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Phd Chemistry Machine Learning Jobs (NOW HIRING)

Nanite Inc.

Machine Learning Engineer

Boston, MA

Nanite is a disruptive Machine Learning/AI therapeutics company focused on revolutionizing drug ... Collaborate with chemistry and biology research teams to design data pipelines, analyze ...

Nanite Inc.

Machine Learning Engineer

Boston, MA · On-site

Nanite is a disruptive Machine Learning/AI therapeutics company focused on revolutionizing drug ... Collaborate with chemistry and biology research teams to design data pipelines, analyze ...

Quantum Machines

Machine Learning Engineer

Boston, MA · On-site

Required : • PhD/Master in Machine Learning, Physics, Applied Physics, Quantum Information Science, or a related field. 4+ years of relevant experience • Strong background in Machine Learning and ...

Point72

Machine Learning Engineer

New York, NY · On-site

PhD or PhD candidate in machine learning, computer science or other AI related research fields * Experience with sequential modeling and time series forecasting using deep learning * Experience with ...

SAIC

Machine Learning Developer

Arlington, VA · Hybrid

The Machine Learning Developer will collaborate with software engineers to create innovative ML/AI ... PhD and zero (0) years related experience; four (4) years of experience considered in lieu of ...

Point72

Machine Learning Engineer

New York, NY

PhD or PhD candidate in machine learning, computer science or other AI related research fields * Experience with sequential modeling and time series forecasting using deep learning * Experience with ...

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How much do phd chemistry machine learning jobs pay per hour?

As of Jun 7, 2026, the average hourly pay for phd chemistry machine learning in the United States is $22.26, according to ZipRecruiter salary data. Most workers in this role earn between $18.27 and $24.52 per hour, depending on experience, location, and employer.

What are the key skills and qualifications needed to thrive as a PhD Chemistry professional specializing in Machine Learning, and why are they important?

To thrive as a PhD Chemistry professional specializing in Machine Learning, you need deep expertise in chemistry, strong programming skills (e.g., Python), and advanced knowledge of machine learning algorithms, typically supported by a doctoral degree. Familiarity with tools like TensorFlow, PyTorch, cheminformatics platforms, and statistical analysis software is commonly required. Effective problem-solving, interdisciplinary collaboration, and clear communication are vital soft skills in this field. These abilities are critical for developing innovative computational models, driving scientific discovery, and translating data-driven insights into practical chemical solutions.

What is the difference between Phd Chemistry Machine Learning vs Data Scientist?

AspectPhd Chemistry Machine LearningData Scientist
Required CredentialsPhD in Chemistry or related field, expertise in machine learningBachelor's or Master's in Data Science, Computer Science, or related field; some roles prefer PhD
Work EnvironmentResearch labs, R&D departments, academia, industry research teamsTech companies, finance, healthcare, consulting firms
Industry UsageDeveloping ML models for chemical data analysis, drug discovery, materials scienceBuilding predictive models, data analysis, business insights across sectors

While both roles involve machine learning, Phd Chemistry Machine Learning specialists focus on applying ML techniques to chemical and scientific data, often within research or industry R&D. Data Scientists have a broader scope, working across various industries to analyze data and develop models. The key difference lies in domain expertise and application focus.

How does a PhD in Chemistry with a focus on Machine Learning typically collaborate with interdisciplinary teams in industry settings?

Professionals in this role often work closely with chemists, data scientists, and software engineers to design experiments, analyze complex datasets, and develop predictive models. Effective communication is key, as you may need to translate chemical concepts into machine learning frameworks and vice versa. Regular meetings and collaborative projects are common, allowing you to contribute your chemistry expertise while learning from colleagues in computing and engineering. This interdisciplinary environment fosters innovation and can open pathways to leadership or specialized research roles.

What is a PhD in Chemistry with a focus on Machine Learning?

A PhD in Chemistry with a focus on Machine Learning is a doctoral program that combines advanced chemistry research with computational techniques from machine learning and artificial intelligence. Students in this program apply machine learning algorithms to solve complex chemical problems, such as predicting molecular properties, accelerating drug discovery, or analyzing large datasets from experiments. This interdisciplinary field prepares graduates for careers in academia, industry, or research, where they can leverage computational tools to advance chemical science.
Infographic showing various Phd Chemistry Machine Learning job openings in the United States as of May 2026, with employment types broken down into 95% Full Time, 3% Part Time, 1% Temporary, and 1% Contract. Highlights an 84% Physical, 1% Hybrid, and 15% Remote job distribution, with an average salary of $46,292 per year, or $22.3 per hour.

Machine Learning Engineer

Kanak Elite Services Inc

Bodega Bay, CA • Remote

Contractor

Posted 22 days ago

Job description

Hello There,

My name is Himanshu Sharma, and I serve as the Recruitment Lead at Kanak-IT INC. I am reaching out to share an excellent career opportunity for the role of Machine Learning Engineer with our esteemed client. If you are interested then please share your updated resume at Himanshu01@kanakits.com .

Job Description

Title:  Machine Learning Engineer
Location:  South San Francisco, CA  - hybrid role in Bay Arear
Position Type:  Contract 
 

Note: DO NOT SEND WITHOUT MOLECULAR EXPERIENCE, 

Work on ML workflows for molecular property prediction & generative modeling to accelerate drug discovery. 3–5 yrs esp. or PhD with publications in molecular design.

Must have Masters or PH.D. Must have experience in working environment or while getting Master’s or no to very little work exp with PH.D  in Molecular design. Need to have portfolio of their work or be published. Find me Machine Learning with Molecular experience in Bay Area or someone who will relocate as last resort. 
MindSource is looking for a Machine Learning Engineer to join our client's team in South San Francisco, CA.  They will be developing and deploying advanced computational methods for molecular design.  This is a 12-month hybrid contract.  

About the Role

  • Build pipelines for probabilistic molecular property prediction and Bayesian acquisition to power active learning–driven drug discovery.
  • Engineer workflows for molecular generative modeling and other innovative design approaches.
  • Collaborate with machine learning scientists, engineers, computational chemists, and biologists.
  • Partner with therapeutic development teams to analyze existing molecules and design new candidates.
  • Contribute to ongoing initiatives while driving new research directions.

Qualifications

  • PhD in Computer Science, Chemistry, Chemical Engineering, Computational Biology, Physics, or related quantitative field — OR MS + 3+ years of relevant industry experience.
  • Demonstrated expertise in production-ready ML workflows (e.g., PyTorch + Lightning + Weights & Biases).
  • Strong track record of achievement (e.g., high-impact first-author publication or equivalent).
  • Excellent written, visual, and verbal communication skills.

Preferred Experience

  • Knowledge of physical modeling (e.g., molecular dynamics) and cheminformatics (e.g., RDKit).
  • Background in molecular property prediction, computational chemistry, de novo drug design, medicinal chemistry, small molecule design, self-supervised learning, geometric deep learning, Bayesian optimization, probabilistic modeling, or statistical methods.
  • Hands-on experience with Python, PyTorch, Torch Geometric, PyTorch Lightning, RDKit, and BoTorch.
  • Public portfolio of computational projects (e.g., GitHub).