Quant Engineer

Digital Waffle; New York (NY); On-site; USD 120,000 - 180,000; Full time

Job summary

Digital Waffle in New York is seeking a Quant Engineer for a role that blends engineering and research. You will build data pipelines, backtesting frameworks and production systems to run quantitative strategies, while staying close to modelling to iterate quickly on ideas.

This is a 5-days-a-week in-office position in NYC. The role combines performance-sensitive data work with collaboration on model implementation, feature engineering and productionisation, delivering robust data and live

Qualifications

• Strong Python production experience with Pandas, NumPy, Polars.
• Solid SQL fundamentals for data pipelines.
• Experience with performance-sensitive engineering (C++, Rust, or optimized Python).
• Track record building data, backtesting or execution infrastructure.
• Experience with time-series data issues (survivorship bias, lookahead, data gaps).
• Familiar with AI engineering tools to raise output.
• High autonomy and pragmatic decision-making.
• Willing to be in-office 5 days a week in New York.

Responsibilities

• Build and maintain high-performance data pipelines in Python and SQL.
• Design backtesting infrastructure and simulation environments.
• Develop low-latency execution and signal-processing systems.
• Collaborate with researchers on model implementation and productionisation.
• Own data quality, timing and reproducibility across lifecycle.
• Work with storage/compute tools (DuckDB, kdb+, Parquet-based lakes).
• Leverage AI tooling to accelerate workflows.

Skills

Python prod experience

Pandas

NumPy

Polars

SQL fundamentals

Performance engineering

C++/Rust

Time-series data

AI tools

Autonomy

In-person collaboration

Tools

DuckDB

kdb+

Parquet

Job description

A high-growth AI-native startup is hiring a Quant Engineer in New York 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: New York, 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.