Machine Learning Engineer

Emonics LLC

📍San Jose, CA
Posted May 18, 2026
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Job Overview

Position

Machine Learning Engineer

Company

Emonics LLC

Location

San Jose, CA

Work Type

On-site

Job ID

li-4415686620

Job Description

Machine Learning Engineer — Fresher Job Description

We are hiring a Machine Learning Engineer to develop and deploy machine learning models for business applications. You will work with data scientists and software engineers to create scalable ML solutions.

Responsibilities

  • Build and train ML models

  • Clean and preprocess datasets

  • Evaluate model performance

  • Deploy machine learning pipelines

Requirements

  • Knowledge of Python and machine learning algorithms

  • Experience with Pandas, NumPy, and Scikit-learn

  • Understanding of statistics and probability

  • Basic cloud knowledge is a plus
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Interview Prep

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Key Skills

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Practice Questions

💡Technical Questions (3)
  • 1.How do you use Pandas and NumPy to clean and preprocess a raw dataset before training a machine learning model?
  • 2.Can you explain the bias-variance tradeoff and how it influences your choice of machine learning algorithms and evaluation metrics?
  • 3.Walk me through how you would build and evaluate a classification model using Scikit-learn.
🎯Behavioral Questions (3)
  • 1.Tell me about a time you worked with a team (like data scientists or software engineers) to complete a technical project.
  • 2.Describe a situation where your machine learning model did not perform as expected. How did you handle it?
  • 3.Give an example of a time you had to learn a new technical concept or tool quickly to complete a task.
🧩Situational Questions (2)
  • 1.You have trained a model that performs exceptionally well on your training data but poorly on unseen test data. What steps do you take?
  • 2.You are tasked with deploying a machine learning pipeline, but you have limited deployment experience. How would you approach this?

Resume Keywords

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Machine LearningPythonPandasNumPyScikit-learnData PreprocessingModel EvaluationML PipelinesProbabilityCloud Services

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