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Internship Spacex Machine Learning 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 ...

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Internship Spacex Machine Learning information

What is the difference between Internship Spacex Machine Learning vs Data Science Intern?

AspectInternship Spacex Machine LearningData Science Intern
Required CredentialsRelevant coursework, basic programming skills, possibly some experience in MLStatistics, programming, data analysis skills, often a related degree
Work EnvironmentHands-on projects in aerospace, collaborative teams, fast-pacedData analysis, modeling tasks, diverse industries, team-based
Employer & Industry UsageSpaceX, aerospace, technology innovationVarious industries including tech, finance, healthcare

Internship Spacex Machine Learning focuses on applying ML techniques to aerospace challenges at SpaceX, emphasizing engineering and technical skills. Data Science Internships are broader, covering data analysis and modeling across multiple industries. Both roles require programming and analytical skills but differ in industry focus and project scope.

Does SpaceX hire interns?

Yes, SpaceX offers internship programs in various fields including engineering, manufacturing, and software development. Internships typically require students to be enrolled in a related degree program and may involve hands-on projects with mentorship and competitive application processes.

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Internship Spacex Machine Learning jobs near you
Infographic showing various Internship Spacex Machine Learning job openings in New York as of September 2026, with employment types broken down into 1% Internship, 1% As Needed, 78% Full Time, 18% Part Time, and 2% Contract. Highlights an 82% Physical, 3% Hybrid, and 15% Remote job distribution.

Machine Learning

Manhattan, NY • On-site

Cantor Fitzgerald Securities
See more jobs at Cantor Fitzgerald Securitiesexternal

Full-time

Posted 10 days ago

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

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