Machine Learning Performance Engineer, Training

Tower Research Capital • New York, NY

1h • Hybrid

About The Position

This role bridges the gap between quantitative research and high-performance computing, focusing on building and optimizing systems for large-scale machine learning model training. The primary goal is to accelerate the entire training lifecycle, from data ingestion and distributed execution to kernel performance and hardware utilization. This will enable researchers to iterate more quickly on complex models and datasets. The position involves analyzing and improving training pipelines, optimizing distributed strategies, developing GPU kernels, and applying model/numerical optimization techniques. Collaboration with various teams, including ML Researchers, Quantitative Researchers, HPC Engineers, and Systems Engineers, is crucial to translate research requirements into efficient training systems.

Requirements

• 3+ years of experience optimizing machine learning training workloads in high-performance, distributed, or large-scale computing environments.
• Deep knowledge of machine learning frameworks such as PyTorch or JAX, including their execution models, compilation paths, autograd systems, and distributed-training capabilities.
• Strong programming skills in Python and C++, with experience developing or optimizing performance-critical systems.
• Proven experience with GPU kernel development and optimization using technologies such as CUDA, Triton, CUTLASS, cuBLAS, cuDNN, or related libraries.
• Strong understanding of GPU architecture, including streaming multiprocessor execution, warp scheduling, tensor cores, and the memory hierarchy from registers through HBM.
• Experience with distributed-training technologies and communication libraries such as NCCL, FSDP, DeepSpeed, Megatron-LM, XLA, or equivalent systems.
• Proficiency with performance-analysis tools such as Nsight Systems, Nsight Compute, PyTorch Profiler, or comparable tracing and profiling platforms.
• Understanding of high-performance networking, storage, and accelerator interconnects, including technologies such as InfiniBand, RDMA, NVLink, or NVSwitch.
• Demonstrated ability to benchmark heterogeneous compute platforms and make rigorous, data-driven recommendations about performance, scalability, and cost.

Nice To Haves

• Experience optimizing training workloads for transformer-based, time-series, reinforcement-learning, or other computationally intensive models.
• Experience with cluster orchestration and scheduling technologies such as Kubernetes, Slurm, Ray, or similar platforms.
• Familiarity with fault-tolerant distributed training, large-scale checkpointing, experiment reproducibility, and GPU-cluster observability.
• Practical experience with specialized accelerators, custom hardware, or compiler technologies for machine learning.
• Prior experience in financial trading is not required.

Responsibilities

• Benchmark model-training workloads across CPUs, GPUs, and other accelerator platforms to identify bottlenecks and guide compute infrastructure decisions.
• Develop performance models and standardized benchmarks for measuring throughput, utilization, scalability, and time to convergence.
• Design and optimize distributed training strategies, including data, tensor, pipeline, and model parallelism.
• Improve communication efficiency across multi-GPU and multi-node environments by optimizing collective operations, topology awareness, and computation–communication overlap.
• Analyze and improve the full training pipeline, including data loading, preprocessing, memory management, forward and backward passes, optimizer execution, checkpointing, and experiment recovery.
• Identify bottlenecks across compute, memory, storage, networking, and interconnects to increase accelerator utilization and researcher productivity.
• Develop and optimize GPU kernels and performance-critical framework components for quantitative machine learning workloads.
• Integrate specialized libraries, compilers, and execution techniques to improve throughput, memory efficiency, and numerical performance.
• Apply techniques such as mixed-precision training, gradient accumulation, activation checkpointing, operator fusion, and memory-efficient optimizers.
• Evaluate tradeoffs among training speed, numerical stability, reproducibility, model quality, and infrastructure cost.
• Partner with HPC and infrastructure teams to optimize workload scheduling, resource allocation, observability, fault tolerance, and reproducibility across shared compute environments.
• Help define the architecture and tooling required to support large-scale experimentation across on-premises and cloud-based infrastructure.
• Work closely with ML Researchers, Quantitative Researchers, HPC Engineers, Systems Engineers, and hardware specialists to translate research requirements into highly efficient training systems.

Benefits

• Generous paid time off policies
• Savings plans and other financial wellness tools available in each region
• Hybrid working opportunities
• Free breakfast, lunch and snacks daily
• In-office wellness experiences and reimbursement for select wellness expenses (e.g., gym, personal training and more)
• Volunteer opportunities and charitable giving
• Social events, happy hours, treats and celebrations throughout the year
• Workshops and continuous learning opportunities

What This Job Offers

• Job Type: Full-time
• Career Level: Senior
• Education Level: No Education Listed