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How much do associate machine learning chemistry jobs pay per year?

As of May 30, 2026, the average yearly pay for associate machine learning chemistry in the United States is $133,062.00, according to ZipRecruiter salary data. Most workers in this role earn between $46,000.00 and $202,000.00 per year, depending on experience, location, and employer.

What are the key skills and qualifications needed to thrive as an Associate Machine Learning Chemistry, and why are they important?

To thrive as an Associate Machine Learning Chemistry professional, you need a solid background in chemistry, data analysis, and machine learning, typically supported by a relevant degree such as chemistry, computer science, or a related field. Experience with programming languages like Python, machine learning libraries (e.g., TensorFlow, scikit-learn), and cheminformatics software is highly valued. Strong problem-solving skills, attention to detail, and the ability to communicate complex concepts clearly are crucial soft skills. These competencies enable effective collaboration on interdisciplinary teams and the development of innovative solutions in computational chemistry research.

How does an Associate Machine Learning Chemistry professional typically collaborate with research scientists and engineers?

As an Associate Machine Learning Chemistry professional, you will frequently work alongside research scientists and chemical engineers to develop predictive models and analyze experimental data. Collaboration involves translating chemical problems into machine learning tasks, sharing insights from model results, and participating in interdisciplinary meetings to refine research objectives. Effective communication and teamwork are essential, as you may be required to explain machine learning concepts to non-technical colleagues and integrate their domain expertise into your models. This collaborative environment fosters both scientific discovery and professional growth.

What are Associate Machine Learning Chemists?

Associate Machine Learning Chemists are professionals who combine expertise in chemistry with skills in machine learning to analyze chemical data, develop predictive models, and accelerate scientific discovery. They often work on tasks like predicting molecular properties, optimizing chemical reactions, and supporting drug discovery efforts using computational tools. Typically, these roles require a strong foundation in chemistry, programming experience (often in Python), and familiarity with machine learning libraries. Associate positions are generally entry-level or early-career roles, providing support to senior scientists and data scientists in research and development teams.

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

AspectAssociate Machine Learning ChemistryAssociate Data Scientist
Required CredentialsBachelor's or Master's in Chemistry, Data Science, or related fields; familiarity with ML frameworksBachelor's or Master's in Data Science, Statistics, Computer Science; programming skills in Python/R
Work EnvironmentResearch labs, pharmaceutical or chemical companies, biotech firmsTech companies, finance, healthcare, consulting firms
Employer & Industry UsageUsed in industries applying ML to chemical data, drug discovery, materials scienceApplied across industries analyzing large datasets, predictive modeling

Associate Machine Learning Chemistry focuses on applying machine learning techniques specifically to chemical and scientific data, often within research or pharmaceutical settings. In contrast, Associate Data Scientist has a broader scope, working with various data types across multiple industries. Both roles require strong analytical skills and familiarity with ML tools, but their industry focus and data types differ.

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What are the most commonly searched types of Machine Learning Chemistry jobs? The most popular types of Machine Learning Chemistry jobs are:
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Associate Machine Learning Chemistry jobs near you
Infographic showing various Associate Machine Learning Chemistry job openings in the United States as of May 2026, with employment types broken down into 1% As Needed, 63% Full Time, 35% Part Time, and 1% Contract. Highlights an 75% Physical, and 25% Remote job distribution, with an average salary of $133,062 per year, or $64 per hour.

Scientist, Machine Learning (Principal Scientist - Associate Director)

Superluminal Medicines, Inc.

Boston, MA • On-site

Full-time

Medical, Dental, Vision, Life, Retirement, PTO

Posted 9 days ago

Job description

About Superluminal Medicines:
Superluminal Medicines is a generative biology and chemistry company revolutionizing the speed and accuracy of how small molecule medicines are created. The Company's platform aims to create candidate-ready compounds with unprecedented speed using a combination of deep biology, computational and medicinal chemistry, machine learning, and proprietary big data infrastructure. We are expanding the team of talented scientists who seek to build the future of small molecule drug discovery with creativity and innovation.
About the Role:
We are seeking a Machine Learning Scientist to join our integrated discovery team and help advance small molecule drug discovery programs through applied ML. In this role, leading from the bench, you will enable the development, validation and deployment of state-of-the-art ML models to generate the quantitative predictions necessary to drive drug discovery. Beyond technical mastery, you will serve as a core strategic partner to medicinal chemists, computational chemists, and biologists, building models that move programs efficiently toward program decision points and candidate nomination.
Key Responsibilities:
  • Lead the application of Large Language Models (LLMs), co-folding algorithms, and generative chemistry techniques to design novel chemical matter aimed at hitting key program milestones, such as establishing selectivity windows and optimizing drug-like properties
  • Serve as the machine learning POC on cross functional projects partnering with medicinal chemists and structural biologists to refine SAR and structure informed modeling efforts
  • Synthesize complex ML outputs into clear, actionable design hypotheses that cross-functional scientific stakeholders can use to make high-stakes program decisions
  • May be responsible for management and development of internal team members

Required Qualifications:
  • Ph.D. in Computational Chemistry, Computer Science, Machine Learning, or a related field
  • 2+ years applying ML methods in a small molecule drug discovery programs in biotech or pharma environments
  • Demonstrated expertise in statistics, probability theory, data modeling, machine learning algorithms, and the languages used to implement analytics solutions
  • Demonstrated success in a cross-functional environment, including biologists, structural biologists, medicinal and computational chemists, with specific examples of computational designs/algorithms/models that directly influence achievement of program milestones
  • Strong practical proficiency in Python and deep learning libraries (e.g., PyTorch, TensorFlow) is required. Demonstrated ability to build and maintain robust, production-quality ML code and data workflows

Preferred Qualifications:
  • Proven experience with protein-ligand co-folding models (e.g.,Boltz, OpenFold, AlphaFold, etc) and the ability to integrate these structural insights into broader ML discovery pipelines
  • Expertise fine-tuning existing models with internally generated structural biology and biology data
  • Strong knowledge of deep learning frameworks, specifically for affinity prediction, ADMET modeling, and the application of LLMs in a biological or chemical context
  • Experience mentoring and developing teams

Skills & Competencies:
  • A demonstrated track record of innovation in the ML/AI space, including developing and validating new architectures or novel applications of existing models to solve complex drug discovery problems
  • Demonstrated expertise using small molecule drug discovery ML/AI tools e.g. AlphaFold, Boltz, OpenFold, ChemProp, DeepChem, Reinvent, etc)
  • Strong level coding for ML tasks including knowledge of key packages (RDKit, scikit-learn, numpy, pandas, pytorch, DeepChem, polars, PyG/DGL).
  • Strong interpersonal and communications skills in the "why" behind a design to a diverse scientific audience

Benefits:
Superluminal offers a comprehensive benefits package that fully covers employees' annual deductibles and monthly premiums for medical, dental, and vision insurance. The package also includes a 401(k) match program, a Massachusetts transportation subsidy, equity, unlimited paid time off, and both disability and life insurance.
Equal Opportunity Statement:
Superluminal Medicines is an Equal Opportunity Employer committed to a culturally diverse workforce. All qualified applicants will receive consideration for employment without regard to race; color; creed; religion; national origin; age; ancestry; nationality; marital, domestic partnership or civil union status; sex, gender, gender identity or expression; affectional or sexual orientation; disability; veteran or military status or liability for military status.

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