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Temporary Machine Learning Postdoc Jobs (NOW HIRING)

Senior Machine Learning Scientist

Brisbane, CA ยท On-site +1

$110K - $150K/yr

... postdoc or post-PhD industry experience achieving impactful results using relevant modeling ... machine learning, deep learning and complex data modeling. * Practical and theoretical ...

Staff Machine Learning Scientist

Brisbane, CA ยท On-site

$199K - $283K/yr

... postdoc or post-PhD industry experience achieving impactful results using relevant modeling ... machine learning, deep learning and complex data modeling. * Practical and theoretical ...

Staff Machine Learning Scientist

Brisbane, CA ยท On-site +1

$199K - $283K/yr

... postdoc or post-PhD industry experience achieving impactful results using relevant modeling ... machine learning, deep learning and complex data modeling. * Practical and theoretical ...

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Temporary Machine Learning Postdoc information

What are the key skills and qualifications needed to thrive as a Temporary Machine Learning Postdoc, and why are they important?

To thrive as a Temporary Machine Learning Postdoc, you need a PhD in a relevant field, a solid grasp of machine learning theory, and strong programming skills (often in Python or R). Experience with tools such as TensorFlow, PyTorch, and high-performance computing environments, as well as a record of peer-reviewed research, is typically required. Strong analytical thinking, collaboration, and effective communication help you stand out in this research-intensive role. These skills are essential for advancing cutting-edge research, publishing impactful findings, and contributing to interdisciplinary projects.

What types of projects and collaborations can a Temporary Machine Learning Postdoc expect to engage in during their appointment?

A Temporary Machine Learning Postdoc typically works on cutting-edge research projects, often contributing to ongoing studies or initiating novel investigations within the field. Collaboration is common, both within their immediate research group and with interdisciplinary teams, such as data scientists, domain experts, or industry partners. Postdocs may also mentor graduate students, present findings at conferences, and publish papers, gaining valuable experience that can lead to academic or industry roles. The environment is fast-paced and research-driven, offering opportunities for professional growth and expanding one's research portfolio.

What is a Temporary Machine Learning Postdoc?

A Temporary Machine Learning Postdoc is a fixed-term research position, typically held at a university or research institution, focused on advancing knowledge and techniques in machine learning. Postdoctoral researchers in this role work on specific projects, often collaborating with faculty, graduate students, or industry partners. The position is designed to provide advanced training and research experience after earning a PhD, usually lasting from several months to a couple of years. Temporary postdocs may contribute to publishing academic papers, developing algorithms, and mentoring students, while preparing for longer-term academic or industry careers.

What is the difference between Temporary Machine Learning Postdoc vs Data Scientist?

AspectTemporary Machine Learning PostdocData Scientist
CredentialsPhD in Computer Science, Data Science, or related fieldBachelor's or Master's in Data Science, Computer Science, or related field; often requires experience
Work EnvironmentAcademic or research institutions, labsCorporate, tech companies, startups
Employer & Industry UsageUniversities, research centersBusiness, technology, finance, healthcare
Search & Comparison IntentUnderstanding research-focused roles, academic opportunitiesIndustry roles, applied data analysis, business impact

The Temporary Machine Learning Postdoc is primarily research-oriented, often in academic or research settings, requiring a PhD. In contrast, a Data Scientist typically works in industry, applying data analysis and machine learning to solve business problems, often with a Bachelor's or Master's degree. Both roles involve machine learning skills but differ in environment, focus, and experience level.

More about Temporary Machine Learning Postdoc jobs
What cities are hiring for Temporary Machine Learning Postdoc jobs? Cities with the most Temporary Machine Learning Postdoc job openings:
What are the most commonly searched types of Machine Learning Postdoc jobs? The most popular types of Machine Learning Postdoc jobs are:
What states have the most Temporary Machine Learning Postdoc jobs? States with the most job openings for Temporary Machine Learning Postdoc jobs include:
What job categories do people searching Temporary Machine Learning Postdoc jobs look for? The top searched job categories for Temporary Machine Learning Postdoc jobs are:
Infographic showing various Temporary Machine Learning Postdoc job openings in the United States as of June 2026, with employment types broken down into 50% Full Time, 22% Part Time, and 28% Temporary. Highlights an 94% In-person, and 6% Remote job distribution.
Senior Machine Learning Scientist

Senior Machine Learning Scientist

Freenome

Brisbane, CA โ€ข On-site, Remote

$110K - $150K/yr

Other

Posted 22 days ago


Job description

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.ย  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/ย for additional company information.ย ย 

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.ย ย 

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

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