Join Us
We're looking for people who combine market intuition with frontier machine-learning engineering — across quantitative research, ML engineering, and trading execution. Open positions are listed below; for questions, reach out via the email below.
ai@springlake.proIf the email link doesn't open your mail client, please copy the address above and reach out directly.
OPEN POSITIONS
DL-ENG
Deep Learning Algorithm Engineer
Responsibilities
- Research and deploy deep learning models for quantitative trading, including sequence modeling, graph neural networks, and representation learning
- Design and iterate algorithms for multi-factor equity selection and statistical arbitrage strategies
- Track and reproduce state-of-the-art research, assessing feasibility for live trading
Requirements
- Master's degree or above from a QS/ARWU (Shanghai Ranking) top-30 university in computer science, AI, statistics, or a related field
- Strong deep learning foundations; proficient with PyTorch/JAX
- A verifiable track record of designing, training, and shipping large-scale deep learning models, with clear technical contribution and measurable impact
- Strong engineering skills to turn research into reliable production code
Nice to Have
- First-author or core-contributor publications at top venues (NeurIPS, ICML, ICLR, CVPR, ICCV, ECCV, AAAI) or top journals (TPAMI, IJCV, JMLR)
- A quantifiable, verifiable track record in live quant trading strategy development
HPC-ENG
HPC Cluster Optimization Engineer
Responsibilities
- Optimize performance and reliability of our GPU training clusters across compute, network, and storage
- Improve communication efficiency and training throughput for distributed frameworks (PyTorch Distributed, DeepSpeed, Megatron-LM) at scale
- Contribute to cluster scheduling design to raise compute utilization
- Diagnose and resolve performance bottlenecks and stability issues in large-scale training
Requirements
- Bachelor's degree or above in computer science, systems architecture, or a related field
- CUDA proficiency with hands-on GPU performance tuning experience
- Strong knowledge of NCCL, RDMA, InfiniBand, and other high-performance networking
- Led or made substantial contributions to building, tuning, or troubleshooting a 1,000+ GPU cluster, with verifiable, quantifiable results (e.g., throughput or utilization gains)
Nice to Have
- Experience with Kubernetes, Slurm, or similar cluster schedulers
- Storage I/O and kernel-level tuning experience
- Track record of industry-leading performance optimization at ultra-large-scale distributed training
MACRO-RSCH
Macro Strategy Researcher
Responsibilities
- Track global macroeconomics, monetary policy, and cross-market asset linkages to form macro views on global equity and futures markets
- Translate macro research into quantifiable strategy signals or risk factors with the quant research team
- Publish regular macro research to support asset allocation and risk management decisions
Requirements
- Master's degree or above in economics, finance, or a related field from a top-tier institution (Ivy League, Oxbridge, LSE, University of Chicago, or equivalent)
- Rigorous macro framework and cross-asset research capability, with systematic understanding of monetary policy and rate cycles across major economies
- A verifiable track record in macro calls or asset allocation, or research recognized by major institutions or media (e.g., Bloomberg, Reuters, Financial Times)
- Strong reasoning and writing skills to communicate complex macro logic clearly
Nice to Have
- Macro research experience at a top investment bank (e.g., Goldman Sachs, Morgan Stanley, J.P. Morgan) or a well-known macro hedge fund/asset allocation team
- Familiarity with quantitative methods to translate macro views into backtestable strategy logic