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Online Machine Learning Postdoc Jobs in California

Adobe

Machine Learning Engineer 4

San Jose, CA

As a Senior Machine Learning Engineer (MLE 40), you will design, build, and deploy models that ... Experience designing and analyzing offline and online evaluations, including A/B testing and ...

Adobe, Inc.

Machine Learning Engineer 4

San Jose, CA · On-site

As a Senior Machine Learning Engineer (MLE 40), you will design, build, and deploy models that ... Experience designing and analyzing offline and online evaluations, including A/B testing and ...

Spotter

Machine Learning Scientist

Culver City, CA · On-site

Overview We're looking for a talented and intensely curious Machine Learning Scientist with deep ... and online learning systems that improve product outcomes. * Creating systems that balance ...

PayPal

Machine Learning Engineer

San Jose, CA · On-site

$117K - $138K/yr

We help merchants and consumers connect, transact, and complete payments, whether they are online ... This job will assist in designing, developing, and implementing machine learning models and ...

Spotter

Machine Learning Scientist

Culver City, CA · On-site

What You'll Do You'll develop machine learning models that move beyond experimentation and into ... and online learning systems that improve product outcomes. * Creating systems that balance ...

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All JobsOnline Machine Learning Postdoc JobsOnline Machine Learning Postdoc Jobs in California

Online Machine Learning Postdoc information

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

AspectOnline Machine Learning PostdocData Scientist
Required CredentialsPhD in Computer Science, Machine Learning, or related fieldBachelor's or Master's in Data Science, Computer Science, or related field; often a PhD is preferred but not required
Work EnvironmentAcademic research settings, universities, research labsIndustry companies, tech firms, startups, corporate analytics teams
Employer & Industry UsagePrimarily academic, research-focused roles in universities or research institutionsCommercial sector, product development, data analysis, and business intelligence

The Online Machine Learning Postdoc typically focuses on academic research, exploring new algorithms and theories in machine learning, often in a university setting. In contrast, a Data Scientist applies machine learning techniques to solve real-world business problems in industry. While both roles require strong technical skills, the Postdoc emphasizes research and publication, whereas Data Scientists focus on data analysis and product development.

What are the most commonly searched types of Machine Learning Postdoc jobs in California?

The most popular types of Machine Learning Postdoc jobs in California are:

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For Online Machine Learning Postdoc jobs in California, the most frequently searched job titles are:

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Cities in California with the most Online Machine Learning Postdoc job openings:

Online Machine Learning Postdoc jobs near you

Machine Learning Engineer - Drug Discovery

Astrix Inc

South San Francisco, CA • On-site

$60 - $70/hr

Full-time, Contractor

Posted 23 days ago

Job description

Pay Rate Low: 60 | Pay Rate High: 70
Our client is a leading biotech company seeking a highly motivated AI/ ML Scientist to join their innovative research organization focused on applying artificial intelligence and machine learning to drug discovery and molecular design.
Title: Machine Learning Scientist - Drug Discovery
Location: Remote - United States (PST preferred)
Schedule: Full-Time, 40 hours/week
Contract Duration: 12 months, with a strong possibility of extension
Employment Type: W-2 + Benefits
Compensation: $60-$70/hour, depending on experience and qualifications
Job Details:
This role will focus on designing, developing, training, and deploying advanced machine learning models and computational engines that support lab-in-the-loop molecular design and optimization. Areas of focus include sequence modeling, molecular structure, conformational ensembles, molecular property prediction, natural language processing, computer vision, and robotics. The successful candidate will work in a highly collaborative, multidisciplinary environment alongside ML scientists, ML engineers, computational scientists, and drug design experts to develop next-generation solutions at the intersection of AI and life sciences.
Key Responsibilities
  • Design, develop, optimize, evaluate, and deploy advanced deep learning models, including large language models, multimodal transformers, and generative AI models.
  • Build and optimize scalable data pipelines supporting machine learning and scientific applications.
  • Optimize model training and inference for performance, scalability, and accuracy using multi-GPU and cloud-based infrastructure.
  • Develop and maintain MLOps workflows covering model deployment, version control, monitoring, reproducibility, and ongoing model performance.
  • Develop machine learning approaches that connect diverse datasets, including genomics, transcriptomics, imaging, molecular, and clinical data.
  • Partner with scientists and engineers across disciplines to translate innovative machine learning methods into practical applications for drug discovery, disease research, and biomedical applications.
  • Independently troubleshoot complex modeling, software, and infrastructure challenges and drive solutions from development through deployment.

Qualifications:
  • B.S., M.S., or Ph.D. in Computer Science, Machine Learning, Computational Biology, Data Science, Statistics, Mathematics, or a related quantitative discipline.
  • Must be authorized to work in the United States without current or future employer sponsorship.
  • 1-5 years of relevant professional experience, including postdoctoral research where applicable.
  • Strong foundation in data structures, algorithms, software engineering, and computational problem solving.
  • Expert-level Python programming skills.
  • Extensive experience with deep learning frameworks such as PyTorch, JAX, or TensorFlow.
  • Strong debugging and software development skills, with the ability to independently diagnose and resolve complex technical issues.
  • Experience with large-scale or distributed model training, such as DDP, Ray, FSDP, or DeepSpeed.
  • Experience with model deployment technologies such as Triton or ONNX.
  • Experience working with cloud and/or GPU computing infrastructure.
  • Hands-on experience with geometric deep learning, molecular cofolding models, neural force fields, or related scientific ML approaches is highly preferred.

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