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gimlet

Member of Technical Staff - ML Systems & Inference

Gimlet is seeking a talented Member of Technical Staff (MTS) to build and scale critical Machine Learning systems and inference pipelines. You will be instrumental in taking cutting-edge research models and deploying them reliably at scale. Key responsibilities include designing robust MLOps frameworks, optimizing model serving latency, and ensuring the scalability of our ML infrastructure using modern cloud technologies. Ideal candidates possess strong software engineering skills coupled with deep knowledge of ML systems (e.g., PyTorch, TensorFlow) and experience working in a high-throughput, low-latency environment. You must be passionate about bridging the gap between research prototypes and production-grade, resilient services.

gimlet San Francisco, CA, United States

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

Preparing

1800

Question set

250

Duration

Permanent

Focus areas

Machine LearningMLOpsPyTorchKubernetesDistributed Systems

People Also Ask (FAQs)

What is the last date to apply for Member of Technical Staff - ML Systems & Inference?

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

How do I apply for Member of Technical Staff - ML Systems & Inference?

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 gimlet 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 Systems & Inference 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: Machine Learning, MLOps, PyTorch, Kubernetes, Distributed Systems.

Quick start

Review the job summary, then jump into prep with the same flow used for exam starts.

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