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Reading list

Machine Learning Basics for a Newbie
6 Steps of Machine learning Lifecycle Introduction to Predictive Modeling
Introduction to Exploratory Data Analysis & Data Insights Descriptive Statistics Inferential Statistics How to Understand Population Distributions?
Reading Data Files into Python Different Variable Datatypes
Probability for Data Science Basic Concepts of Probability Axioms of Probability Conditional Probability
Central Tendencies for Continuous Variables Spread of Data KDE plots for Continuous Variable Overview of Distribution for Continuous variables Normal Distribution Skewed Distribution Skeweness and Kurtosis Distribution for Continuous Variable
Central Tendencies for Categorical Variables Understanding Discrete Distributions Performing EDA on Categorical Variables
Dealing with Missing Values Understanding Outliers Identifying Outliers in Data Outlier Detection in Python Outliers Detection Using IQR, Z-score, LOF and DBSCAN
Sample and Population Central Limit Theorem Confidence Interval and Margin of Error
Bivariate Analysis Introduction
Covariance Pearson Correlation Spearman's Correlation & Kendall's Tau Correlation versus Causation Tabular and Graphical methods for Bivariate Analysis Performing Bivariate Analysis on Continuous-Continuous Variables
Tabular and Graphical methods for Continuous-Categorical Variables Introduction to Hypothesis Testing P-value Two sample Z-test T-test T-test vs Z-test Performing Bivariate Analysis on Continuous-Catagorical variables
Chi-Squares Test Bivariate Analysis on Categorical Categorical Variables
Multivariate Analysis A Comprehensive Guide to Data Exploration The Data Science behind IPL
Supervised Learning vs Unsupervised Learning Reinforcement Learning Generative and Descriminative Models Parametric and Non Parametric model
Machine Learning Pipeline Preparing Dataset Build a Benchmark Model: Regression Build a Benchmark Model: Classification
Evaluation Metrics for Machine Learning Everyone should know Confusion Matrix Accuracy Precision and Recall AUC-ROC Log Loss R2 and Adjusted R2
Dealing with Missing Values Replacing Missing Values Imputing Missing Values in Data Working with Categorical Variables Working with Outliers Preprocessing Data for Model Building
Understanding Cost Function Understanding Gradient Descent Math Behind Gradient Descent Assumptions of Linear Regression Implement Linear Regression from Scratch Train Linear Regression in Python Implementing Linear Regression in R Diagnosing Residual Plots in Linear Regression Models Generalized Linear Models Introduction to Logistic Regression Odds Ratio Implementing Logistic Regression from Scratch Introduction to Scikit-learn in Python Train Logistic Regression in python Multiclass using Logistic Regression How to use Multinomial and Ordinal Logistic Regression in R ? Challenges with Linear Regression Introduction to Regularisation Implementing Regularisation Ridge Regression Lasso Regression
Introduction to K Nearest Neighbours Determining the Right Value of K in KNN Implement KNN from Scratch Implement KNN in Python
Bias Variance Tradeoff Introduction to Overfitting and Underfitting Visualizing Overfitting and Underfitting Selecting the Right Model What is Validation? Hold-Out Validation Understanding K Fold Cross Validation
Introduction to Feature Selection Feature Selection Algorithms Missing Value Ratio Low Variance Filter High Correlation Filter Backward Feature Elimination Forward Feature Selection Implement Feature Selection in Python Implement Feature Selection in R
Introduction to Decision Tree Purity in Decision Tree Terminologies Related to Decision Tree How to Select Best Split Point in Decision Tree? Chi-Squares Information Gain Reduction in Variance Optimizing Performance of Decision Tree Train Decision Tree using Scikit Learn Pruning of Decision Trees
Introduction to Feature Engineering Feature Transformation Feature Scaling Feature Engineering Frequency Encoding Automated Feature Engineering: Feature Tools
Introduction to Naive Bayes Conditional Probability and Bayes Theorem Introduction to Bayesian Adjustment Rating: The Incredible Concept Behind Online Ratings! Working of Naive Bayes Math behind Naive Bayes Types of Naive Bayes Implementation of Naive Bayes
Understanding how to solve Multiclass and Multilabled Classification Problem Evaluation Metrics: Multi Class Classification
Introduction to Ensemble Techniques Basic Ensemble Techniques Implementing Basic Ensemble Techniques Finding Optimal Weights of Ensemble Learner using Neural Network Why Ensemble Models Work well?
