About this opportunity:

At Freenome, we are seeking a Senior Software Engineer II to join our innovative Engineering team. The ideal candidate is an experienced backend engineer with a strong background in developing scalable and reliable software solutions. You will play a crucial role in designing, building, and maintaining backend services that support our mission to detect cancer early. This position requires a collaborative mindset, as you will work closely with an interdisciplinary team of engineers, scientists, and product managers to deliver high-quality software solutions. You are passionate about software development best practices 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 our engineering management team. This role will be a Remote role. 

What you’ll do:

• Design, develop, and deploy reliable, maintainable, scalable, and fault-tolerant backend services that power both our internal and external systems.
• Collaborate with interdisciplinary teams, including scientists, product managers, and other engineers, to solve complex problems and deliver high-quality software solutions.
• Mentor and guide junior engineers, fostering their growth and enhancing the team's technical expertise.
• Lead code and design reviews, champion engineering best practices and promote a culture of quality and collaboration.
• Contribute to the development of data infrastructure for machine learning applications, ensuring efficient data processing and integration.
• Drive the implementation of engineering hygiene practices, ensuring the reliability and maintainability of our systems.
• Advocate for and implement innovative software development methodologies and tools to improve team efficiency and product quality.
  

Must haves:

• Bachelor of Science in Computer Science, Engineering, or related field or equivalent training, fellowship, and/or work experience.
• At least 8 years of experience as part of a software development team successfully shipping software products, including leading projects from end-to-end and mentoring others.
• Proficiency in Python and experience with backend development in a team production environment.
• Strong experience with containerization and orchestration technologies such as Docker and Kubernetes.
• Familiarity with cloud platforms like AWS, Azure, or Google Cloud Platform.
• Proven experience in designing and implementing scalable backend systems, with a focus on reliability and performance.
• Excellent written and verbal communication skills, with a mindful and transparent approach to collaboration.
  

Nice to haves:

• Understanding of statistical and machine learning methods, with practical experience in applying them to real-world problems.
• Expertise in developing ETL or data pipelines that run at scale, utilizing common workflow management systems like Flyte, Airflow, or similar.
• Domain-specific experience in computational biology, genomics, bioinformatics, or a related field.
• Experience working in an FDA regulated environment, with a strong understanding of compliance requirements and best practices.
  

Benefits and additional information:

The US target range of our base salary for new hires is $161,925 - $227,325. 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-REMOTE

About this opportunity:

At Freenome, we are seeking a Director, Technical Product Management to help grow the Freenome Product Management team. The ideal candidate is a seasoned leader who bridges the gap between complex molecular biology and scalable software products in a regulated environment. This person is responsible for the technical roadmaps, feature prioritization, software requirements, and specification definition related to Lab Information Management Systems (LIMS), laboratory automation, and software managing sample processing-to-result generation. You are passionate about building seamless, high-throughput clinical workflows that ensure data integrity and regulatory compliance, and you 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, Product Management. This role will be a Hybrid (based in our Brisbane, California headquarters) or Remote role. 

What you’ll do:

• Define and execute the multi-year technical roadmap for IVD/LDT software platforms, ensuring alignment with clinical needs and regulatory requirements.
• Lead feature prioritization and software requirements definition of LIMS, lab automation, and software managing the sample processing-to-result generation to optimize high-throughput sample processing and data integrity.
• Direct the integration of complex diagnostic hardware with cloud-based or on-premise processing software, focusing on seamless end-to-end workflows.
• Ensure all software products comply with global regulatory standards, including FDA, CLIA/CAP, ISO 13485, and HIPAA/GDPR data privacy requirements.
• Collaborate with R&D, Molecular Biology, Clinical Lab, Lab Automation, and Engineering teams to translate complex assay protocols into automated, scalable software requirements.
• Ensure compliance to Software Development Life Cycle (SDLC) for diagnostic products, from initial concept to commercial launch for deliverables owned by Technical Product Management.
• Manage cross-functional dependencies between wet-lab operations, bioinformatics pipelines, and software engineering teams to ensure timely product delivery.
• Establish and monitor key performance indicators (KPIs) for software reliability, processing speed, and diagnostic accuracy in a clinical laboratory environment. 

Must haves:

• 8+ years in Technical Product Management, with at least 4 years specifically in IVD, Medical Devices, or Clinical SaaS.
• Proven experience with LIMS/LIS and laboratory automation workflows.
• Deep familiarity with FDA software guidelines and CLIA regulations.
• BS/MS in Computer Science, Bioengineering, Molecular Biology, or a related field.
• Demonstrated experience managing and scaling technical product teams in a regulated environment.
  

Nice to haves:

• MS or PhD in a technical or scientific field.
• Experience with Next-Generation Sequencing (NGS) data pipelines.
• Hands-on experience with cloud infrastructure (AWS/Azure/GCP) in a GxP environment.

Benefits and additional information:

The US target range of our base salary for new hires is $220,400 - $281,400. 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-REMOTE

Role

The Software & Data Science team at Vivodyne tackles some of the hardest problems at the intersection of human biology, large-scale data, and machine learning using uniquely rich imaging and multi-omics datasets from lab-grown human tissues.

We are expanding our capabilities to generate high-throughput -omics data across automated human tissue models, with a primary focus on single-cell and bulk transcriptomics, secretomics, and proteomics. We're looking for a Bioinformatician who can develop new analytical methods and build robust infrastructure to support them at scale.

