Build quantitative finance
from first principles.

Build and lead a top-performing HFT quant team; own the design and live-trading of cross-asset systematic strategies.

Responsibilities

• →Build and lead a top-performing HFT quant team
• →Define and run systematic trading strategies across multiple asset classes
• →Partner with the team to design and implement trading algorithms
• →Track market dynamics and industry trends; stay alert to emerging tech and strategies; iterate and refine continuously

Requirements

• →Advanced degree in Computer Science, Mathematics, Financial Engineering or a related field
• →Significant experience in trading, P&L generation, and buy-side asset management
• →Demonstrated success in assembling and leading teams through alpha research and production rollout
• →Deep, original insight into every module of a high-frequency quant trading stack
• →Strong passion for quantitative trading, algorithmic trading, and market making
• →Self-driven, ambitious, and eager to grow alongside the team
• →Strong technical chops — fluent in Python or C++; demonstrated live-trading experience with ML / DL / RL algorithms is a plus

Build the engineering glue — from model-training tooling to strategy deployment — that takes researchers' work into production.

Responsibilities

• →Design and build model-training tooling that streamlines backtest-to-production, giving researchers turnkey management of models, data and algorithms to accelerate development and testing
• →Implement select strategies and optimize execution-algorithm performance
• →Partner closely with quant researchers to translate mathematical models and strategies into robust production code

Requirements

• →Bachelor's degree or above in Computer Science, Engineering, Mathematics, or a related field
• →Proficient in C++ with 3+ years of hands-on development experience
• →Comfortable in Python; familiar with numpy, pandas, torch, and the broader scientific-Python stack
• →Working knowledge of financial markets and trading-system fundamentals

Build the core hot path of an HFT system — microsecond latency, venue connectivity, and protocol-stack tuning.

Responsibilities

• →Design, build, and optimize a high-performance HFT system with reliable microsecond-latency operation
• →Tune order-submission and market-data paths; raise throughput and stability under burst conditions
• →Integrate with multiple venue APIs (REST / WebSocket / proprietary) and aggressively tune path performance
• →Optimize the network protocol stack — TCP tuning, WebSocket client behavior, reconnection strategy, congestion control, etc.
• →Build ultra-low-latency order-submission and market-data channels using kernel-bypass or user-space stacks

Requirements

• →Bachelor's degree or above in a computer-related field
• →Solid C++11/14/17/20 fundamentals; comfortable with STL, multithreading, and low-level memory model details
• →Deep understanding of TCP — congestion control, delayed ACK, Nagle, zero-copy, protocol-stack behavior analysis
• →Fluent with WebSocket / HTTP / TLS / long-connection maintenance mechanisms
• →Comfortable with high-performance network models: epoll, IO multiplexing, async network frameworks
• →Familiar with at least one kernel-bypass technology: Onload, VMA, DPDK, user-space TCP/IP stack, etc.
• →Hands-on with Linux network and system tuning — TCP parameters, CPU affinity / NUMA, NIC interrupt binding / RSS / RPS / RFS, etc.
• →Real-world experience analyzing p99 / p999 latency
• →Able to design and execute high-concurrency, burst-scenario stress tests
• →Comfortable with perf / bpf / tcpdump / Wireshark / flame graphs to quantitatively pinpoint latency bottlenecks

Join the core quant research workstream on factor development, backtesting, and evaluation. Open to graduate or senior-year undergraduate students.

Responsibilities

• →Factor research: develop factors across multiple data dimensions — fundamentals, technicals, event-driven, and more
• →Factor testing: use the backtest framework to validate factor efficacy and evaluate risk-adjusted performance and stability
• →Data processing: clean and process market data to provide reliable input for factor development
• →Model refinement: help build quant stock-selection and asset-allocation models; improve factor performance within existing strategies
• →Market tracking: study state-of-the-art factor-mining methods and academic frontiers to keep the team competitive

Requirements

• →Experience: able to research, evaluate, and bring a personal library of factors
• →Education: graduate or senior-year undergraduate student in Financial Engineering, Applied Mathematics, Statistics, Computer Science, or a related field
• →Programming: fluent with Python or R for data analysis and backtesting; comfortable with common data-processing and visualization tools
• →Math foundation: solid theoretical grounding in statistics, linear algebra, and time-series analysis
• →Finance knowledge: familiar with quant-investing fundamentals; familiarity with factor models and stock/asset pricing theory is a plus
• →Learning agility: strong interest in quant investing and factor research; quick to pick up new methods and tools
• →Collaboration: strong communication and teamwork skills; able to ramp up quickly into the team's cadence

Plus

• →Multi-asset trading or research experience
• →Prior quant research or factor-development internship experience
• →Familiarity with common factor models (e.g., Barra, Fama-French)
• →SQL or other database experience; big-data processing experience
• →Deep interest in market microstructure or data science