HFT - Lead Python Engineer - Low Latency Trading Infrastructure – New York - Up to $650k TC
Mondrian Alpha
Location
🇺🇸 New York, United States
Type
Full-time
Salary
Undisclosed
Posted
0mo ago
Job Description
HFT - Lead Python Engineer - Low Latency Trading Infrastructure – New York - Up to $700k TC A tier-1 proprietary trading firm is seeking a Lead Python Engineer to join a high-performance engineering team supporting one of the firm's most successful trading businesses in the US. This leading prop trading business has established itself as a tech-first powerhouse through custom-built infrastructure, co-located execution, advanced networking, and sub-microsecond trading systems. This is deep systems engineering, building the low-level Python services that power core trading infrastructure. You'll develop production-grade systems handling high-throughput market data processing, real-time analytics, execution workflows, and performance monitoring, all engineered to deterministic standards where system latency directly drives P&L. The culture is uncompromisingly technical: elite software engineers who own the full stack from market data normalisation and event processing to low-latency services. The focus is pure infrastructure, solving scalability, reliability, and performance challenges. The Firm's Technical Approach This prop shop runs HFT-grade infrastructure across global markets: • Execution stack: Kernel bypass networking, FPGA-accelerated feeds, microwave connectivity, tick-to-trade under 30μs • Data processing: Lock-free structures, zero-copy pipelines, nanosecond timestamping, in-memory analytics • Optimised Python: PyPy/Numba/Cython for hot paths, integrated with C++/Rust for critical components • Distributed systems: Custom event-driven architecture, partitioned services, consistent state across 200+ venues • Production engineering: p99 latency guarantees, deterministic replay, chaos testing, full-stack observability
Key Responsibilities
- Build Python services processing market data with strict latency SLAs and sub-ms response under peak load
- Implement high-throughput analytics engines, execution monitoring, and performance dashboards
- Optimise core infrastructure: custom protocol parsers, memory pooling, async processing, cache-conscious design