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Weekend Machine Learning Postdoc Jobs in New York, NY

Jane Street

Machine Learning Researcher

New York, NY · On-site

An undergraduate, PhD student, or postdoc with practical experience working on ML problems ... Curious about the machine learning landscape and excited to apply state-of-the-art techniques drawn ...

New York University

POSTDOCTORAL ASSOCIATE

New York, NY · On-site

$62K - $67K/yr

Description POSTDOCTORAL ASSOCIATE New York University Tandon School of Engineering NYU Tandon ... Conducting advanced research in machine learning and data analytics for power system operation and ...

Comcast

Machine Learning Engineer 2

Manhattan, NY · On-site

$126K - $189K/yr

Must be able to work nights and weekends, variable schedule(s) as necessary. * Other duties and ... Skills: Machine Learning Algorithms; Design; Python (Programming Language) Salary: New York Pay ...

Comcast

Machine Learning Engineer 2

New York, NY · On-site

$126K - $189K/yr

Training machine learning models, validating their accuracy, and manage the deployment of these ... Must be able to work nights and weekends, variable schedule(s) as necessary. * Other duties and ...

Mount Sinai Hospital

Postdoctoral Fellow-MSH

Manhattan, NY · On-site

$53K - $73K/yr

This Postdoctoral Research Fellowship position will focus primarily on using biomedical data science and machine learning methodologies to explore cardiovascular disease phenotypes in the Mount Sinai ...

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Weekend Machine Learning Postdoc information

What is the difference between Weekend Machine Learning Postdoc vs Weekend Data Scientist?

AspectWeekend Machine Learning PostdocWeekend Data Scientist
Required CredentialsPhD in Computer Science, Machine Learning, or related fieldBachelor's or Master's in Data Science, Computer Science, or related field
Work EnvironmentAcademic research settings, universities, research labsIndustry companies, startups, consulting firms
Employer & Industry UsageResearch institutions, universities, academic grantsTech companies, finance, healthcare, retail
Common Search & ComparisonYesYes

The Weekend Machine Learning Postdoc typically involves academic research with a focus on advancing machine learning theories and models, often requiring a PhD. In contrast, a Weekend Data Scientist applies data analysis and machine learning techniques in industry settings, often with a bachelor's or master's degree. Both roles may work on similar projects but differ mainly in their environment, credentials, and end goals.

What are the typical projects and collaboration opportunities for a Weekend Machine Learning Postdoc?

As a Weekend Machine Learning Postdoc, you will often contribute to ongoing research projects, developing and refining machine learning models in collaboration with faculty, graduate students, and occasionally industry partners. While your hours are concentrated on weekends, you’ll typically participate in regular research meetings, code reviews, and may co-author papers or grant proposals. The role provides opportunities to mentor junior researchers and expand your expertise by working on interdisciplinary teams. This structure allows you to make significant research contributions while maintaining flexibility in your schedule.

What is a Weekend Machine Learning Postdoc?

A Weekend Machine Learning Postdoc is a postdoctoral researcher who focuses on machine learning projects and typically works on weekends or has a flexible schedule that includes weekend hours. This role often involves conducting advanced research in machine learning, developing algorithms, publishing papers, and collaborating with academic or industry teams. Weekend postdoc positions may be ideal for those balancing other commitments or seeking non-traditional work hours while continuing their research careers.

What are the key skills and qualifications needed to thrive as a Weekend Machine Learning Postdoc, and why are they important?

To thrive as a Weekend Machine Learning Postdoc, you need a strong background in machine learning, statistics, and programming, typically supported by a PhD in a relevant field. Experience with tools such as Python, TensorFlow, PyTorch, and data analysis platforms, as well as familiarity with academic research methodologies, is essential. Exceptional problem-solving abilities, self-motivation, and effective communication are vital soft skills for success in research and collaboration. These skills enable you to drive innovative research, efficiently manage independent projects, and contribute meaningful insights to the field.
What are the most commonly searched types of Machine Learning Postdoc jobs in New York, NY? The most popular types of Machine Learning Postdoc jobs in New York, NY are:
What are popular job titles related to Weekend Machine Learning Postdoc jobs in New York, NY? For Weekend Machine Learning Postdoc jobs in New York, NY, the most frequently searched job titles are:
What job categories do people searching Weekend Machine Learning Postdoc jobs in New York, NY look for? The top searched job categories for Weekend Machine Learning Postdoc jobs in New York, NY are:
Weekend Machine Learning Postdoc jobs near you

Machine Learning Researcher

Jane Street

New York, NY • On-site

Full-time

Re-posted 20 days ago

Job description

About the Position
Our goals are to give you a real sense of what it's like to work at Jane Street as a Machine Learning Researcher while also providing a truly unparalleled educational experience. You'll work side by side with experienced ML Researchers on projects that we've selected for their combination of novel ML ideas and relevance to real-world systematic trading strategies. You'll learn how we think about markets through challenging classes and activities, and practice using established methods alongside our own unique twists to train practical models.
At Jane Street, the lines between research, technology, and trading are intentionally blurry, and you'll have access to petabytes of data, a computing cluster with hundreds of thousands of cores, and a growing GPU cluster containing thousands of high-end GPUs. Trading poses unusual challenges-large models and nonstationary datasets in a competitive multi-agent environment-that force us to search for novel techniques.
You'll spend the bulk of your internship working closely with full-time machine learning researchers on projects drawn from their own work. You might conduct an end-to-end study of an unexplored dataset, try a new modeling paradigm for a thorny problem, or consider blue-sky approaches that we're still trying to figure out. The problems we work on rarely have clean, definitive answers, and they often require insights from colleagues across the firm with different areas of expertise. Depending on the day, you might be diving deep into market data, tuning hyperparameters, debugging training issues, or analyzing the predictions your model makes.
Note that given the IP-sensitive nature of machine learning research at Jane Street, it is unlikely that any research findings associated with the internship will be suitable for outside academic publication.
About You
If you've never thought about a career in finance, you're in good company. Many of us were in the same position before working here. If you have a curious mind and a passion for solving interesting problems, we have a feeling you'll fit right in. We're more interested in how you think and learn than what you currently know. You should be:
  • An undergraduate, PhD student, or postdoc with practical experience working on ML problems
  • Interested in applying logical and mathematical thinking to all kinds of problems
  • Curious about the machine learning landscape and excited to apply state-of-the-art techniques drawn from many problem domains
  • Fluent with a versatile set of models and tricks
  • Able to rapidly implement and iterate on your ideas in Python and your favorite ML framework
  • Eager to ask questions, admit mistakes, and learn new things

If you'd like to learn more, you can read about our interview process and meet some of the team. Learn more about Jane Street's internship program here.

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