Junior Quantitative Researcher - Deep Learning (Time Series)

About Akuna:
Akuna Capital is an innovative trading firm with a strong focus on collaboration, cutting-edge technology, data driven solutions and automation. We specialize in providing liquidity as an options market maker – meaning we are committed to providing competitive quotes that we are willing to both buy and sell. To do this successfully we design and implement our own low latency technologies, trading strategies and mathematical models.
Our Founding Partners, including Akuna's CEO Andrew Killion, first conceptualized Akuna in their hometown of Sydney. They opened the firm’s first office in 2011 in the heart of the derivatives industry and the options capital of the world – Chicago. Today, Akuna is proud to operate from additional offices in Shanghai, Sydney, Singapore and London.
At Akuna, we believe that the people are the centre of everything we do. Akuna Software Technologies was our first international office and opened in the Fall of 2014. Our Shanghai office works with the latest hardware and software technologies to develop high performance low latency solutions that are robust and scalable, ensuring our strategies are as fast as possible for Akuna’s global trading operations. We run happy hours, have a fully stacked snack room with free drinks and fresh fruits, host team events, social club events, and offer great training with our Akuna University. We are looking for the best talent to join us on the journey. If you enjoy being part of smart, driven teams with real challenges to solve- this could be the place for you!
What you’ll do as a Junior Quantitative Researcher at Akuna:
We're looking for a Junior Quantitative Researcher who will work specifically on deep learning projects involving large-scale time series data. Strong preference for candidates with hands-on DL experience over general ML/data science backgrounds. In this role you will:
Develop trading strategies using statistical and deep learning technologies
Design and implement optimization algorithms for portfolio construction
Develop quantitative models describing market behavior
Advance existing initiatives and explore opportunities for new research topics
Qualities that make great candidates:
Bachelors, Masters or PhD in a technical field – Engineering, Statistics, Computer Science, Mathematics, Physics (or a related subject), graduate(d) during Jul 2023 - Jul 2027
Deep learning experience is required: hands-on work with neural network architectures (e.g., RNNs, LSTMs, Transformers, TCNs, or similar) applied to sequential or time series data — through research, working projects, competitions, or internships
Programming skills: Strong Python programming experience, including familiarity with DL frameworks (PyTorch and/or TensorFlow); C++ knowledge is a plus
Experience handling large-scale datasets efficiently — data pipelines, batching, and training at scale (GPU experience is a plus)
Solid foundation in math and statistics, with the ability to translate real-world, noisy, high-dimensional problems into mathematical/DL models
Prior exposure to time series-specific challenges (non-stationarity, autocorrelation, forecasting, sequence modeling) is a strong plus
Financial experience is not a requirement
Willing to communicate in English at the technical interviews
Please note: If you apply to multiple roles, you may be asked to complete multiple coding challenges and interviews.