Lead Quant Systems Engineer -- Backtesting & Data - Quant Genie AI

Lead Engineer — Backtesting & Data Systems

QuantGenie.ai

Location: United States (Required) - REMOTE

Employment: Full-Time

Seniority: Founding Engineer / Technical Lead

About QuantGenie.ai

QuantGenie is a no-code platform that lets anyone build, backtest, and deploy trading strategies using natural language. Traders describe their ideas in plain English — the platform turns them into structured logic, runs fast, deterministic backtests, and generates deployable code for real platforms.

We’re making strategy development accessible without sacrificing correctness, speed, or depth.We already two strong engineers. Now we’re looking for the technical lead who owns the engine at the heart of the product — someone who has personally built backtesting systems , deeply understands trading-system correctness , and knows how to architect scalable time-series tick-level data infrastructure . This is a high-impact founding role. You’ll be hands-on, own the most mission-critical systems, and shape the engineering culture as we scale.

The Role

You will lead the architecture and development of QuantGenie’s core simulation data platform, while managing and mentoring a small engineering team. Expect your time to split roughly 70% hands-on building and 30% leadership, architecture, and direction-setting . You should be comfortable operating in an early-stage environment where correctness, determinism, and engineering rigor matter just as much as velocity.

What You’ll Build & Lead

• Backtesting Engine (Your Core Domain)
  
  
  

You will own the design and evolution of our engine, including:

• Tick-level precision event modeling
• Bar-based iteration across multiple timeframes (intraday higher TFs)
• Deterministic execution logic, slippage/spread modeling, and order-state transitions
• Multi-asset, multi-strategy portfolio simulation
• Reproducibility, latency reduction, and correctness guarantees
• Scaling approaches for heavy workloads (parallelization, caching, batching)
  
  
  

Non-negotiable: you must have personally implemented a backtesting system before — custom engine, in-house tool, or equivalent.2.

Time-Series & Tick Data Infrastructure

You will architect or supervise the architecture of:

• Ingestion normalization for tick, OHLCV, volume, and derived features
• Efficient storage formats for large datasets
• Indexing, caching, and retrieval patterns optimized for simulation workloads
• Pipelines to compute and version indicators at scale
• intraday multi-symbol data alignment
  
  
  

You can hire supporting engineers, but you must have done this work at a deep level before.

• Platform & System Architecture
• Define the technical roadmap and engineering standards
• Lead backend and data engineers to deliver high-quality, reliable systems
• Guide design around performance, determinism, and debuggability
• Shape the infra supporting NL → SDL → execution workflows
• Collaborate with product and our price-action SME to ensure semantic correctness of signals, indicators, and strategy constructs
  
  
  

What We’re Looking For (Required) - Technical Must-Haves

• You’ve built a backtesting engine end-to-end (not just used one).
• Deep understanding of trading-system mechanics , order state machines, execution modeling, and determinism.
• Strong experience with tick data, multi-timeframe intraday data, and high-volume time-series engineering .
• Python expertise — but more importantly, systems architecture and performance engineering expertise.
• Ability to design scalable data pipelines and simulation architectures.
• Ability to lead engineers while staying hands-on.
  
  
  

Domain Must-Haves

Strong understanding of markets, strategy development workflows, indicators, and signal construction.

Comfort working with quants, NLP/LLM engineers, and product stakeholders.

Ability to reason about correctness, edge-cases, and simulation fidelity at a deep level.

Leadership Must-Haves

Can establish engineering processes from scratch.

Makes clean architectural decisions early (and knows when not to over-engineer).

Thrives in early-stage ambiguity while maintaining rigor.

Bonus (Not Required, But Strong Pluses)

• Experience scaling parallel backtests or distributed compute
• Experience with GPU acceleration or optimizers like Numba/Polars/Rust/Cython
• Familiarity with DSL/AST/strategy-language design
• NinjaTrader, MT5, IBKR, or exchange-style execution modeling
• Prior fintech/quant / startup experience
  
  
  

Compensation

This is a founding / lead engineer role — high ownership, high influence.

• Competitive salary
• Meaningful equity
• Direct influence over architecture, team, and long-term product direction