Quant Developer

Hunter Bond; Tallahassee (FL); On-site; USD 140,000 - 210,000; Full time

Benefits offered by this job

Equity on offer

Base salary plus bonus

Startup culture

Job summary

Hunter Bond partners with an ambitious early-stage quantitative investment firm to recruit a Quant Developer who will own mission-critical trading infrastructure. You will work closely with research, trading, and execution teams to design, build, and scale high-performance systems across equities, crypto, and futures in a fast-paced startup setting with tangible P&L impact.

Strong Python skills and ML experience are essential; C++ knowledge is a plus, with a pragmatic, shipping‑minded approach

Qualifications

• Production-quality code in a latency-sensitive trading environment.
• BSc/MSc/PhD in ML/CS/Engineering.
• Advanced Python with PyTorch, TensorFlow, NumPy, pandas, scikit-learn.
• Problem-solving mindset with a passion for shipping real systems.
• Ownership mindset for an early-stage fund.

Responsibilities

• Build, maintain, and evolve production-grade trading systems across multiple assets.
• Optimize performance, reliability, and latency in the execution stack.
• Collaborate with quant researchers to refine data pipelines, signals, and execution logic.
• Contribute as a hands-on engineer across software and quant-development workloads.

Skills

Python

PyTorch

TensorFlow

NumPy

pandas

scikit-learn

Latency-sensitive

Machine learning

Education

Bachelor/Master/PhD in ML/CS/Engineering

Tools

C++

Distributed computing

Data pipelines

Job description

Quant DeveloperClient

Compensation: Excellent base plus bonus + Equity on offer

Location: Connecticut, United States

Overview

We’re partnering with an ambitious, early‑stage quantitative investment firm that’s quietly assembling a world‑class engineering and research team. Backed by experienced founders and operating at the intersection of markets, data, and machine intelligence, this fund is building high‑performance trading systems across equities, crypto, and futures. They’re now looking to add a Quant Developer who thrives in a fast‑moving start‑up environment and wants real ownership over mission‑critical trading infrastructure.

The Role

This is not a siloed role. You’ll sit close to the research, trading, and execution stack, helping to design, build, and scale systems that directly impact P&L from day one. You will contribute broadly as a hands‑on engineer—stepping in across software and quant‑development workloads as needed in a demanding startup setting.

Key responsibilities

• Build, maintain, and evolve production‑grade trading systems that manage and execute orders across multiple MFT strategies and exchanges in equities, crypto, and futures.
• Optimize performance, reliability, and latency across the execution stack, with a strong focus on robustness in live trading environments.
• Partner closely with quant and machine learning researchers to continuously refine data pipelines, signals, and execution logic.
• Contribute broadly as a hands‑on engineer—stepping in across software and quant‑development workloads as needed in a demanding startup setting.

Requirements

• Strong experience building high‑performance, production‑quality code in a trading or similarly latency‑sensitive environment.
• Strong BSc, MSc or PhD academic background in Machine Learning, Computer Science or Engineering.
• Advanced Python skills; experience with PyTorch, TensorFlow, NumPy, pandas, scikit‑learn.
• A pragmatic, problem‑solving mindset—someone who enjoys shipping, iterating, and improving real systems.
• The flexibility and ownership mentality required to succeed at an early‑stage fund.

Nice to Have

• C++ or other low‑level language experience
• Distributed computing/large‑scale data pipelines
• Prior systematic/prop trading or quant fund experience
• Equities mid‑frequency

What’s on Offer

• Start‑up culture with genuine roadmap to $1billion AUM.
• Equity on offer alongside excellent base plus bonus.
• Machine learning is core to the investment process, not an overlay.
• Exposure to multiple asset classes within a unified research framework.
• Opportunity to influence model design, data architecture and research standards.