Machine Learning Performance Engineer

Goliath Partners Inc.; New York (NY); On-site; USD 200,000 - 300,000; Full time

Job summary

Goliath Partners Inc. in New York is seeking a Machine Learning Performance Engineer to optimize production inference and accelerate models with custom GPU kernels.

You will tackle bottlenecks, memory behavior, and mixed-precision execution while aiming for the fastest, most reliable deployments in a fast-paced trading environment. Based in New York, you will collaborate with ML researchers, computing infrastructure, and hardware accelerators to shape model deployment strategies and ensure

Qualifications

• 2+ years improving deep learning inference performance in production environments where speed or throughput is critical.
• Strong Python and C++, with experience working beneath the high-level APIs of frameworks such as PyTorch or JAX.
• Practical experience developing custom GPU kernels and using inference optimization tools.
• Detailed knowledge of GPU execution, memory behavior, and mixed-precision computation.
• A measurement-driven approach to profiling systems and comparing performance across different hardware architectures.

Responsibilities

• Write and tune GPU kernels to accelerate production inference.
• Investigate performance bottlenecks, from memory access patterns to communication between hardware components.
• Reduce model size and computational demands while preserving numerical stability.
• Compare hardware options and help determine the best deployment approach for demanding trading workloads.
• Partner with researchers and engineering teams to bring optimized models into production.

Skills

Python

C++

PyTorch

JAX

GPU kernels

Performance profiling

Hardware acceleration

Inference optimization

GPU memory

Low latency

FPGA/ASIC experience

Measurement-driven profiling

Job description

Goliath is partnered with an established quantitative trading firm seeking an engineer to improve the performance of machine learning models in production.

Working within its central engineering group, you’ll tackle the hardware and software challenges behind fast, reliable inference. The work combines hands-on GPU optimization with broader decisions about how and where models should run, alongside specialists in machine learning, computing infrastructure, and hardware acceleration.

They are currently looking for a Machine Learning Performance Engineer based in New York.

• Write and tune GPU kernels to accelerate production inference
• Investigate performance bottlenecks, from memory access patterns to communication between hardware components
• Reduce model size and computational demands while preserving numerical stability
• Compare hardware options and help determine the best deployment approach for demanding trading workloads
• Partner with researchers and engineering teams to bring optimized models into production

Essential Skills

• 2+ years improving deep learning inference performance in production environments where speed or throughput is critical
• Strong Python and C++, with experience working beneath the high-level APIs of frameworks such as PyTorch or JAX
• Practical experience developing custom GPU kernels and using inference optimization tools
• Detailed knowledge of GPU execution, memory behavior, and mixed-precision computation
• A measurement-driven approach to profiling systems and comparing performance across different hardware architectures

Preferred Skills

• Experience deploying or optimizing models on FPGAs or ASICs
• Familiarity with inference workloads where individual predictions must be delivered with exceptionally low latency

Candidates from outside financial services are welcome; trading experience is not required.

Compensation

• $200K–$300K base salary plus discretionary bonus