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Machine Learning Computational Chemistry Jobs in Massachusetts

SupportFinity™

Machine Learning Engineer

Boston, MA · On-site

$140 - $210/hr

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 ...

Bayer

Principal Machine Learning Scientist

Cambridge, MA

$128K - $192K/yr

Principal Machine Learning Scientist The Principal Machine Learning Scientist will develop novel ... D degree in Computational Chemistry/Biology, Chem/Bioinformatics, Chemical/Biological/Molecular ...

Bayer

$128K - $192K/yr

Principal Machine Learning Scientist The Principal Machine Learning Scientist will develop novel ... D. degree in Computational Chemistry/Biology, Chem/Bioinformatics, Chemical/Biological/Molecular ...

Bayer

Principal Machine Learning Scientist

Cambridge, MA · On-site

$128K - $192K/yr

Principal Machine Learning Scientist The Principal Machine Learning Scientist will develop novel ... D. degree in Computational Chemistry/Biology, Chem/Bioinformatics, Chemical/Biological/Molecular ...

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Showing results 21-40

All JobsMachine Learning Computational Chemistry JobsMachine Learning Computational Chemistry Jobs in Massachusetts

Machine Learning Computational Chemistry information

See Massachusetts salary details

$26.3K

$122.8K

$226.9K

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

As of Aug 23, 2026, the average yearly pay for machine learning computational chemistry in Massachusetts is $122,800.00, according to ZipRecruiter salary data. Most workers in this role earn between $82,603.00 and $165,744.00 per year, depending on experience, location, and employer.

What is machine learning computational chemistry?

Machine learning computational chemistry is a field that combines machine learning techniques with computational chemistry to accelerate the discovery and design of molecules and materials. By training algorithms on large datasets of chemical information, researchers can predict molecular properties, simulate chemical reactions, and optimize compounds more efficiently than traditional methods. This approach helps reduce the time and cost required for research in drug discovery, materials science, and related fields.

What are some common challenges faced by professionals working in machine learning computational chemistry roles?

One common challenge in Machine Learning Computational Chemistry roles is integrating large and often complex chemical datasets with appropriate machine learning models, which requires a solid understanding of both domains. Professionals may also encounter difficulties in ensuring that their models are both interpretable and generalizable to new data, as overfitting is a frequent issue. Additionally, collaboration with chemists and data scientists is essential, so clear communication across disciplines is key to success. Staying up to date with the latest developments in both computational chemistry and machine learning is crucial for ongoing professional growth.

What are the key skills and qualifications needed to thrive as a machine learning computational chemist, and why are they important?

To thrive as a Machine Learning Computational Chemist, you need a solid background in chemistry, mathematics, and computer science, typically supported by an advanced degree in computational chemistry, cheminformatics, or a related field. Proficiency with programming languages (such as Python), machine learning frameworks (like TensorFlow or PyTorch), and molecular modeling software is essential. Strong analytical thinking, problem-solving skills, and effective collaboration are key soft skills that help drive innovation and teamwork. These skills and qualifications are critical for developing accurate models, advancing research, and translating computational insights into real-world chemical solutions.

What is the difference between Machine Learning Computational Chemistry vs Computational Chemist?

AspectMachine Learning Computational ChemistryComputational Chemist
Required CredentialsAdvanced degrees in chemistry, computer science, or related fields; knowledge of machine learning and programmingDegree in chemistry, chemical engineering, or related fields; strong background in chemical theory and modeling
Work EnvironmentResearch labs, tech companies, academia; focus on algorithm development and data analysisLaboratories, research institutions, industry; focus on chemical modeling and simulation
Employer & Industry UsageTech firms, pharmaceutical companies, research institutions applying AI/ML techniquesPharmaceutical, chemical, and materials industries conducting chemical research and development

Machine Learning Computational Chemists specialize in applying machine learning algorithms to chemical data, enhancing predictive models and simulations. Computational Chemists focus on traditional chemical modeling and simulations using computational methods. Both roles require strong chemistry backgrounds, but Machine Learning Computational Chemists emphasize data science and AI skills, while Computational Chemists focus on chemical theory and modeling techniques.

What are popular job titles related to Machine Learning Computational Chemistry jobs in Massachusetts?

For Machine Learning Computational Chemistry jobs in Massachusetts, the most frequently searched job titles are:

What job categories do people searching Machine Learning Computational Chemistry jobs in Massachusetts look for?

The top searched job categories for Machine Learning Computational Chemistry jobs in Massachusetts are:

What cities in Massachusetts are hiring for Machine Learning Computational Chemistry jobs?

Cities in Massachusetts with the most Machine Learning Computational Chemistry job openings:

Machine Learning Computational Chemistry jobs near you
Infographic showing various Machine Learning Computational Chemistry job openings in Massachusetts as of August 2026, with employment types broken down into 1% As Needed, 80% Full Time, 18% Part Time, and 1% Contract. Highlights an 87% Physical, 2% Hybrid, and 11% Remote job distribution, with an average salary of $122,800 per year, or $59 per hour.

