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 HaveExperience with MetaTrader (MT4/MT5) bridge integrations
Knowledge of FX/CFD/crypto market microstructure
Contributions to open-source trading, quant, or ML projects
Role SummaryLocation New York, NY / Remote
Type Full-time
Reports to VP Engineering
Compensation$180K – $240K base
Equity participation
Annual learning budget
