量趨科技股份有限公司

This role will focus on developing quantitative algorithmic CTA and high-frequency trading strategies using machine-learning-driven and data-driven methodologies, you will need to think about how to exploit modern machine-learning techniques on diverse financial data sets. It's quite different from other typical machine learning jobs because our percentage-based lucrative dividends and annual bonuses are directly associated with your model's performance! You will also have the opportunity to conduct independent algorithmic research. The main programming languages are Python and Rust. 【About Us】 Quantrend Technology focuses on building financial trading strategies across a variety of asset classes and global markets. We empower the paradigm shift from traditional quant to AI quant by using modern end-to-end deep learning models. The difference between traditional approaches and our proprietary solution is that our models can automatically extract robust and high-quality trading signals (Alphas), but traditional hand-crafted approaches often fail to do so. 【Responsibilities】 1. Conduct quantitative research, and apply advanced modern machine learning methods to diverse data sets to build robust models for forecasting financial market risks and returns. 2. Design and implement algorithmic CTA and high-frequency trading strategies including backtesting and evaluation. 3. Research / propose/validate new effective financial market predictive features, models, and trading strategies. 4. Design and implement directional movement/volatility/risk/price impact/slippage forecasting models in CTA and high-frequency trading. 5. Deep reinforcement learning-based optimal control of trade execution, risk management, and portfolio construction. 6. Self-supervised / unsupervised learning on financial market data sets. 7. Co-work with trading system developers to deploy trading strategies in live trading environments. 【Requirements】 1. Advanced training in Mathematics, Statistics, Physics, Computer Science, Electrical Engineering, Financial Engineering, or another highly quantitative field. (Bachelor’s, Master’s, Ph.D. degree) 2. Strong knowledge of probability, statistics, machine learning, deep learning, time-series analysis, pattern recognition, computer vision, NLP, etc. 3. Strong programming skills in Python machine learning packages, including NumPy, pandas, scikit-learn, XGboost, Tensorflow, and Keras or PyTorch. 4. Solid experience in EDA (exploratory data analysis) using Python, familiarity with data visualization using packages including matplotlib, seaborn, etc. 5. Deep understanding of machine learning theories and algorithms, with the ability to debug ML models, tune hyperparameters, and identify and solve the root cause of model performance bottlenecks. 6. In-depth understanding of deep learning theories, network architecture design, and training/optimization techniques, with hands-on experience in the development of deep learning models. 7. Superb analytical and quantitative skills, understanding of and experience with mapping domain problems into algorithms, along with a healthy streak of creativity. 8. Entrepreneurial, highly-productive, extremely detail-oriented, with a sense of ownership of his/her work, working well both independently and within a small collaborative team. 9. Great communication and problem-solving skills. 10. Self-motivated and fast-paced learner. 【Nice to Have】 1. Bachelor’s degree in financial engineering. 2. Experience in trading and in-depth knowledge of financial markets. 3. Prior experience working in a data-driven research environment. 4. Experience in training DRL (Deep Reinforcement Learning). 5. Bayesian / hierarchical probabilistic graphical modeling experience. 6. Experience in algorithmic trading. 7. Knowledge of SQL and NoSQL databases and Docker containers. 8. Experience with AWS.

This position focuses on the development of backend components of algorithmic CTA and high-frequency trading systems. You will be focusing on optimizations for performance and latency-critical high-frequency trading systems. And also be co-working with quantitative trading strategy developers to implement the live high-frequency trading system and bring trading strategies online. The main programming languages are Rust and Python. 【About Us】 Quantrend Technology focuses on building financial trading strategies across a variety of asset classes and global markets. We empower the paradigm shift from traditional quant to AI quant by using modern end-to-end deep learning models. The difference between traditional approaches and our proprietary solution is that our models can automatically extract robust and high-quality trading signals (Alphas), but traditional hand-crafted approaches often fail to do so. We are a performance-driven company, seeking agile and talented people to join our team. Our working environment is relaxed yet intellectually intense. If you are a tech-savvy individual who enjoys the challenges of solving difficult technical problems in a fast-paced, energetic environment, then this is the role for you. We value the contributions of trading strategy developers and offer lucrative dividends and annual bonuses to high-performance employees. 【Responsibilities】 1. Develop algorithmic CTA and high-frequency trading systems. 2. Implement trading risk management / alert systems. 3. Implement financial data preprocessing pipelines. 4. Implement trading data logger components and dashboards. 5. TCP layer latency tuning and advanced CPU instruction set/compiler optimizations for HFT systems. 6. Build up CI / CD with Docker containers and Kubernetes on GCP and AWS. 7. Write unit tests and integration tests. 【Requirements】 1. Bachelor’s degree in computer science or equivalent experience. 2. Solid backend system implementation experience. 3. Expert-level knowledge of algorithms, design patterns, OOP, threading, multiprocessing, etc. 4. Knowledge of design and implementation of high-availability, high-throughput, and low-latency backend components. 5. 3+ years of experience with Rust / Golang / Python, or other programming languages, like C/C++, C#, Java, and Node. JS. 6. Familiar with SQL databases and NoSQL databases (like MongoDB). 7. Familiar with REST API and WebSocket. 8. Familiar with microservices, Docker containers, Kubernetes, and CI/CD. 9. Familiar with Git, software engineering, and agile software development. 10. Strong writing and verbal communication skills, ability to express complex concepts in simple terms. 11. Excellent analytical and problem-solving skills and extremely detail-oriented. 12. Self-motivated and fast-paced learner. 【Nice to Have】 1. Bachelor’s degree in financial engineering. 2. Experience and in-depth knowledge of financial markets. 3. Experience in algorithmic trading and machine learning algorithms. 4. Experience with ReactJS, Redux, WebPack, or other modern front-end development skills. 5. Experience with building web-based data dashboards.

[About Us] Quantrend Technology focuses on building financial trading strategies and providing technology, infrastructure and software services across a variety of digital asset classes and global markets. We empower the paradigm shift from traditional quant to AI quant by using modern end-to-end deep learning models. We are a performance-driven company, seeking agile and talented people to join our team. Our working environment is relaxed yet intellectually intense. We value the contributions of all developers who contribute to the team and services and offer competitive commissions and annual bonuses to high-performance employees. [Key Responsibilities] 1. Product Vision & Roadmap Define the product strategy, vision, and roadmap for our proprietary TAAS (Trading-as-a-Service) platform. Work closely with quant researchers, developers, and infrastructure engineers to translate complex trading needs into user-centric product features. 2. Cross-functional Collaboration Act as the central point between engineering, quant, and business teams to prioritize features and drive product iterations. Lead product discovery and translate insights into scalable product specs and workflows. 3. Market & User Understanding Deeply understand the workflow of institutional traders, quant teams, and hedge funds to develop intuitive product solutions. Gather feedback from internal users and early clients to refine product-market fit. 4. Execution & Delivery Own the full product lifecycle: from ideation and spec writing to delivery, testing, and refinement. Define and track KPIs to measure product performance and adoption. [Qualifications] -Bachelor’s degree in Computer Science, Engineering, Finance, or related fields. -Proven experience as a Product Manager, ideally in fintech, trading systems, or B2B SaaS. -Strong understanding of trading systems, financial infrastructure, or quantitative strategies is a major plus. -Ability to break down technical complexity into clear, actionable product goals. -Excellent communication skills in English. -Self-driven, highly organized, and comfortable working in fast-paced, high-stakes environments. -Experience working with global or cross-cultural teams is a plus.