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

As of May 30, 2026, the average yearly pay for internship machine learning chemistry in the United States is $42,584.00, according to ZipRecruiter salary data. Most workers in this role earn between $32,500.00 and $46,000.00 per year, depending on experience, location, and employer.

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

To thrive as an intern in Machine Learning Chemistry, you need a solid understanding of chemistry fundamentals and proficiency in programming languages such as Python, often supported by ongoing or completed coursework in chemistry, computer science, or related fields. Familiarity with machine learning libraries (e.g., scikit-learn, TensorFlow), cheminformatics tools (e.g., RDKit), and data analysis platforms is highly valued. Strong analytical thinking, problem-solving skills, and teamwork set standout candidates apart in collaborative research environments. These skills are important to effectively develop, implement, and interpret machine learning models that address complex chemical problems.

What are some common challenges faced during a Machine Learning Chemistry internship, and how can interns overcome them?

Interns in Machine Learning Chemistry often encounter challenges such as bridging the gap between computational methods and chemical domain knowledge, working with complex and sometimes limited datasets, and adapting to rapidly evolving technologies. To overcome these hurdles, it's helpful to proactively seek mentorship from both data scientists and chemists within the team, dedicate time to learning domain-specific concepts, and regularly participate in team discussions to clarify project goals. Embracing a collaborative mindset and staying curious will also help interns effectively contribute and grow in this interdisciplinary environment.

What is an Internship in Machine Learning Chemistry?

An Internship in Machine Learning Chemistry is a temporary, often academic or industry-based position where students or early-career professionals gain hands-on experience applying machine learning techniques to solve problems in chemistry. Interns may work on projects involving data analysis, molecular modeling, drug discovery, or material design using algorithms and computational tools. The internship provides practical exposure to interdisciplinary research, allowing interns to collaborate with chemists, data scientists, and engineers. It is an excellent opportunity to develop both technical and professional skills in a rapidly growing field.

What is the difference between Internship Machine Learning Chemistry vs Chemistry Research Intern?

AspectInternship Machine Learning ChemistryChemistry Research Intern
Required CredentialsBasic programming, chemistry knowledge, courseworkChemistry coursework, lab skills, basic research experience
Work EnvironmentData analysis, coding, computational toolsLaboratory experiments, chemical analysis
Industry UsageTech companies, research labs integrating ML and chemistryAcademic, industrial chemistry labs
Search & Comparison IntentUnderstanding roles combining ML and chemistry internshipsTraditional chemistry research internship details

Internship Machine Learning Chemistry focuses on applying machine learning techniques to chemistry problems, often involving coding and data analysis. In contrast, Chemistry Research Internships emphasize hands-on laboratory research in chemistry. Both roles require chemistry knowledge, but the former integrates computational skills, making it ideal for those interested in data-driven chemistry careers.

More about Internship Machine Learning Chemistry jobs
What cities are hiring for Internship Machine Learning Chemistry jobs? Cities with the most Internship Machine Learning Chemistry job openings:
What are the most commonly searched types of Machine Learning Chemistry jobs? The most popular types of Machine Learning Chemistry jobs are:
What states have the most Internship Machine Learning Chemistry jobs? States with the most job openings for Internship Machine Learning Chemistry jobs include:
What job categories do people searching Internship Machine Learning Chemistry jobs look for? The top searched job categories for Internship Machine Learning Chemistry jobs are:
Internship Machine Learning Chemistry jobs near you
Infographic showing various Internship Machine Learning Chemistry job openings in the United States as of May 2026, with employment types broken down into 78% Full Time, 11% Part Time, 7% Contract, and 4% Nights. Highlights an 69% Physical, 4% Hybrid, and 27% Remote job distribution, with an average salary of $42,584 per year, or $20.5 per hour.

Machine Learning Engineer Intern - Research

Good At Numbers

Remote

$30/hr

Internship

Posted 16 days ago

Job description

GoodAtNumbers is building an always-on decision intelligence platform that is trying to replace what a data scientist, data analyst and a business analyst does. We are looking for someone who can help us push both the research quality and production quality of our ML systems forward.
We are hiring a Machine Learning Engineer Intern for a paid 12-week summer internship from May through July 2026. This is a remote role based in the United States and is expected to be 40 hours per week. Compensation for this internship is $30/hour.
This role sits at the intersection of ML research, software engineering, and MLOps. You will work on problems related to retrieval, context construction, model/tool orchestration, evaluation, monitoring, and the productionization of AI systems. This is a strong fit for someone who can move from experiments to production code and who wants to work on real product problems instead of isolated notebooks.
What you'll work on
  • Design and run experiments across areas such as retrieval, ranking, context construction, tool use, grounded generation, model evaluation, anomaly detection, forecasting, or optimization workflows
  • Improve the quality, reliability, latency, and observability of ML and LLM-driven features
  • Build reproducible evaluation workflows for model behavior, answer quality, grounding, failure analysis, and regression testing
  • Help productionize research work through pipelines, APIs, services, monitoring, versioning, and deployment workflows
  • Improve MLOps practices around experiment tracking, prompt/model versioning, dataset versioning, testing, rollout safety, and post-deployment monitoring
  • Collaborate closely with software and platform engineers to ship ML systems that are useful, measurable, and production-ready

What success looks like by the end of the internship
  • At least one meaningful ML or LLM system is measurably improved in quality, reliability, or latency
  • Research work is backed by reproducible evaluation and monitoring rather than one-off experimentation
  • The path from experiment to production is cleaner, faster, and safer

What we're looking for
  • 3-4 years of relevant experience preferred through research labs, internships, startups, open-source work, or production ML systems
  • Strong software engineering ability and strong comfort writing production-quality code
  • Strong Python skills preferred
  • Experience with machine learning experimentation, evaluation, and debugging preferred
  • Experience with LLMs, retrieval systems, vector search, ranking, prompt/tool workflows, or agent-style systems preferred
  • Experience with MLOps practices such as experiment tracking, versioning, model testing, deployment, and monitoring preferred
  • Comfort with statistics, error analysis, benchmarking, and translating ambiguous research ideas into shippable systems
  • Strong communication and the ability to document tradeoffs, assumptions, and results

Nice to have
  • Experience with PyTorch, Transformers, or modern ML tooling
  • Experience with vector databases, RAG systems, or evaluation harnesses
  • Experience with time-series forecasting, causal analysis, anomaly detection, or optimization systems
  • Experience with Docker, Kubernetes, cloud infrastructure, or batch/orchestration systems
  • Publications, benchmark work, or strong public repos/writeups

Work authorization
Applicants must be authorized to work in the United States for the full internship period and must be based in the U.S. during the internship. We are not able to provide employment visa sponsorship for this internship.
We welcome applicants from all backgrounds and evaluate candidates based on technical depth, execution, communication, and fit for the role.

Apply