Quant Engineer

Digital Waffle; San Francisco (CA); On-site; USD 180,000 - 260,000; Full time

Job summary

Digital Waffle in San Francisco is seeking a Quant Engineer to build the data, modelling and execution infrastructure behind quantitative strategies. This hybrid role blends engineering with research, delivering data pipelines, backtesting frameworks and production systems that live on in live trading.

You’ll partner with researchers on model implementation, feature engineering and productionisation, ensuring data quality, timing and reproducibility across the research-to-production lifecycle

Qualifications

• Strong production Python experience with Pandas/NumPy/Polars.
• Solid SQL fundamentals.
• Experience with high-performance engineering (C++, Rust or optimized Python).
• Track record building data, backtesting or execution infrastructure in quant/trading environments.
• Experience with real-world time-series data issues (survivorship, lookahead, gaps).
• Fluent with AI engineering tools to raise output.
• High autonomy and pragmatism with incomplete information.
• Willing to work in-person 5 days a week in San Francisco.

Responsibilities

• Build and maintain high-performance data pipelines in Python (Pandas, Polars, NumPy) and SQL.
• Design backtesting infrastructure and simulation environments that researchers can trust.
• Build and optimise execution and signal-processing systems with low latency in mind.
• Partner with researchers on model implementation, feature engineering and productionisation.
• Own data quality, timing and reproducibility across the research-to-production lifecycle.
• Work with modern storage and compute (DuckDB, kdb+, Parquet-based lakes or similar) depending on workload.
• Use AI tooling across your workflow to move faster.

Skills

Python

Pandas

NumPy

Polars

SQL

C++

Rust

Backtesting

Tools

DuckDB

kdb+

Parquet

Job description

A high-growth AI-native startup is hiring a Quant Engineer in San Francisco to build the data, modelling and execution infrastructure behind quantitative strategies.

This is a hybrid role: part engineer, part researcher. You’ll be building the data pipelines, backtesting frameworks and production systems that strategies actually run on, while staying close enough to the modelling side to iterate quickly on ideas.

Role: Quant Engineer

Location: San Francisco, 5 days per week in office

What you’ll be doing:

• Build and maintain high-performance data pipelines in Python (Pandas, Polars, NumPy) and SQL
• Design backtesting infrastructure and simulation environments that researchers can trust
• Build and optimise execution and signal-processing systems with low-latency in mind
• Partner with researchers on model implementation, feature engineering and productionisation
• Own data quality, timing and reproducibility across the research-to-production lifecycle
• Work with modern storage and compute (DuckDB, kdb+, Parquet-based lakes or similar) depending on workload
• Use AI tooling (Claude, Cursor and similar) across your workflow to move faster

What you’ll need:

• Strong Python with real production experience (Pandas, NumPy, Polars) and solid SQL fundamentals
• Experience with performance-sensitive engineering, whether in C++, Rust or heavily optimised Python
• Track record building data, backtesting or execution infrastructure in a quant, trading or similar numerically-intensive environment
• Comfort with the realities of real-world time-series data: survivorship bias, lookahead bias, data gaps, timing issues
• Fluent in modern AI engineering tools and able to use them to raise output
• High autonomy and the pragmatism to make foundational calls with incomplete information
• Happy working 5 days a week in-person with a small, high-intensity team

Nice to have:

• Prior experience at a quant fund, HFT firm, trading desk or quantitative crypto team
• C++ or Rust experience for latency-critical paths
• Exposure to kdb+/q, DuckDB or Parquet-based data lakes for market data

This role would suit a quant-leaning engineer who likes building the infrastructure as much as the models and wants to see their work show up in live PnL rather than a slide deck.