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
Would this interest you? If so, apply & Goliath will be in contact!
