Principal Scientist or Assoc Director, Bioinformatics / Machine Learning

Job Description

Job DescriptionAbout Radionetics
Radionetics Oncology, Inc. is a clinical stage radiopharmaceutical company focused on the discovery and development of novel radiopharmaceuticals for the treatment of a wide range of oncology indications and is poised to capitalize on the increasing demand for novel radiotherapeutics. Radionetics Oncology is supported by Frazier Life Sciences, 5AM Ventures, DCVC Bio, Crinetics Pharmaceuticals, and GordonMD Global Investments and has entered into a strategic agreement with Eli Lilly. Radionetics is advancing a pipeline of first-in-class small molecule radioligands targeting G protein coupled receptors for the treatment of a broad range of cancers, including breast cancer, lung cancer, and other indications of high unmet need. For more information, visit https://radionetics.com.

Position summary
We are seeking a highly experienced Principal Scientist/Associate Director (Princ Sci/Assoc Dir) to lead the application of machine learning and AI to large‑scale proteomics data in support of radiopharmaceutical target validation, prioritization, and patient selection strategies. This role will also have scientific ownership of the internal sample and proteomics data infrastructure, ensuring data quality, consistency, and long‑term usability across discovery and clinical programs. The Princ Sci/Assoc Dir will operate at the intersection of computational biology, proteomics, and translational oncology, transforming complex datasets into actionable insights that directly impact both preclinical and clinical decision‑making.

Essential job functions and duties

• Create and manage the central sample database integrated with the internal proteomics database, including the definition and implementation of data standards, schemas, and governance practices
• Integrate internal proteomics and sample databases with public resources ensuring harmonization between internal and public datasets
• Apply supervised, unsupervised, and semi‑supervised learning approaches for high‑dimensional proteomics data
• Design and implement machine learning models using quantitative LC‑MS/MS proteomics data to: (1) identify biologically meaningful patient subgroups; (2) derive protein signatures predictive of target expression, uptake, and response; and (3) support target validation, prioritization, and indication selection
• Collaborate with translational and clinical teams to align analytical outputs with clinical study objectives
• Develop proteomics‑based patient selection signatures to: (1) identify responder‑enriched patient populations; (2) inform inclusion/exclusion criteria for clinical trials; and (3) support potential companion diagnostic strategies
• Develop models evaluating tumor selectivity versus normal and critical organs, and expression stability across disease stages and metastatic sites
• Maintain a work environment focused on scientific integrity and quality
• Perform other duties as required by business needs

Education & Experience

• Ph.D. in Computer Science / Machine Learning or similar field with relevant experience (industry experience preferred)

Required Expertise

• Hands‑on experience with biological data infrastructure, including sample and omics data management
• Proven use of public biological databases
• Deep understanding and expertise of Machine Learning Principles and how they apply to different models
• Proficiency in R and/or Python’s deep learning libraries
• Familiarity with multimodal data integration, including early and/or late fusion strategies.
• ML applied to Omics data (e.g., Proteomics, RNA-seq, DNA methylation), biological imaging modalities (e.g. microscopy, H&E, IF), and/or spatial biology.

Highly Desirable

• Experience with multi-GPU and distributed training at scale
• Experience analyzing large‑scale proteomics (LC‑MS/MS) datasets
• Experience in oncology drug discovery or translational research
• Familiarity with membrane proteins, GPCRs, or surface‑targeted therapeutics
• Experience supporting target validation, biomarker development, or clinical study design

Non-standard work schedule, travel or environmental requirements
Position is on-site in San Diego; occasional weekend work hours may be required.

Compensation & Benefits
Radionetics has a competitive total compensation package that includes bonus opportunity; equity; medical, dental, vision, life, short-term, and long-term disability insurance; 401(k) retirement plan with employer match; 4 weeks of paid time off (PTO) annually; and generous paid holidays.

Pay Range
The pay for this position is $175,000 - $215,000 and dependent on the level of position hired. Radionetics evaluates a variety of factors in determining individual pay decisions, which may include relevant education, experience, and skills; internal equity; complexity and responsibility of the role; and market demand relative to the position. Geographic location may also be a consideration in evaluating salary when candidates work in states outside of California.

Important notices
Radionetics Oncology, Inc. is committed to a policy of equal opportunity in which all qualified applicants receive equal consideration for employment without regard to race, color, national origin, ancestry, religion, sex, pregnancy, marital status, sexual orientation, gender, gender identity and expression, age, physical and medical disability, medical condition, genetic information, military or veteran status, or any other federal, state or local protected class.

The job description specifics provided above are intended to describe the general nature and level of work performed by people assigned to this classification. They are not intended to be construed as an exhaustive list of all responsibilities and requirements. Radionetics retains the right to add or change duties, education, experience, skills or any other requirements of the position at any time.

Radionetics does not accept unsolicited referrals from employment agencies for position vacancies unless written authorization is provided from the Human Resources department before any candidates are referred for specific identified positions. In the absence of such written authorization, any actions taken by the employment business/agency shall be deemed to have been performed without consent or contractual agreement, and Radionetics shall not be liable for any fees arising from such actions or referrals for position vacancies at Radionetics.

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