You will define how we process, store, integrate, and interpret biological datasets, making foundational decisions that shape the long-term utility of our data for internal research, AI/ML model development, and external partners. This means owning both the science (what question does this analysis answer, and how defensible is the conclusion?) and the engineering (does this pipeline run reliably at scale, and can someone else extend it?).

This is a high-impact, cross-functional role. You'll sit within the Software & Data Science organization and collaborate daily with experimental biologists, AI/ML researchers, software engineers, and client-facing teams.

This is a full-time, onsite role in San Francisco.

Responsibilities

Develop and Own Analytical Methods

• Design, implement, and critically evaluate computational approaches for scRNA-seq, bulk RNA-seq, secretomics, and proteomics data including QC, normalization, batch correction, dimensionality reduction, clustering, differential expression, trajectory analysis, and multi-dataset integration.
• Go beyond applying standard tools: develop or adapt methods for perturbation modeling, causal inference, network reconstruction, ligand-receptor inference, and multi-omics integration as the science demands.
• Select and standardize tooling based on rigorous benchmarking, not convention. Challenge default choices when the data or use-case warrants it.

Build Scalable, Production-Grade Pipelines

• Architect end-to-end analysis workflows in Python and/or R using modern workflow orchestration (Nextflow, Snakemake, or equivalent) that are reproducible, version-controlled, and designed to run at the scale of tens to hundreds of thousands of samples.
• Partner with software and data engineers to productionize pipelines, ensure robust data ingestion and storage, and contribute to decisions on biological data architecture (storage formats, schema design, long-term data usability).
• Write well-documented, testable code that other engineers and scientists can run, review, and extend.

Drive Biological Insight and Interpretation

• Own dataset analysis to answer biological and client-driven questions with rigor. Ground conclusions in the limits of the data and clearly communicate what can and cannot be concluded with confidence.
• Partner with experimental biologists to frame the most relevant biological questions, design analyses, and recommend experiments and data-generation strategies to fill key gaps.
• Produce publication-quality figures, internal reports, and partner-facing deliverables that are decision-useful.

Shape Data Strategy

• Help define benchmarking strategies for internal tissue model development.
• Proactively identify opportunities to leverage internal and public datasets to accelerate scientific progress.
• Contribute to decisions around data standards, comparability, and the integration of omics features into downstream ML models and AI workflows.

Communicate Across Functions

• Translate complex multi-omic analyses into clear insights for biologists, AI/ML researchers, software engineers, and non-technical stakeholders.
• Tailor depth and framing to the audience while maintaining scientific rigor.

Requirements and Expectations

Technical Expertise

• Ph.D. in Computational Biology, Bioinformatics, Genomics, Systems Biology, Biostatistics, or a related quantitative field with deep focus on transcriptomics and/or proteomics.
• 2–5 years of post-PhD experience; some industry experience strongly preferred.
• Strong, hands-on proficiency in Python for bioinformatics (Scanpy and the broader scverse ecosystem in particular) with the judgment to know when existing tools are insufficient and the skill to build alternatives. Familiarity with R-based workflows (Seurat, Bioconductor, Monocle) is valued as evidence of breadth, but Python is the production language.
• Deep experience with scRNA-seq datasets and workflows, including QC, batch correction, clustering, differential expression, annotation, and integration across experiments.
• Experience with proteomics or secretomics analysis (Olink or similar high-dimensional panels), including normalization and cross-modality integration with transcriptomic profiles.
• Comfort in a Unix/Linux environment with standard dev tools (git, containers, CI/CD); experience with cloud environments (AWS) and workflow managers is a strong plus.
• Familiarity with data infrastructure for large-scale biological data (e.g., AnnData/H5AD, TileDB-SOMA, or similar) is a plus.

Problem Solving & Ownership

• Ability to independently structure and execute complex analytical projects from ambiguous initial questions to concrete, reviewable outputs.
• Strong biological intuition for distinguishing meaningful signal from artifact and the discipline to validate before interpreting.
• Comfortable navigating ambiguity in a fast-moving startup; can prioritize, make trade-offs, and hit agreed timelines.

Collaboration & Communication

• Excellent written and verbal communication; able to explain computational methods and biological implications to biologists, engineers, and non-technical stakeholders.
• Proven ability to collaborate across disciplines.

Innovation & Leadership

• Track record of taking omics analyses from raw data to figures/tables suitable for publications, reports, or decision meetings.
• Demonstrated ability to manage multiple analyses in parallel and ship work on predictable timelines.
• History of method development or meaningful adaptation of existing methods to novel data types or experimental designs.

Preferred Qualifications

• Experience with multi-modal data integration (e.g., combining imaging, transcriptomics, and proteomics features).
• Background in drug discovery, toxicology, or translational research where multi-omic data inform therapeutic decisions.
• Experience with representation learning or ML for biological data (embeddings for cells, genes, or proteins), especially in collaboration with ML specialists.
• Familiarity with organoids, primary tissues, or organ-on-chip systems.

Vivodyne, Inc. is an equal opportunity employer. Vivodyne complies with all laws respecting equal employment opportunity and does not discriminate against applicants with regard to any protected characteristic as defined by federal, state, and local law.

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