Summer 2026 Systems Engineering Internship at PDT Partners

New York, New York, United States

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Not SpecifiedCompensation

InternshipExperience Level

InternshipJob Type

UnknownVisa

Quantitative Finance, Investment ManagementIndustries

Requirements

Current Bachelor's, Master's, or PhD student pursuing degrees in rigorous, highly technical fields (e.g., Computer Science, Computer Engineering)
Eligible for full-time roles starting in 2027 or 2028 (graduating Fall 2026 or later)
Excellent academic background from a rigorous computer science or engineering program
Fast-learner with intellectually curious, problem-solving approach
Proactive with sense of urgency
Strong verbal and written communication skills
Interest in working in a world-class, professional environment
Committed to improvement and keen to apply constructive feedback
Knowledge or interest in: using software and automation to solve problems; distributed computing including hybrid cloud architectures; operating systems including low-level performance tuning and analysis; global hybrid networking including automated configuration and packet analysis; petascale storage systems; information security

Responsibilities

Work closely with expert developers on technically challenging projects to help research, implementation, and software engineering teams take ideas from inception to production
Face problems without well-defined solutions, using creativity to explore new technologies
Learn about infrastructure needed to support a quantitative trading business
Gain experience with multicast market data distribution
Analyze the latency impact of network buffering
Automate the provisioning of cloud infrastructure, including compute, services, and storage
Develop GitOps-driven configuration management for consistent global deployments at scale
Add core components to cloud-based Kubernetes infrastructure
Contribute to PDT's culture through intern-specific and company-wide social events

Skills

Systems Engineering

Networking

DevOps

InfoSec

Cloud

Infrastructure Engineering

PDT Partners

Quantitative asset management and algorithmic trading

About PDT Partners

PDT Partners focuses on quantitative asset management by developing models that identify inefficiencies in financial markets and predict market movements. The firm uses sophisticated trading algorithms that analyze large amounts of market data to find patterns and execute trades accurately. Unlike many competitors, PDT Partners has a long history in algorithmic trading, having been a pioneer in the field since the 1990s. The company serves institutional clients, such as hedge funds and pension funds, helping them optimize their investment strategies through data-driven insights. PDT Partners aims to foster a collaborative environment where its team can continuously explore and innovate, ensuring that all ideas are carefully validated before implementation.

New York City, New YorkHeadquarters

1993Year Founded

VENTURE_UNKNOWNCompany Stage

Quantitative Finance, Financial ServicesIndustries

201-500Employees

Benefits

Full medical, dental, vision, disability, and life insurance coverage

Wellness perks and programs

Generous time off and unlimited sick leave

Match charitable giving

Subsidize fitness and exercise

Emergency back-up child and elder care

401k matching and pre-tax benefits

Risks

Emerging quantitative firms using AI may erode PDT's market share.

Intensifying regulatory scrutiny could lead to stricter compliance requirements for PDT.

Technological advancements necessitate costly upgrades to maintain PDT's competitive edge.

Differentiation

PDT Partners excels in quantitative investing with a focus on market inefficiencies.

The firm leverages cutting-edge technology and scientific research for algorithmic trading.

PDT Partners fosters a collaborative culture, encouraging innovation and intellectual curiosity.

Upsides

Rising ESG investing trends offer opportunities for specialized algorithm development by PDT.

Alternative data sources enhance PDT's market prediction capabilities.

Machine learning adoption allows PDT to integrate sophisticated pattern recognition into models.

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