Machine Learning Performance Engineer
New York, New York
- Pay
USD 200,000/year · BaseAnnual period assumed — pay source
Base Salary Range $200,000—$200,000 USD
Read the full posting- Work setup
- Unconfirmed
- Employment
- Unconfirmed
What you’ll bring
All qualificationsCore experience
- Strong knowledge of low-level GPU programming with CUDA, including Tensor Cores, cooperative groups, graphs, and warp-level intrinsics
- Experience with JAX ecosystem (XLA, Flax, etc.)
- Experience with large-scale distributed training
- Expertise in internals of deep-learning frameworks like PyTorch, JAX, TensorFlow, etc.
- Familiarity with GPU libraries and tools such as Triton, CUB, cuDNN, and cuBLAS
- Deep understanding of computer architecture
Qualification wording
Strong knowledge of low-level GPU programming with CUDA, including Tensor Cores, cooperative groups, graphs, and warp-level intrinsics
Experience with JAX ecosystem (XLA, Flax, etc.)
Experience with large-scale distributed training
Expertise in internals of deep-learning frameworks like PyTorch, JAX, TensorFlow, etc.
Familiarity with GPU libraries and tools such as Triton, CUB, cuDNN, and cuBLAS
Deep understanding of computer architecture
Tools in this posting
- PyTorch
- TensorFlow
- Python
- C++
Source — Tool mentions in context
- Strong knowledge of low-level GPU programming with CUDA, including Tensor Cores, cooperative groups, graphs, and warp-level intrinsics - Expertise in internals of deep-learning frameworks like PyTorch, JAX, TensorFlow, etc. - Deep understanding of computer architecture
- Deep understanding of computer architecture - Experience in C++ and Python Nice to have
About Optiver
At Optiver, our mission is to improve the market by injecting liquidity, providing accurate pricing, increasing transparency and stabilizing the market no matter the conditions.
In the employer’s words · Read in context
Job description
Optiver is a seeking a Machine Learning Performance Engineer to join our team, focusing on a pivotal AI initiative. This role would offer the opportunity to have significant impact across Machine Learning infrastructure, training, and inference challenges to advance our futures trading strategies.
What you'll do
As a Machine Learning Performance Engineer, your key responsibilities include:
- Building scalable and robust training and inference pipelines for deep learning
- Diving into internals of open-source deep learning frameworks and enhance their functionality
- Identifying and eliminate performance bottlenecks
- Collaborating closely with researchers and other engineers
- Developing an in-depth understanding of trading systems
What you’ll get
You’ll join a culture of collaboration and excellence, surrounded by curious thinkers and creative problem-solvers. Motivated by a passion for continuous improvement, you’ll thrive in a supportive, high-performing environment alongside talented colleagues, collectively tackling some of the toughest challenges in the financial markets.
In addition, you’ll receive:
- The opportunity to work alongside best-in-class professionals from over 40 different countries
- A highly competitive compensation package
- Global profit-sharing pool and performance-based bonus structure
- 401(k) match up to 50%
- Comprehensive health, mental, dental, vision, disability, and life coverage
- 25 paid vacation days alongside market holidays
- Extensive office perks, including breakfast, lunch and snacks, regular social events, clubs, sporting leagues and more
Who you are
- Strong knowledge of low-level GPU programming with CUDA, including Tensor Cores, cooperative groups, graphs, and warp-level intrinsics
- Expertise in internals of deep-learning frameworks like PyTorch, JAX, TensorFlow, etc.
- Deep understanding of computer architecture
- Experience in C++ and Python
Nice to have
- Experience with JAX ecosystem (XLA, Flax, etc.)
- Familiarity with GPU libraries and tools such as Triton, CUB, cuDNN, and cuBLAS
- Linux system programming experience
- Experience with large-scale distributed training
- Contributions to open-source projects related to data science and machine learning
Who we are
At Optiver, our mission is to improve the market by injecting liquidity, providing accurate pricing, increasing transparency and stabilizing the market no matter the conditions. With a focus on continuous improvement, we prioritize safeguarding the health and efficiency of the markets for all participants. As one of the largest market making institutions, we are a respected partner on 100+ exchanges across the globe.
Our differences are our edge. Optiver does not discriminate on the basis of race, religion, color, sex, gender identity, sexual orientation, age, physical or mental disability, or other legally protected characteristics.
Below is the expected base salary for this position. This is a good-faith estimate of the base pay scale for this position and offers will ultimately be determined based on experience, education, skill set, and performance in the interview process. This position will also be eligible for a discretionary bonus (if determined by Optiver) and Optiver’s benefits package with the benefits listed above.
Your next step
- Have your CV and examples of relevant work ready.
- Check the listed location, eligibility and core experience before starting.
Complete your application on www.optiver.com. The employer’s form will show what is required.
Already applied? Track this application
Source & posting history
Source notes
Source excerptsSelected passages from the saved posting. Check the full description for conditions and exceptions.
- Pay
Base Salary Range $200,000—$200,000 USD
- Location & working pattern
New York, New York
Working pattern and location restrictions need checking in the full posting.
- Work authorization
No clear work-authorization passage found. Eligibility is unconfirmed.
- Status in our records
- Active
- First seen by us
- Apr 14, 2026
- Recorded sightings
- 246
- Last seen by us
- Oct 8, 2026
These dates show when we found the listing. Check the employer’s website to confirm it is still accepting applications.
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