Acquire Me

About the Company

We are a specialist technology recruiting firm dedicated to connecting the brightest and best STEM talent into the most exciting opportunities across the world’s most renowned scientific-led quantitative hedge funds, proprietary trading firms and high-growth start-ups. As an organisation, we seek to combine our 20+ years of human expertise in tech recruiting best practice alongside a data-led research-driven methodology to unearth, connect and engage with the most exceptional calibre of technology talent across the industry. Get in touch to learn more about how we can help with your growth plans or the search for your next role. UK: +44 (0)203 393 2490 US: +1 (646)-895-6354

Listed Jobs

Company Name

Acquire Me

Job Title

Machine Learning Performance Engineer - Quant Research & Trading

Job Description

**Job title:** Machine Learning Performance Engineer – Quant Research & Trading **Role Summary:** Design, tune, and deploy large‑scale deep learning and transformer pipelines for systematic trading. Optimize GPU‑accelerated training and inference to convert research models into production systems that directly influence trading performance. **Expectations:** - Deliver consistently high throughput and low latency on GPU‑based workloads. - Identify and eliminate bottlenecks in distributed training and inference. - Translate research outputs into robust, deployable systems. - Produce measurable performance improvements and report impact. **Key Responsibilities:** - Build and optimize end‑to‑end ML training and inference pipelines. - Enhance deep learning frameworks (PyTorch, JAX, TensorFlow) for GPU efficiency. - Debug and resolve GPU, memory, and distributed training performance issues. - Collaborate with researchers to deploy models into live trading infrastructure. - Profile and benchmark models; recommend architectural or code‑level optimizations. **Required Skills:** - Strong understanding of ML fundamentals: transformers, LLMs, attention mechanisms, RLHF. - Advanced GPU programming: CUDA, Tensor Cores, warp‑level operations. - Proficient in Python and C++ (or equivalent compiled language). - Experience with deep‑learning libraries: PyTorch, JAX (TensorFlow optional). - Familiarity with GPU libraries/tools: Triton, CUB, CuDNN, cuBLAS. - Experience with distributed training frameworks (e.g., Horovod, MPI, NCCL). - Proficient in profiling/debugging tools (Nsight, nvprof, torch.profiler). **Required Education & Certifications:** - Bachelor’s or Master’s degree in Computer Science, Electrical Engineering, Applied Mathematics, or related field. - Minimum 3 years of professional experience in machine‑learning engineering or performance optimization.

New york city, United states

Hybrid

25-11-2025

Company Name

Acquire Me

Job Title

Deep Learning Researcher – Quantitative Trading

Job Description

Job title: Deep Learning Researcher – Quantitative Trading Role Summary: Lead the research, development, and deployment of deep learning and large language model (LLM) solutions that directly influence trading strategy performance, cost efficiency, and execution quality. Expactations: - Deliver end‑to‑end research from hypothesis to production within an agile, high‑impact environment. - Translate complex research ideas into scalable, high‑performance models suitable for live trading workloads. - Demonstrate measurable financial impact through model improvements and operational efficiencies. Key Responsibilities: - Conduct pioneering machine learning research focused on deep learning and LLM architectures. - Design, prototype, and iterate models, optimizing for accuracy, speed, and resource utilization. - Build and maintain production pipelines that support training, validation, deployment, and monitoring of models in real‑time trading systems. - Collaborate with quantitative trading teams, data scientists, and engineering to integrate models into trading strategies. - Lead open‑ended research projects, establishing proof‑of‑concepts that evolve into production solutions. - Maintain up‑to‑date knowledge of emerging ML techniques and industry best practices. Required Skills: - Minimum 5 years of experience transitioning research into production‑grade systems. - Deep expertise in LLMs and advanced deep learning architectures. - Proven training and optimization experience, including hyper‑parameter tuning, model compression, and inference acceleration. - Strong proficiency in Python and C++; fluency with ML frameworks such as PyTorch, TensorFlow, or JAX. - Solid statistical and mathematical foundation (probability, statistics, linear algebra, optimization). - Demonstrated ability to communicate complex technical concepts to cross‑functional teams. Required Education & Certifications: - PhD in Computer Science, Electrical Engineering, Statistics, or related STEM discipline **or** Master’s degree with a strong record of publications in machine learning or deep learning.

New york city, United states

Hybrid

25-11-2025