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odysseyml

Member of Technical Staff, ML Performance

Odysseyml is seeking a Member of Technical Staff (MTS) focused on ML Performance. This critical role involves optimizing the entire machine learning lifecycle, from model training efficiency to real-time inference speed. You will be responsible for identifying performance bottlenecks within complex deep learning models and implementing cutting-edge solutions. Key responsibilities include profiling model execution paths, applying techniques like quantization and pruning, developing optimized serving infrastructure (e.g., using Triton or ONNX Runtime), and ensuring the scalability of ML pipelines on cloud platforms. The ideal candidate has a strong foundation in both machine learning principles and high-performance computing. You must be proficient in Python and have hands-on experience with performance engineering tools to deliver enterprise-grade, efficient AI solutions.

odysseyml Palo Alto, CA, United States

(تازہ ترین نوکریاں، سلیبس اور ٹیسٹ کی تیاری کی مکمل معلومات)

bereiten sich vor

2500

Fragensatz

180

Dauer

Permanent

Schwerpunkte

MLOpsPerformance OptimizationPython/C++Deep Learning FrameworksDistributed Computing

People Also Ask (FAQs)

What is the last date to apply for Member of Technical Staff, ML Performance?

The closing date is listed in the official advertisement — apply as early as possible.

How do I apply for Member of Technical Staff, ML Performance?

Use the official apply link on this page to submit your application. A step-by-step how-to-apply guide for this job is also linked below.

Is there negative marking in the odysseyml test?

The marking rule is announced per test in the advertisement and roll-number-slip instructions. Many MCQ screening tests have no negative marking, but always verify it for your specific test.

What is the passing marks for this test?

The official passing marks are generally 50%, but merit is competitive — scoring above 75% is a safer target to secure an interview call.

How can I prepare for the Member of Technical Staff, ML Performance test?

Prepare with the free subject-wise MCQs, past papers and timed mock tests on PrepPro Academy — every question includes the answer with an explanation. Focus areas: MLOps, Performance Optimization, Python/C++, Deep Learning Frameworks, Distributed Computing.

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