ML Systems & Performance Engineer
Join the Engram team as an ML Systems & Performance Engineer to tackle complex challenges in optimizing our machine learning infrastructure. This role focuses heavily on performance tuning, ensuring that our models run efficiently at scale across distributed systems. You will be responsible for enhancing the entire MLOps lifecycle, from data ingestion through model serving. Key tasks include implementing scalable performance testing frameworks, refactoring resource-intensive components written in Scala, and optimizing hardware utilization within cloud environments. Candidates should possess deep knowledge of both ML principles and high-performance computing concepts. If you are passionate about making complex AI systems fast, reliable, and robust, this role offers the chance to drive significant architectural improvements across Engram’s product suite.
(تازہ ترین نوکریاں، سلیبس اور ٹیسٹ کی تیاری کی مکمل معلومات)
Preparing
2100
Question set
300
Duration
Permanent
Focus areas
People Also Ask (FAQs)
What is the last date to apply for ML Systems & Performance Engineer?
The closing date is listed in the official advertisement — apply as early as possible.
How do I apply for ML Systems & Performance Engineer?
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 engram 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 ML Systems & Performance Engineer 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 Systems, Performance Optimization, Distributed Computing, Scala, PyTorch.
Quick start
Review the job summary, then jump into prep with the same flow used for exam starts.