What You'll DoAs a Researcher in the AI Lab, your key responsibilities include:
- Designing, developing, and training novel ML models and methods - including LLMs and other foundation models - drawing on advances in deep learning and sequence modeling, for deployment in production trading systems
- Researching and developing new ML approaches for complex quantitative and sequential modeling problems
- Formulating hypotheses and designing rigorous experiments and evaluation frameworks, using appropriate baselines, ablations, and out-of-sample testing to identify promising research directions and understand why approaches succeed or fail
- Translating ideas from research papers and theoretical work into working implementations, adapting and extending them to new problem domains
- Applying ML techniques to large-scale financial datasets, including time-series and unstructured data, to identify and evaluate potential predictive signals
- Training and evaluating models at scale, leveraging GPU and distributed computing environments as needed
- Building and leveraging research tooling, including agentic and automated research workflows, to accelerate experimentation
What You'll GetYou'll join a culture of collaboration and excellence, surrounded by curious thinkers and creative problem-solvers. Motivated by a passion for continuous improvement, you'll thrive in a supportive, high-performing environment alongside talented colleagues, collectively tackling some of the toughest challenges in the financial markets.
In addition, you'll receive:
- The opportunity to work alongside best-in-class professionals from over 40 different countries
- 401(k) match up to 50%
- Comprehensive health, mental, dental, vision, disability, and life coverage
- 25 paid vacation days alongside market holidays
- Extensive office perks, including breakfast, lunch and snacks, regular social events, clubs, sports leagues and more
What We're Looking For- Strong foundations in machine learning, statistics, optimization, and experimental design.
- Demonstrated ability to conduct independent ML research, from developing hypotheses through implementation, experimentation, and evaluation.
- Deep understanding of modern deep learning architectures, particularly transformers, foundation models, sequence models, and/or state-space models.
- Experience developing and training models rather than primarily applying or integrating existing models.
- Strong empirical judgment and the ability to understand why an approach is or is not working and determine the next research direction.
- Strong programming skills, particularly Python, with experience in frameworks such as PyTorch or JAX.
- Experience training and evaluating models in GPU-based computing environments.
Particularly Relevant ExperienceExperience in one or more of the following would be especially valuable:
- Developing and training large-scale foundation models or LLMs
- Large-scale sequence or time-series modeling
- Transformers, efficient attention, SSMs, Mamba, RWKV, or related architectures
- Reinforcement learning or sequential decision-making
- Self-supervised, representation, or generative learning
- Pre-training, post-training, or novel model architecture research
- Distributed training and large-scale GPU workloads
- Quantitative research, financial markets, or high-frequency data
- CUDA, Triton, custom kernels, or ML performance optimization
- Agent-assisted or automated research methodologies
Below is the expected base salary for this position. This position will also be eligible for a discretionary bonus and Optiver's benefits package with the benefits listed above.
Base Salary Range
$200,000-$200,000 USD
