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IMC is looking for Machine Learning Researchers to join our summer internship program. Selected candidates will work on developing high to mid frequency delta one trading strategies and predictive models for the equities and futures markets. If you’re excited about pushing the boundaries of Machine Learning in trading, utilizing IMC’s significant execution edges, and getting to know our global team across Amsterdam and Sydney, this may be the role for you.

You will be responsible for performing large scale data analysis to derive statistically significant models of market behavior. These predictions are used to inform all of our trading, and any improvement will have a high and immediate impact across the office. You will also be part of the team shaping the future directions of delta one research inside IMC.

You will work as part of an established and growing research team, collaborating closely with traders, software and hardware developers to find improvements to our models and see them impact our production results. IMC competes and wins as a team, with open idea sharing and collaboration across disciplines, desks and offices.

  • You thrive working on meaningful and challenging projects?
  • You want to make an impact in an environment where creativity, teamwork, and innovation are recognized and rewarded?
  • You’re super ambitious, and eager to secure a graduate position before graduating?

.. then this is the right internship for you!

Core Responsibilities: 

  • Leverage deep learning techniques to improve prediction quality in a trading context
  • Collaborate with engineers and traders, to help ensure the research is crafted and used to maximum effect
  • Develop and improve sampling, weighting, cost, target, hyperparameters and network architecture to suit a range of problems and data presentations
  • Push the team forward, by keeping abreast of the latest developments in industry and academia, and tackling some of the hardest problems we have in the quant space
  • Apply understanding of distributed computing, and how to align training hardware and approach to rapidly achieve strong results on very large datasets

Skills and Experience: 

  • PhD candidates/graduates from top universities, with specialization in machine learning, statistics, deep learning, probabilistic programming
  • Strong experience in at least one mainstream deep learning framework (PyTorch, Jax or other)
  • Thorough understanding of the strengths & weaknesses of deep learning architectures
  • Preferred Profiles: candidates nearing completion of their studies, candidates with publications in respected journals in deep learning, GNNs, time-series models 

What to expect:

  • Delve into the trading landscape during a week of classroom-based training
  • Hone your machine learning skills by working on a concrete project, working with cutting edge techniques
  • Enjoy the support of a dedicated mentor and build a valuable network of IMC researchers, traders and engineers
  • Experience IMC’s unique culture, joining regular social events, a day of and participating in charity work
  • Benefit from a highly competitive compensation package, with accommodation included
  • Start your internship in Amsterdam, and finalize it in Sydney!

About Us 

IMC is a leading global market maker with offices in Europe, the US, and Asia-Pacific. We use algorithmic trading and advanced technology to buy and sell securities across multiple trading venues. Our vital role at the center of the financial markets provides liquidity for buyers and sellers worldwide.

In the late 1980s, IMC quickly identified the importance of technology in the fast-evolving trading environment. Our passion for innovation continues to drive us today. Although our company has grown rapidly over recent decades, we still approach our work with the same entrepreneurial spirit, emphasizing continuous improvement and innovation. We like our people to think for themselves, and we give them the responsibility and support they need to make an impact.

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Confirmed 10 hours ago. Posted 21 days ago.

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