About the Role
You'll be part of the core platform team building the infrastructure that powers over $6.5 trillion in annual trading volume across 200+ brokerages worldwide. This is a technically deep role — you will design and implement distributed systems, low-latency execution engines, and AI-augmented risk modules that must perform flawlessly under extreme market conditions.
We're looking for engineers who think in systems, move fast without breaking things, and have genuine experience applying machine learning or AI in high-stakes production environments.
What You'll Do
- Design and build high-throughput, low-latency backend services for order routing, execution, and risk management
- Develop and productionise ML/AI models for predictive risk scoring, adverse-selection detection, and order-fill optimisation
- Build and maintain real-time data pipelines processing millions of market events per second
- Architect scalable microservices deployed across multi-region cloud infrastructure
- Integrate with FIX protocol, MT4/MT5 bridges, and third-party liquidity provider APIs
- Participate in on-call rotations and own system reliability for critical trading infrastructure
- Mentor junior engineers and lead technical design reviews
- Contribute to our AI Risk Management platform — one of Spencer Logic's most strategic products
Requirements
- 6+ years of backend engineering experience with production-scale distributed systems
- Strong proficiency in Go, Rust, Java, or C++ (low-latency experience preferred)
- Hands-on experience with ML/AI frameworks (PyTorch, TensorFlow, scikit-learn) in production
- Deep understanding of stream processing (Kafka, Flink, or equivalent)
- Experience with financial systems, trading protocols (FIX), or order management systems
- Solid grasp of database internals — TimescaleDB, Redis, PostgreSQL
- Strong understanding of concurrency, memory management, and performance profiling
- Experience with cloud infrastructure (AWS, GCP, or Azure) and container orchestration (Kubernetes)
AI / ML Experience (Required)
Designing and shipping ML models for real-time inference in latency-sensitive environments
Feature engineering from tick-level market data and order-book snapshots
Model monitoring, drift detection, and retraining pipelines in production
Experience with reinforcement learning or time-series forecasting is a strong plus
Nice to Have
- Experience with MetaTrader (MT4/MT5) bridge integrations
- Knowledge of FX/CFD/crypto market microstructure
- Contributions to open-source trading, quant, or ML projects
Role Summary
LocationNew York, NY / Remote
TypeFull-time
DepartmentEngineering
Reports toVP Engineering
Tech Stack
Go
Rust
Python
Kafka
Kubernetes
PostgreSQL
Redis
PyTorch
AWS
FIX Protocol
Compensation
$180K – $240K base
Equity participation
$5,000 equipment budget
Annual learning budget