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Internship Machine Learning Chemistry Jobs in New York

Jane Street

Machine Learning Engineer

New York, NY

Many classes and activities are shared with our Software Engineering interns, while others focus specifically on machine learning applications and techniques. Machine learning is a critical pillar of ...

Jane Street

Machine Learning Engineer

New York, NY · On-site

Many classes and activities are shared with our Software Engineering interns, while others focus specifically on machine learning applications and techniques. Machine learning is a critical pillar of ...

Trading Interview

Machine Learning Engineer

Manhattan, NY · On-site

$55 - $83/hr

Many classes and activities are shared with our Software Engineering interns, while others focus specifically on machine learning applications and techniques. Machine learning is a critical pillar of ...

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All JobsMachine Learning Chemistry JobsInternship Machine Learning Chemistry JobsInternship Machine Learning Chemistry Jobs in New York

Internship Machine Learning Chemistry information

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 are the key skills and qualifications needed to thrive as an internship in machine learning chemistry?

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 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.

What are the most commonly searched types of Machine Learning Chemistry jobs in New York?

The most popular types of Machine Learning Chemistry jobs in New York are:

What are popular job titles related to Internship Machine Learning Chemistry jobs in New York?

For Internship Machine Learning Chemistry jobs in New York, the most frequently searched job titles are:

What job categories do people searching Internship Machine Learning Chemistry jobs in New York look for?

The top searched job categories for Internship Machine Learning Chemistry jobs in New York are:

What cities in New York are hiring for Internship Machine Learning Chemistry jobs?

Cities in New York with the most Internship Machine Learning Chemistry job openings:

Internship Machine Learning Chemistry jobs near you
Infographic showing various Internship Machine Learning Chemistry job openings in New York as of August 2026, with employment types broken down into 1% As Needed, 79% Full Time, 19% Part Time, and 1% Contract. Highlights an 87% Physical, 2% Hybrid, and 11% Remote job distribution.

Machine Learning

Cantor Fitzgerald Securities

Manhattan, NY • On-site

Full-time

Posted 3 days ago

New

Job description


Join Cantor Fitzgerald Technology Markets LLC as a Machine Learning Engineer focused on building AI-driven solutions for a high-volume financial services business. You will work closely with product, engineering, and business teams to create, test, and operationalize large language model (LLM) applications, ensuring they meet performance, reliability, and responsible-AI standards.
Responsibilities
  • Design and implement LLM-driven features in production systems.
  • Build and maintain data pipelines for both structured and unstructured data.
  • Write clean, testable Python code and maintain reusable libraries.
  • Develop prompts, tool-calling workflows, and retrieval pipelines.
  • Create evaluation suites, define success metrics, and analyze failures.
  • Diagnose and mitigate hallucination, latency, and cost issues.
  • Collaborate with product, engineering, and business stakeholders.
  • Implement monitoring, logging, and alerting for AI services.
  • Contribute to responsible-AI guardrails and human-in-the-loop processes.
  • Document designs, experiments, and findings for internal knowledge sharing.

Qualifications
  • Bachelor's degree in computer science, machine learning, mathematics, physics, statistics, econometrics, or equivalent practical experience.
  • Experience contributing to production or production-like software through work, internships, research, open source, or substantial personal projects.
  • Strong programming ability in Python with clear, tested, and maintainable code.
  • Experience with web services, data integrations, testing, logging, and basic monitoring across diverse data types.
  • Hands-on experience building with LLM tools or frameworks (prompting, structured outputs, tool-calling, retrieval, multi-step workflows) and awareness of common failure modes.
  • Experience evaluating LLM-powered applications: building test sets, reviewing failures, defining metrics, and iterating on prompts or retrieval.
  • Solid grounding in machine learning, statistics, and experimental design with ability to interpret technical papers and documentation.
  • Strong communication skills and comfort working with product, engineering, and business partners.
  • Interest in applying AI responsibly in financial services, including privacy, security, human review, and appropriate automation.
  • Familiarity with cloud deployment, containers, and modern release pipelines.

$140,000 - $160,000

Apply