---
title: 'Machine Learning Performance Engineer, Training at Tower Research Capital'
canonical: 'https://feeny.ai/job/machine-learning-performance-engineer-training-tower-research-capital-new-york-4y261fqmg1k7'
type: 'job'
last_seen: '2026-10-13'
---

# Machine Learning Performance Engineer, Training at Tower Research Capital

- **Company:** Tower Research Capital
- **Location:** New York, NY
- **Posted:** 2026-09-24
- **Last confirmed live:** 2026-10-13
- **Apply:** https://www.tower-research.com/open-positions/?gh_jid=8230413

## Job description

Tower Research Capital is a leading quantitative trading firm founded in 1998. Tower has built its business on a high-performance platform and independent trading teams. We have a 25+ year track record of innovation and a reputation for discovering unique market opportunities.

Tower is home to some of the world’s best systematic trading and engineering talent. We empower portfolio managers to build their teams and strategies independently while providing the economies of scale that come from a large, global organization.

Engineers thrive at Tower while developing electronic trading infrastructure at a world class level. Our engineers solve challenging problems in the realms of low-latency programming, FPGA technology, hardware acceleration and machine learning. Our ongoing investment in top engineering talent and technology ensures our platform remains unmatched in terms of functionality, scalability and performance.

At Tower, every employee plays a role in our success. Our Business Support teams are essential to building and maintaining the platform that powers everything we do — combining market access, data, compute, and research infrastructure with risk management, compliance, and a full suite of business services. Our Business Support teams enable our trading and engineering teams to perform at their best.

At Tower, employees will find a stimulating, results-oriented environment where highly intelligent and motivated colleagues inspire each other to reach their greatest potential.

Summary

You will bridge the gap between quantitative research and high-performance computing, building and optimizing the systems used to train machine learning models at scale. You will focus on accelerating the end-to-end training lifecycle—from data ingestion and distributed execution to kernel performance and hardware utilization—enabling researchers to iterate more quickly across increasingly complex models and datasets.

## Responsibilities

- Training Performance and Benchmarking
- Benchmark model-training workloads across CPUs, GPUs, and other accelerator platforms to identify bottlenecks and guide Tower’s compute infrastructure decisions.
- Develop performance models and standardized benchmarks for measuring throughput, utilization, scalability, and time to convergence.
- Distributed Training Optimization
- 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.
- End-to-End Training Efficiency
- 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.
- GPU Kernel and Framework Development
- 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.
- Model and Numerical Optimization
- 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.
- Training Infrastructure
- 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.
- Cross-Functional Collaboration
- Work closely with ML Researchers, Quantitative Researchers, HPC Engineers, Systems Engineers, and hardware specialists to translate research requirements into highly efficient training systems.

## Qualifications

- 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.

## Preferred Qualifications

- 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.

Anticipated New York annual base salary of $200,000, plus eligible for discretionary bonus.

## Benefits

Tower’s headquarters are in the historic Equitable Building, right in the heart of NYC’s Financial District and our impact is global, with over a dozen offices around the world.

At Tower, we believe work should be both challenging and enjoyable. That is why we foster a culture where smart, driven people thrive – without the egos. Our open concept workplace, casual dress code, and well-stocked kitchens reflect the value we place on a friendly, collaborative environment where everyone is respected, and great ideas win.

Our benefits include:

- 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

At Tower, you’ll find a collaborative and welcoming culture, a diverse team and a workplace that values both performance and enjoyment. No unnecessary hierarchy. No ego. Just great people doing great work – together.

Tower Research Capital is an equal opportunity employer.

## About Tower Research Capital

## Company Overview
- **One-liner**: Tower Research Capital is an American quantitative trading firm that builds high-performance technology platforms for systematic trading across all asset classes, markets, and time horizons.
- **Entity Type**: Private (Privately Held)
- **Headquarters**: New York, New York, United States
- **Founded**: 1998
- **Founders**: Mark Gorton, Alistair Brown

## Core Business
- **Primary industry**: Financial Services (Quantitative Trading, High-Frequency Trading, Liquidity Provision)
- **Target customers**: Institutional (the firm trades for its own capital; it provides a platform for systematic portfolio managers and quantitative trading teams)
- **Mission or purpose statement**: To create an environment that empowers quantitative trading teams to innovate and reach their greatest potential, powered by a high-performance technology platform [tower-research.com](https://tower-research.com/)

## Products & Services
- **Quantitative Trading Platform**: A proprietary, high-performance technology stack supporting every aspect of trading and research — market access, data management, quantitative research, compute infrastructure, compliance, and risk management.
- **Systematic Portfolio Management**: A platform that allows dozens of independent trading and research teams to build and execute strategies while benefiting from economies of scale.
- **Liquidity Provision**: Market-making and liquidity services across a massive range of asset classes, products, regions, and venues.
- **Tower Ventures**: An investment arm for evaluating business opportunities and supporting new ventures.

