Skip to main content

PDT Partners

Cloud Computing Engineer

New York, United States

Location
New York, United States
Work
Hybrid
Employment
Full-time
Level
Senior
Function
Engineering
Pay
$180K – $250K a year (read from the ad)
Posted
5 weeks ago

Pay and the market here

The top of this role’s range, $250K a year, is 9% above the median of $230K for senior engineering jobs in New York (middle half $200K – $250K, 1,001 jobs that state pay).

3,709 engineering jobs open in New York: 12% remote, 695 posted in the last 30 days.

Wondering how well you fit this one? OptiTalent Career checks each requirement against your own record, privately. About OptiTalent Career →

About the role

The Cloud Computing team is a group of experts solving computing problems in the critical path of Research. We work directly with Research and Model Implementation teams and provide them with platform tools and computing resources to take their ideas from inception to real tradable products. We are looking for an ambitious and operationally minded software engineer to join our team as we mature and scale our cloud HPC platform to the next iteration of our firm-wide Research platform.

This is a hybrid position and will require the person to work from our New York City office at minimum 3 days a week.

Why join us?

PDT Partners has a 30+ year track record and a reputation for excellence. Our goal is to be the best quantitative investment manager in the world—measured by the quality of our products, not their size. PDT’s very high employee-retention rate speaks for itself. Our people are intellectually extraordinary and our community is close-knit, down-to-earth, and diverse.

Responsibilities

We are a small flat team sitting at the cross-section of research, implementation, and platform infrastructure. Our team responsibilities span many areas. Including:

  • Design and implementation of cloud-based HPC systems. Our projects involve equal parts engineering and operations for success in our fast-moving environment. You will be expected to conceive and implement projects small and large in the intersection of HPC scheduling, metrics, containerization, software distribution, accelerated compute performance/efficiency, cloud architecture.
  • Running our HPC plant day-to-day. Our research environment is up 24/7, and we want to keep it that way. Everybody on the team contributes to the support of our platform, which thankfully is light because of our automation and quality work.
  • Implementing automation. We will always choose to work smart over working hard. You will be responsible for conception and implementation of automation from CI/CD pipelines to production metrics and monitoring of our cloud HPC platform.
  • Obsessive User Focus. All members of the team are expected to partner with researchers and engineers to deliver high-quality cloud HPC systems that are efficient and reliable. This includes leading projects to evolve it as our needs change.
  • Capacity management and benchmark optimization. Our demand for scale and performance is constant and involves challenging optimization problems for workloads critical to research and trading

Below is a list of skills and experiences we think are relevant. Even if you don’t think you’re a perfect match, we still encourage you to apply because we are committed to developing our people.

  • Bachelors or Masters degree in an Engineering or Applied Sciences field from a rigorous academic program or equivalent professional experience.
  • 5+ years of experience building and shipping software systems
  • 2+ years running production compute platforms and scheduling systems.
  • Mastery of core software engineering concepts such as system design, testing, debugging, building reliable production systems with fluency in a language such as Python, Go, Rust etc.
  • Experience with a cloud-based infrastructure-as-code tool such as Terraform
  • Excellent written and verbal communication skills
  • Experience working with or supporting researchers and/or other developers is a plus
  • Hands-on experience managing inferencing and training hardware at scale
  • Knowledge of NVIDIA GPU management, Kubernetes, Slurm, and the wider large-scale compute ecosystem

The salary range for this role is between $180,000 and $250,000. This range is not inclusive of any potential bonus amounts. Factors that may impact the agreed upon salary within the range for a particular candidate include years of experience, level of education obtained, skill set, and other external factors.

PRIVACY STATEMENT: For information on ways PDT may collect, use, and process your personal information, please see PDT’s privacy notices.

Source

Posted by PDT Partners on their own hiring page, job-boards.greenhouse.io. OptiTalent shows it as published, read 07 Nov 2026 11:56 UTC; the original is the reference.

View the original ad ↗

More at PDT Partners

  • PDT Partners · New York, United States

    HybridFull-timeEntry level$120K – $150K a yearFinance

    Develop broad expertise by rotating across treasury, finance, fund reporting, and tax compliance; Support day-to-day FACT operations, e.g. cash management, margin analysis, tax estimates, compliance reporting, and counterparty management; Identify opportunities to streamline…

  • PDT Partners · New York, United States

    HybridFull-timeMid level$190K – $250K a yearData & AI

    Work closely with senior researchers on a variety of trading strategies and research projects, with the opportunity to conduct independent research and originate research topics over time; Contribute to the long-term success of our research-driven algorithmic trading business

  • Software Engineer

    5 months ago

    PDT Partners · London, England, United Kingdom

    HybridFull-timeMid levelEngineering

    At PDT, technology is a core driver of our business. Engineers tackle complex technical and quantitative challenges that advance our research methodology and investment approach.

  • PDT Partners · New York, United States

    HybridFull-time$195K – $225K a yearEngineering

    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…