Scientist II / Senior ML Scientist, Cofolding and Structure-Aware ML

Lila Sciences

Cambridge, MA • On-site

Full-time

Medical, Dental, Vision, Life

Posted 20 days ago

Job description

Your Impact at LILA
Lila Sciences is seeking a Machine Learning Scientist, Cofolding and Structure-Aware ML to train next-generation cofolding models for drug discovery. This role is focused on improving models that reason over proteins, ligands, binding context, and experimental data, potentially using contrastive learning and related representation-learning approaches.
This person should have direct experience training modern scientific ML models, not only using pretrained systems. You will work with ML researchers, computational chemists, computational biophysicists, data engineers, and drug discovery teams to develop models that learn from DEL and related datasets, connect molecular and protein context, and improve AI-driven discovery decisions.
The models developed in this role should produce outputs that medicinal and computational chemists as well as biophysicists can interrogate, validate, and use in downstream agent-driven discovery decisions.
What You'll Be Building
  • Train and evaluate cofolding models for protein-ligand and related molecular discovery applications.
  • Use contrastive learning, representation learning, self-supervised learning, or related methods where they help improve cofolding models trained on molecules, proteins, structures, and experimental readouts.
  • Develop modeling approaches that make DEL data more useful for learning binding, enrichment, selectivity, and structure-activity signals.
  • Build and evaluate models informed by Boltz, AlphaFold-style cofolding, equivariant GNNs, and related structure-aware ML methods.
  • Design training objectives, including contrastive, self-supervised, or multimodal objectives, that connect ligands, proteins, structures, assays, simulations, and experimental data.
  • Build rigorous evaluation frameworks that distinguish meaningful molecular learning from dataset artifacts, leakage, or spurious correlations.
  • Collaborate with data and platform teams to define datasets, labels, negatives, controls, and metadata needed for model training.
  • Partner with computational chemistry and biophysics teams to connect model outputs to physically and chemically meaningful hypotheses.
  • Work with low-data learning scientists to identify which DEL, assay, simulation, or structural data would most improve model performance in focused chemical spaces.
  • Work with research engineers to scale training, inference, and evaluation workflows.
  • Help expose trained models and model-derived capabilities as tools for scientists and AI agents.

What You'll Need to Succeed
  • PhD or equivalent experience in machine learning, computational biology, computational chemistry, bioinformatics, computer science, or a related field.
  • Hands-on experience training deep learning models for molecular, protein, structural biology, or scientific data applications.
  • Experience with contrastive learning, representation learning, self-supervised learning, or multimodal learning.
  • Familiarity with DEL or related selection, enrichment, screening, or molecular assay datasets.
  • Experience with protein-ligand modeling, cofolding, structure prediction, geometric deep learning, or structure-aware molecular ML.
  • Practical experience with PyTorch, JAX, or an equivalent ML framework.
  • Ability to design careful experiments, ablations, and evaluations for scientific ML models.
  • Strong understanding of data quality, leakage risks, negative construction, and benchmark design.
  • Ability to collaborate across ML, data, computational science, and drug discovery functions.

Bonus Points For
  • Hands-on experience with DEL data.
  • Drug discovery experience, especially in protein-ligand modeling or molecular optimization contexts.
  • Experience with Boltz, AlphaFold or AlphaFold-derived methods, equivariant GNNs, diffusion models, protein language models, or molecular encoders.
  • Experience training or extending cofolding, protein-ligand, protein-protein, structure prediction, diffusion, or geometric deep learning models.
  • Experience with distributed model training and large-scale scientific data pipelines.
  • Familiarity with active learning or closed-loop molecular design.
  • Experience integrating ML models into agentic scientific workflows.

Compensation
We offer competitive base compensation with bonus potential and generous early-stage equity. Your final offer will reflect your background, expertise, and expected impact.
U.S. Benefits. Full-time U.S. employees receive a comprehensive benefits program including medical, dental, and vision coverage; employer-paid life and disability insurance; flexible time off with generous company wide holidays; paid parental leave; an educational assistance program; commuter benefits, including bike share memberships for office based employees; and a company subsidized lunch program.
International Benefits. Full-time employees outside the U.S. receive a comprehensive benefits program tailored to their region. USD salary ranges apply only to U.S.-based positions; international salaries are set to local market.
Expected Base Salary Range
$228,000-$358,000 USD
About LILA
Lila Sciences is building Scientific Superintelligence™ to solve humankind's greatest challenges. We believe science is the most inspiring frontier for AI. Rather than hard-coding expert knowledge into tools, LILA builds systems that can learn for themselves.
LILA combines advanced AI models with proprietary AI Science Factory™ instruments into an operating system for science that executes the entire scientific method autonomously, accelerating discovery at unprecedented speed, scale, and impact across medicine, materials, and energy. Learn more at www.lila.ai.
Guided by our core values of truth, trust, curiosity, grit, and velocity, we move with startup speed while tackling problems of historic importance. If this sounds like an environment you'd love to work in, even if you don't meet every qualification listed above, we encourage you to apply.
We're All In
Lila Sciences is committed to equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity or Veteran status.
Information you provide during your application process will be handled in accordance with our Candidate Privacy Policy.
A Note to Agencies
Lila Sciences does not accept unsolicited resumes from any source other than candidates. The submission of unsolicited resumes by recruitment or staffing agencies to Lila Sciences or its employees is strictly prohibited unless contacted directly by Lila Science's internal Talent Acquisition team. Any resume submitted by an agency in the absence of a signed agreement will automatically become the property of Lila Sciences, and Lila Sciences will not owe any referral or other fees with respect thereto.

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