## Market Standing
- **Valuation/Market Cap**: Not publicly available (private company)
- **Key Metric**: Annual Revenue of $600.0M (estimated, per LinkedIn data) [linkedin.com](https://www.linkedin.com/company/tower-research-capital)
- **Notable Investors/Partners**: The firm is founder-owned; no external venture investors listed. Key partners include exchanges and trading venues globally.
- **Growth Signals**: Headcount of 997 employees (+15.3% YoY, +191 people); operates in 21 countries with 11 offices (New York, Chicago, South Charleston, Montreal, London, Amsterdam, Gurgaon, GIFT City, Singapore, Hong Kong, Shanghai); 64 active job postings; 204,460 LinkedIn followers (+20.7% yearly growth) [linkedin.com](https://www.linkedin.com/company/tower-research-capital)

## Competitive Advantages
- **25+ Year Track Record**: One of the longest-standing quantitative trading firms, with a proven ability to adapt to new market opportunities.
- **Platform-First Model**: Unlike many hedge funds, Tower provides a shared high-performance platform (technology, data, infrastructure, risk management, compliance) that allows independent trading teams to focus purely on alpha generation.
- **Continuous Investment**: Robust, ongoing investment in market access, data, research, compute infrastructure, low-latency technology, and machine learning.
- **Team-Based Structure**: Dozens of independent trading and research teams operate autonomously while benefiting from the firm’s scale, creating an entrepreneurial environment within a stable organization.
- **Cutting-Edge Engineering**: Engineers work on the latest advances in machine learning, wireless telecommunication, hardware acceleration, and low-latency programming.

## Strategic Focus
- **Talent Acquisition**: Continuous investment in top trading, engineering, and business support talent as the firm’s primary growth driver.
- **Technology Leadership**: Building the fastest, most scalable, highest-performing trading technology stack in the industry.
- **Global Expansion**: Operating across 21 countries with a growing presence in key financial hubs (GIFT City, Singapore, etc.).
- **Diversification**: Expanding across asset classes, products, regions, and venues to capture new market opportunities.

## Why Work Here
- **Culture**: Values excellence, teamwork, integrity, respect, and innovation. Described as a stimulating, results-oriented environment that values entrepreneurs and self-starters [tower-research.com](https://tower-research.com/about-us/)
- **Work Style**: Team-based structure with autonomy and independence; cross-functional collaboration encouraged. "Calm and Focus" is a stated value — the firm emphasizes maintaining composure when the unexpected occurs [tower-research.com](https://tower-research.com/careers/)
- **Remote/Hybrid**: "Possibilities for hybrid work" are mentioned as a perk [tower-research.com](https://tower-research.com/careers/)
- **Perks**:
    - Discretionary and team-based bonuses
    - 5 weeks of paid vacation per year
    - Robust medical benefits plans
    - 401(k) with company matching (US employees)
    - Complimentary in-office meals daily
    - Regular social gatherings and events
    - Professional development workshops
    - Donation matching program
- **Engineering Culture**: Engineers work on cutting-edge problems in machine learning, wireless telecom, hardware acceleration, and low-latency programming. The interview process is described as straightforward — "no gotcha questions, random logic problems, or abstract, overly academic discussions" [tower-research.com](https://tower-research.com/careers/)
- **Career Growth**: Commitment to supporting employee growth with professional development workshops and a focus on helping employees reach their full potential.

## Sources
1. [tower-research.com](https://tower-research.com/)
2. [tower-research.com/about-us/](https://tower-research.com/about-us/)
3. [tower-research.com/careers/](https://tower-research.com/careers/)
4. [linkedin.com/company/tower-research-capital](https://www.linkedin.com/company/tower-research-capital)

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