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Software Performance Engineer Jobs in New York (NOW HIRING)

Seven Seven Software

Senior Performance Engineer

New York, NY · On-site

$114K - $157K/yr

May provide performance engineering expertise in contributing to simulation modelling projects. * Provides input to project plans and recommends process improvements. * Monitors progress project and ...

Layer Health

Machine Learning Engineer

Manhattan, NY

$210K - $250K/yr

In this role, you will design, develop, and deploy scalable ML software systems to address large ... performance characteristics, etc.). * Collaborate with the broader product, engineering, and ...

Ramp

Software Engineer, Onboarding

New York, NY · On-site

$168K - $275K/yr

As an engineer here, you'll own workstreams that expand Ramp's total serviceable market and improve ... Build software to grow Ramp to its next millions of users * Scale the output of our application ...

New

Ramp

Software Engineer, Onboarding

New York, NY · Remote

$168K - $275K/yr

As an engineer here, you'll own workstreams that expand Ramp's total serviceable market and improve ... Build software to grow Ramp to its next millions of users * Scale the output of our application ...

New

Alignerr

Machine Learning Engineer

Manhattan, NY · Remote

$154K/yr

Professional background in Machine Learning, Software Engineering, or a related technical discipline * Exceptional ability to articulate complex reasoning processes in a clear, structured, and ...

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Software Performance Engineer information

See New York salary details

$119.3K

$154.3K

How much do software performance engineer jobs pay per year?

As of Jun 10, 2026, the average yearly pay for software performance engineer in New York is $152,649.00, according to ZipRecruiter salary data. Most workers in this role earn between $153,200.00 and $153,200.00 per year, depending on experience, location, and employer.

What are the key skills and qualifications needed to thrive as a Software Performance Engineer, and why are they important?

To thrive as a Software Performance Engineer, you need a solid background in computer science, strong programming skills (often in languages like Java, C++, or Python), and experience in analyzing and optimizing software performance. Familiarity with profiling tools (such as JProfiler, VisualVM, or Perf), performance testing frameworks (like JMeter or LoadRunner), and knowledge of system monitoring solutions are typically required. Analytical thinking, problem-solving, and clear communication are standout soft skills for diagnosing issues and collaborating with development teams. These competencies ensure that applications run efficiently and reliably, directly impacting user satisfaction and system scalability.

What are Software Performance Engineers?

Software Performance Engineers are specialists who focus on ensuring that software applications run efficiently and meet performance requirements. They analyze system bottlenecks, optimize code and system configurations, and conduct performance testing to identify and resolve issues such as slow response times or high resource usage. Their work helps improve user experience and system reliability, especially for applications expected to handle large numbers of users or complex computations. Software Performance Engineers often collaborate with developers, QA teams, and system administrators to maintain and enhance application performance throughout the software development lifecycle.

What is the difference between Software Performance Engineer vs Software Quality Assurance Engineer?

AspectSoftware Performance EngineerSoftware Quality Assurance Engineer
Primary FocusOptimizing software speed, scalability, and efficiencyEnsuring software meets quality standards and is bug-free
Skills & CertificationsPerformance testing, profiling, scripting, knowledge of performance toolsTesting methodologies, defect tracking, automation tools, ISTQB certification
Work EnvironmentDevelopment teams, performance testing labs, cloud environmentsTesting teams, QA labs, cross-functional project teams
Industry UsageTech, finance, e-commerce, gamingSoftware development, enterprise applications, healthcare

While both roles focus on software quality, the Software Performance Engineer specializes in optimizing system performance and scalability, whereas the Software Quality Assurance Engineer concentrates on overall quality assurance and defect prevention. Understanding these differences helps employers and professionals align skills with job requirements.

What are some common challenges faced by Software Performance Engineers when optimizing large-scale applications?

Software Performance Engineers often encounter challenges such as identifying performance bottlenecks in complex, distributed systems and ensuring scalability as user demand grows. Troubleshooting issues may require deep dives into both application code and infrastructure layers, often under tight deadlines. Collaboration with development, QA, and operations teams is essential to implement performance improvements without disrupting existing functionality. Staying up-to-date with evolving technologies and profiling tools is also key to maintaining optimal performance.
What are popular job titles related to Software Performance Engineer jobs in New York? For Software Performance Engineer jobs in New York, the most frequently searched job titles are:
What job categories do people searching Software Performance Engineer jobs in New York look for? The top searched job categories for Software Performance Engineer jobs in New York are:
What are popular job titles related to Software Performance Engineer jobs in NY? For Software Performance Engineer jobs in NY, the most frequently searched job titles are:
Software Performance Engineer jobs near you
Infographic showing various Software Performance Engineer job openings in New York as of June 2026, with employment types broken down into 90% Full Time, and 10% Contract. Highlights an 80% In-person, and 20% Remote job distribution, with an average salary of $152,649 per year, or $73.4 per hour.
Machine Learning Performance Engineer

Machine Learning Performance Engineer

Jane Street

New York, NY • On-site

$153K/yr

Full-time

Posted 9 hours ago

Job description

We are looking for an engineer with experience in low-level systems programming and optimisation to join our growing ML team.
Machine learning is a critical pillar of Jane Street's global business. Our ever-evolving trading environment serves as a unique, rapid-feedback platform for ML experimentation, allowing us to incorporate new ideas with relatively little friction.
Your part here is optimising the performance of our models - both training and inference. We care about efficient large-scale training, low-latency inference in real-time systems and high-throughput inference in research. Part of this is improving straightforward CUDA, but the interesting part needs a whole-systems approach, including storage systems, networking and host- and GPU-level considerations. Zooming in, we also want to ensure our platform makes sense even at the lowest level - is all that throughput actually goodput? Does loading that vector from the L2 cache really take that long?
If you've never thought about a career in finance, you're in good company. Many of us were in the same position before working here. If you have a curious mind and a passion for solving interesting problems, we have a feeling you'll fit right in.
There's no fixed set of skills, but here are some of the things we're looking for:
  • An understanding of modern ML techniques and toolsets
  • The experience and systems knowledge required to debug a training run's performance end to end
  • Low-level GPU knowledge of PTX, SASS, warps, cooperative groups, Tensor Cores and the memory hierarchy
  • Debugging and optimisation experience using tools like CUDA GDB, NSight Systems, NSight Computesight-systems and nsight-compute
  • Library knowledge of Triton, CUTLASS, CUB, Thrust, cuDNN and cuBLAS
  • Intuition about the latency and throughput characteristics of CUDA graph launch, tensor core arithmetic, warp-level synchronization and asynchronous memory loads
  • Background in Infiniband, RoCE, GPUDirect, PXN, rail optimisation and NVLink, and how to use these networking technologies to link up GPU clusters
  • An understanding of the collective algorithms supporting distributed GPU training in NCCL or MPI
  • An inventive approach and the willingness to ask hard questions about whether we're taking the right approaches and using the right tools
  • Fluent in English

If you're a recruiting agency and want to partner with us, please reach out to agency-partnerships@janestreet.com.

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