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Internship Research Assistant Machine Learning Jobs in California

Machine Learning Engineer Location: Fremont, CA (Local) Onsite interview Duration: 12+ Mos H1B Only ... Write clean, modular, and sustainable code to translate research ideas into production-ready ...

We have an opening for a Machine Learning (ML) Bioengineer to conduct research training and evaluating next-generation clinical, protein and genome language models. You will join the Bioresilience ...

Wehave an opening for a Machine Learning (ML) Bioengineer to conduct research training and evaluating next-generation clinical, protein and genome language models. You will join the Bioresilience ...

The role involves designing, building, and deploying machine learning solutions while collaborating with a team of software engineers and researchers to tackle significant security challenges.

Showing results 41-60

Internship Research Assistant Machine Learning information

What is the difference between Internship Research Assistant Machine Learning vs Research Assistant Data Science?

AspectInternship Research Assistant Machine LearningResearch Assistant Data Science
Required CredentialsUndergraduate or graduate in CS, AI, or related fieldsUndergraduate or graduate in CS, Statistics, or related fields
Work EnvironmentAcademic labs, research institutions, tech companiesAcademic institutions, research centers, industry
Employer & Industry UsageUniversities, research firms, tech companies focusing on AI/MLUniversities, research organizations, data-driven industries
Common Search & ComparisonYesYes

The Internship Research Assistant Machine Learning and Research Assistant Data Science roles share similarities in educational background and work environments. However, the Machine Learning position emphasizes AI and ML-specific skills, while Data Science focuses more on statistical analysis and data management. Both roles are common in academic and industry settings, often compared by students and professionals exploring research opportunities in data-driven fields.

What are popular job titles related to Internship Research Assistant Machine Learning jobs in California? For Internship Research Assistant Machine Learning jobs in California, the most frequently searched job titles are:
What job categories do people searching Internship Research Assistant Machine Learning jobs in California look for? The top searched job categories for Internship Research Assistant Machine Learning jobs in California are:
What cities in California are hiring for Internship Research Assistant Machine Learning jobs? Cities in California with the most Internship Research Assistant Machine Learning job openings:

Machine Learning/Operations Research Engineer

Socket.dev

Cupertino, CA • On-site

$180 - $280/hr

Other

Posted 4 days ago


Job description

DESCRIPTION

With the explosive growth of Apple products we are creating new opportunities for individuals to work on the most exciting new technologies at Apple. We are seeking a machine learning/operations research engineer to apply advanced mathematical modeling, statistical analysis, and optimization algorithms to solve complex manufacturing challenges. Machine learning/operations research engineers on our team directly impact our factory throughput, supply chain strategies, and cost-reduction initiatives by transforming raw operational data into actionable, data-driven decisions.

MINIMUM QUALIFICATIONS
  • Master’s degree or PhD in Operations Research, Industrial Engineering, Management Science, Applied Mathematics, or related field. Proficiency with solvers and modeling languages such as Gurobi, CPLEX, CP-SAT, Pyomo, and GAMS.
  • Hands-on experience with simulation platforms like Arena, FlexSim, SimPy, and AnyLogic. Experience with machine learning platforms such as PyTorch and Scikit-learn.
  • Excellent communication and presentation skills; ability to explain complex statistical and mathematical theories to non-technical stakeholders in simple, universal language.
PREFERRED QUALIFICATIONS
  • Proven experience in GenAI application building with agents and agentic workflows. Experience with LLM and LMM development and fine-tuning is a major plus.
  • Proficiency in using cutting-edge GenAI tools, i.e. Claude Code, Roo Code, etc.
  • Familiarity with distributed computing, cloud infrastructure, and orchestration tools, such as Kubernetes, Apache Airflow (DAG), Docker, Conductor, Ray for LLM training and inference at scale is a plus.
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