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PDT Partners

Quantitative asset management via algorithmic trading

Performance Engineer

Systems Software EngineeringConfirmed live in the last 24 hours
Full-timeNew York, NY, USA$195k - $225k /yr

About the Job

Requirements

  • Linux internals expertise: kernel subsystems, scheduling, memory management, virtual memory, cgroups, namespaces, and the networking stack.
  • Performance tooling: proficiency with perf, eBPF, Ftrace, strace, sysstat, and related tracing/profiling tools. Ability to develop custom tooling when existing tools fall short.
  • Systems-level optimization: Experience tuning system and application performance via sysctls, scheduler policies, NUMA topology awareness, memory allocators, network tunables, and storage I/O configuration.
  • Hardware-software interface: Deep understanding of CPU microarchitecture, cache hierarchies, memory subsystems, hardware performance counters, and how software behavior maps to hardware reality.
  • Versatile coding ability: Comfortable reading and contributing to code across multiple languages (C, C++, Python, Go, Rust, Java) and navigating unfamiliar codebases quickly.
  • Observability and telemetry: Experience building or integrating monitoring and observability infrastructure (Prometheus, Grafana, OpenTelemetry).
  • Collaborative and communicative: Ability to analyze performance holistically and to effectively communicate findings, trade-offs, and recommendations to development teams and stakeholders.

Responsibilities

  • Analyze and understand end-to-end system performance to enhance researcher throughput and velocity.
  • Work across the OS, kernel, networking stack, storage subsystems, memory hierarchy, and CPU layers to optimize compute resource utilization.
  • Perform deep-dive analysis using Linux performance tools to identify bottlenecks, latency outliers, and non-scalable patterns.
  • Read and understand software layers, from application code through language runtimes, system libraries, and kernel code paths, providing suggestions/PRs that optimize critical paths.
  • Free up capacity, reduce costs, and scale models by improving computational efficiency through tuning, configuration, and architectural changes.
  • Develop systems to improve performance telemetry and observability
  • Conduct proof-of-concept evaluations of new performance technologies (new kernel features, alternative system libraries) and contribute to system design decisions.
  • Work collaboratively with development teams to catch performance regressions, review performance-sensitive code, and share optimization learnings across the organization

Desired Qualifications

  • Linux internals expertise: kernel subsystems, scheduling, memory management, virtual memory, cgroups, namespaces, and the networking stack.
  • Performance tooling: proficiency with perf, eBPF, Ftrace, strace, sysstat, and related tracing/profiling tools. Ability to develop custom tooling when existing tools fall short.
  • Systems-level optimization: Experience tuning system and application performance via sysctls, scheduler policies, NUMA topology awareness, memory allocators, network tunables, and storage I/O configuration.
  • Hardware-software interface: Deep understanding of CPU microarchitecture, cache hierarchies, memory subsystems, hardware performance counters, and how software behavior maps to hardware reality.
  • Versatile coding ability: Comfortable reading and contributing to code across multiple languages (C, C++, Python, Go, Rust, Java) and navigating unfamiliar codebases quickly.
  • Observability and telemetry: Experience building or integrating monitoring and observability infrastructure (Prometheus, Grafana, OpenTelemetry).
  • Collaborative and communicative: Ability to analyze performance holistically and to effectively communicate findings, trade-offs, and recommendations to development teams and stakeholders.

About the Company

PDT Partners

Quantitative asset management via algorithmic trading

PDT Partners uses quantitative models to identify market inefficiencies and predict price movements for institutional clients through algorithmic trading. It analyzes large market datasets to find patterns and executes trades with automated strategies that run with precision. Differentiators include a long history since the 1990s, emphasis on empirical validation and scientific research, and a collaborative culture that vets ideas before implementation. The goal is to deliver consistent investment returns by systematically exploiting market inefficiencies through data-driven trading.

Company size
201-500
Company stage
Not listed
Total funding
Not listed
Headquarters
New York City, New York
Founded
1993

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