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Temporary Machine Learning Postdoc Jobs (NOW HIRING)

Tufts University

Postdoctoral Scholar

Medford, MA · On-site

$67K/yr

Overview Postdoctoral Scholar in Machine Learning for Physical Systems The Department of Electrical and Computer Engineering at Tufts University invites applications for a Postdoctoral Scholar in the ...

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Temporary Machine Learning Postdoc information

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 skills and qualifications are needed to thrive as a temporary machine learning postdoc?

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?

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 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.

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Temporary Machine Learning Postdoc jobs near you
Infographic showing various Temporary Machine Learning Postdoc job openings in the United States as of August 2026, with employment types broken down into 1% As Needed, 75% Full Time, 23% Part Time, and 1% Contract. Highlights an 87% Physical, 2% Hybrid, and 11% Remote job distribution.

Machine Learning Scientist - Drug Discovery

Astrix Inc

South San Francisco, CA • On-site

$60 - $70/hr

Full-time, Contractor

Posted 2 days ago

New

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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