| Job Position | Company | Posted | Location | Salary | Tags |
|---|---|---|---|---|---|
Binance | Taipei, Taiwan |
| |||
OpenTag | Sofia, Bulgaria | $82k - $110k | |||
Groma | Boston, MA, United States | $57k - $72k | |||
Elwoodtechnologies | Remote |
| |||
| Learn job-ready web3 skills on your schedule with 1-on-1 support & get a job, or your money back. | | by Metana Bootcamp Info | |||
B2c2 | Remote | $84k - $108k | |||
Bluecubeservices | Remote | $63k - $81k | |||
Crypto.com | Sofia, Bulgaria | $57k - $72k | |||
Coins.ph | Manila, Philippines | $45k - $60k | |||
Tether Operations Limited | Warsaw, Poland | $76k - $84k | |||
Dvtrading | Remote | $33k - $72k | |||
Bitfinex | London, United Kingdom | $86k - $101k | |||
Anchorage Digital | Singapore, Singapore | $86k - $88k | |||
Elwoodtechnologies | Remote |
| |||
Bitpanda | Remote | $90k - $92k | |||
Dvtrading | Remote | $66k - $78k |
Junior Quantitative Researcher (Fresh STEM PhD graduates are welcome)
We are building out a new research function at the intersection of artificial intelligence and quantitative trading to improve the efficiency of execution algo models and more, and we are looking for a Junior Quantitative Researcher to be a founding member of this effort. You will work alongside senior quants, engineers, and traders to design AI-driven workflows that generate alpha signals, diagnose model and PnL behavior, and deepen our understanding of market microstructure.
This is a high-ownership role suited to someone who is genuinely excited about markets, has a strong research background, and is already building with modern AI tooling â including LLM-based agents. We are open to hiring at the fresh-PhD level, provided you can demonstrate research depth and a real interest in trading.
Â
Responsibilities
-
Signal research and construction. Develop, test, and productionize predictive signals across asset classes using a combination of statistical methods, machine learning, and AI agentâdriven research workflows. Take ideas from hypothesis through backtest, validation, and deployment.
-
Root cause analysis (RCA). Investigate model behavior, signal decay, PnL attribution, and unexpected trading outcomes. Build tools â including agentic ones â that accelerate diagnosis and shorten the loop between observation and fix.
-
Market microstructure research. Study order book dynamics, execution costs, liquidity, and venue behavior to inform both signal design and execution strategy.
-
AI agent infrastructure for research. Help design and extend internal agentic systems that automate parts of the research pipeline â data exploration, hypothesis generation, backtest configuration, results summarization, and report drafting.
-
Collaborate broadly. Work closely with traders, engineers, and other researchers to turn ideas into live, monitored strategies.
Requirements
-
PhD (recently completed or near completion) in a quantitative field â e.g., Computer Science, Machine Learning, Statistics, Physics, Mathematics, Electrical Engineering, Operations Research, or a related discipline.
-
Strong programming skills in Python; comfortable with the modern data and ML stack (NumPy, pandas, PyTorch or JAX, etc.).
-
Hands-on experience building with AI agents and LLM-based systems â for example, tool-using agents, multi-step reasoning pipelines, retrieval systems, or evaluation frameworks. We want to see that you have actually built things, not just read papers.
-
Solid grounding in statistics, probability, and machine learning, with the rigor to know when a result is real and when it isn't.
-
Genuine interest in financial markets and trading, demonstrable through coursework, personal projects, competitions, internships, or self-directed study.
-
Strong written and verbal communication; able to explain technical work clearly to a mixed audience.
Nice to Have
-
Prior internship or research experience at a hedge fund, prop trading firm, market maker, bank, or fintech.
-
Exposure to market microstructure, limit order books, or high-frequency data.
-
Experience with backtesting frameworks, time-series analysis, or causal inference.
-
Familiarity with low-latency systems, or large-scale data infrastructure.
-
Publications, open-source contributions, or trading competition results.