עדיין מחפשים עבודה במנועי חיפוש? הגיע הזמן להשתדרג!
במקום לעבור לבד על אלפי מודעות, Jobify מנתחת את קורות החיים שלך ומציגה לך רק משרות שבאמת מתאימות לך.
מעל 80,000 משרות • 4,000 חדשות ביום
חינם. בלי פרסומות. בלי אותיות קטנות.
While vision and language models have become increasingly commoditized, Finals proprietary deep learning models are unique, fast-evolving, and deployed in live trading across the worlds most efficient and sophisticated financial markets. Operating in this environment presents distinct scaling challenges and continuous opportunities for optimization. Success in this role requires first-principles thinking and a deep understanding of the engineering trade-offs behind high-performance DL systems.
This is a pivotal role within Finals engineering organization. You will work closely with researchers and engineers across the company, running deep learning models on massive compute clusters and adapting them for production serving under strict and non-trivial constraints.
Requirements
B.Sc. with honors in CS/EE/Math/Physics, or a related field from a top-tier university.
5+ years of hands-on experience building and deploying large-scale deep learning systems in production.
Advanced proficiency in PyTorch/TensorFlow.
Preferred Qualifications :
M.Sc. or Ph.D. in a relevant quantitative field - Advantage.
Proficiency in Python/C/C++.
Deep, working knowledge of PyTorch internals.
Strong experience in several of the following areas:
Performance profiling and optimization of deep learning workloads.
Orchestrating and optimizing large-scale distributed training (hundreds to thousands of GPUs).
Optimizing model serving and inference pipelines (quantization, distillation, compilation, memory optimization, etc.).
Training and scaling state-of-the-art vision, language, or diffusion models.
Implementing custom CUDA/Triton kernels.
במקום לעבור לבד על אלפי מודעות, Jobify מנתחת את קורות החיים שלך ומציגה לך רק משרות שבאמת מתאימות לך.
מעל 80,000 משרות • 4,000 חדשות ביום
חינם. בלי פרסומות. בלי אותיות קטנות.
ערב