, and contrastive learning.
• Proficient in current state of the art in ML/DL approaches in different domains, with an ability to envision their applications in biological data.
• Proficiency in a general-purpose programming language: Python, R, Java, C, C++, etc.
• Proficiency in one or more ML frameworks such as; Pytorch, Tensorflow and Jax; and ML platforms like Hugging Face.
• Experience in ML analysis and developer tools like TensorBoard, MLflow or Weights & Biases.
• Excellent ability to communicate across disciplines, work collaboratively, and make progress in smaller steps via experimental iterations.
• A passion for innovation and demonstrated initiative in tackling new areas of research.

Nice to haves:

• Deep domain-specific experience in computational biology, genomics, proteomics or a related field.
• Experience in building DL models for genomic data, with knowledge of state-of-the-art DNA foundation models.
• Experience in NGS data analysis and bioinformatic pipelines.
• Experience with containerized cloud computing environments such as Docker in GCP, Azure, or AWS.
• Experience in a production software engineering environment, including the use of automated regression testing, version control, and deployment systems.

Benefits and additional information:

The US target range of our base salary/hourly rate for new hires is $173,775 - $246,750. You will also be eligible to receive equity, cash bonuses, and a full range of medical, financial, and other benefits depending on the position offered.  Please note that individual total compensation for this position will be determined at the Company’s sole discretion and may vary based on several factors, including but not limited to, location, skill level, years and depth of relevant experience, and education. We invite you to check out our career page @ freenome.com/job-openings/ for additional company information.  

Freenome is proud to be an equal-opportunity employer, and we value diversity. Freenome does not discriminate on the basis of race, color, religion, marital status, age, national origin, ancestry, physical or mental disability, medical condition, pregnancy, genetic information, gender, sexual orientation, gender identity or expression, veteran status, or any other status protected under federal, state, or local law.

Applicants have rights under Federal Employment Laws.  

• Family & Medical Leave Act (FMLA)
• Equal Employment Opportunity (EEO)
• Employee Polygraph Protection Act (EPPA)

#LI-HYBRID

About this opportunity:

At Freenome, we are seeking a Staff Machine Learning Scientist to help grow the Machine Learning Science team, within the Computational Science department. The ideal candidate has a strong knowledge of artificial intelligence (AI), including machine learning (ML) fundamentals and extensive experience with deep learning (DL) methods, a track record of successfully using these methods to answer complex research questions, the ability to drive independent research and thrive in a highly cross-functional environment.

They will be responsible for the development of algorithms for early, blood-based detection tests for cancer. They will build on a foundation of ML/DL and statistical skills to develop models for identifying molecular signals from blood. They will also work with computational biologists, molecular biologists and ML engineers to design and drive research experiments, and will have a significant impact on the continued growth of an organization dedicated to changing the entire landscape of cancer.

The role reports to the Director, Machine Learning Science. This role can be a Hybrid role based in our Brisbane, California headquarters (2-3 days per week in office), or remote.

What you’ll do:

• Independently pursue cutting edge research in AI applied to biological problems (including cancer research, genomics, computational biology, immunology, etc.).
  
• Build new models or fine-tune existing models to identify biological changes resulting from disease.
  
• Build models that achieve high accuracy and that generalize robustly to new data.
  
• Apply contemporary interpretability techniques to provide a deeper understanding of the underlying signal identified by the model, ideally suggesting potential biological mechanisms.
  
• Work closely with ML Engineering partners to ensure that Freenome’s computational infrastructure supports optimal model training and iteration.
  
• Take a mindful, transparent, and humane approach to your work.
  

Must haves:

• PhD or equivalent research experience with an AI emphasis and in a relevant, quantitative field such as Computer Science, Statistics, Mathematics, Engineering, Computational Biology, or Bioinformatics.
• 6+ years of postdoc or post-PhD industry experience achieving impactful results using relevant modeling techniques.
• Expertise demonstrated by research publications or industry achievements, in driving independent research in applied machine learning, deep learning and complex data modeling.
• Practical and theoretical understanding of fundamental ML models like generalized linear models, kernel machines, decision trees and forests, neural networks, boosting and model aggregation.
• Practical and theoretical understanding of DL models like large language models or other foundation models.
• Extensive experience with training paradigms like supervised learning, self-supervised learning, and contrastive learning.
• Proficient in current state of the art in ML/DL approaches in different domains, with an ability to envision their applications in biological data.
• Proficiency in a general-purpose programming language: Python, R, Java, C, C++, etc.
• Proficiency in one or more ML frameworks such as; Pytorch, Tensorflow and Jax; and ML platforms like Hugging Face.
• Experience in ML analysis and developer tools like TensorBoard, MLflow or Weights & Biases.
• Excellent ability to communicate across disciplines, work collaboratively, and make progress in smaller steps via experimental iterations.
• Proficient at productive cross-functional scientific communication and collaboration with software engineers and computational biologists.
• A passion for innovation and demonstrated initiative in tackling new areas of research.

Nice to haves:

• Deep domain-specific experience in computational biology, genomics, proteomics or a related field.
  
• Experience in building DL models for genomic data, with knowledge of state-of-the-art DNA foundation models.
  
• Experience in NGS data analysis and bioinformatic pipelines.
  
• Experience with containerized cloud computing environments such as Docker in GCP, Azure, or AWS.
  
• Experience in a production software engineering environment, including the use of automated regression testing, version control, and deployment systems.
  

Benefits and additional information:

The US target range of our base salary for new hires is $199,675.00 - $283,500.00. You will also be eligible to receive equity, cash bonuses, and a full range of medical, financial, and other benefits depending on the position offered.  Please note that individual total compensation for this position will be determined at the Company’s sole discretion and may vary based on several factors, including but not limited to, location, skill level, years and depth of relevant experience, and education. We invite you to check out our career page @ freenome.com/job-openings/ for additional company information.  

Freenome is proud to be an equal-opportunity employer, and we value diversity. Freenome does not discriminate on the basis of race, color, religion, marital status, age, national origin, ancestry, physical or mental disability, medical condition, pregnancy, genetic information, gender, sexual orientation, gender identity or expression, veteran status, or any other status protected under federal, state, or local law.

Applicants have rights under Federal Employment Laws.  

• Family & Medical Leave Act (FMLA)
• Equal Employment Opportunity (EEO)
• Employee Polygraph Protection Act (EPPA)

#LI-HYBRID