Introduction to Stacking Implementing Stacking Variants of Stacking Implementing Variants of Stacking Introduction to Blending Bootstrap Sampling Introduction to Random Sampling Hyper-parameters of Random Forest Implementing Random Forest Out-of-Bag (OOB) Score in the Random Forest IPL Team Win Prediction Project Using Machine Learning Introduction to Boosting Gradient Boosting Algorithm Math behind GBM Implementing GBM in python Regularized Greedy Forests Extreme Gradient Boosting Implementing XGBM in python Tuning Hyperparameters of XGBoost in Python Implement XGBM in R/H2O Adaptive Boosting Implementing Adaptive Boosing LightGBM Implementing LightGBM in Python Catboost Implementing Catboost in Python
Different Hyperparameter Tuning methods Implementing Different Hyperparameter Tuning methods GridsearchCV RandomizedsearchCV Bayesian Optimization for Hyperparameter Tuning Hyperopt
Understanding SVM Algorithm SVM Kernels In-depth Intuition and Practical Implementation SVM Kernel Tricks Kernels and Hyperparameters in SVM Implementing SVM from Scratch in Python and R
Introduction to Principal Component Analysis Steps to Perform Principal Compound Analysis Computation of Covariance Matrix Finding Eigenvectors and Eigenvalues Implementing PCA in python Visualizing PCA A Brief Introduction to Linear Discriminant Analysis Introduction to Factor Analysis
Introduction to Clustering Applications of Clustering Evaluation Metrics for Clustering Understanding K-Means Implementation of K-Means in Python Implementation of K-Means in R Choosing Right Value for K Profiling Market Segments using K-Means Clustering Hierarchical Clustering Implementation of Hierarchial Clustering DBSCAN Defining Similarity between clusters Build Better and Accurate Clusters with Gaussian Mixture Models
Understand Basics of Recommendation Engine with Case Study
8 Ways to Improve Accuracy of Machine Learning Models
Introduction to Dask Working with CuML
Introduction to Machine Learning Interpretability Framework and Interpretable Models model Agnostic Methods for Interpretability Implementing Interpretable Model Understanding SHAP Out-of-Core ML Introduction to Interpretable Machine Learning Models Model Agnostic Methods for Interpretability Game Theory & Shapley Values
Introduction to Machine Learning Interpretability Framework and Interpretable Models model Agnostic Methods for Interpretability Implementing Interpretable Model Understanding SHAP Out-of-Core ML Introduction to Interpretable Machine Learning Models Model Agnostic Methods for Interpretability Game Theory & Shapley Values
Introduction to AutoML Implementation of MLBox Introduction to PyCaret TPOT Auto-Sklearn EvalML
Pickle and Joblib Introduction to Model Deployment
Deploying Machine Learning Model using Streamlit Deploying ML Models in Docker Deploy Using Streamlit Deploy on Heroku Deploy Using Netlify Introduction to Amazon Sagemaker Setting up Amazon SageMaker Using SageMaker Endpoint to Generate Inference Deploy on Microsoft Azure Cloud Introduction to Flask for Model Deploying ML model using Flask
Model Deployment in Android Model Deployment in Iphone

Robotics Engineer Salary, Responsibilities, Skills & More

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Analytics Vidhya Last Updated : 14 Sep, 2023
5 min read

Robotic engineering is a rapidly growing field with exciting opportunities. If you are interested in robotics and are considering a career as a robotics engineer, it’s natural to wonder about the salary prospects in this field. In this article, we will explore robotics engineer salary, providing valuable insights into their earning potential.

Table of contents

What is Robotics Engineering?

Robotics is the base of the currently developing era of automation and technology. With the capability of performing numerous functions and easing life, the domain has also increased employment. Robotics engineers are the masterminds behind the best robot development for specific industries. They work with cutting-edge technology, such as Artificial Intelligence, Natural Language Processing, and Machine Learning. Robotic engineers earn lucrative salaries owing to their educational background in computer science, mechanical, robotics and automation and similar other fields, along with expertise. Discover the salary trends in different countries and the factors that impact them.

How to become robotics engineer?
Source: Columbia University

Average Robotics Engineer Salary in India

The salary of an entry-level robotics engineer is around INR 2 lakhs, while the highly experienced robotics software engineer’s salary is around INR 12 lakhs. The mid-level robotics engineer with medium experience in India earns about INR 5 lakhs. The additional cash compensation will be around INR 17,222, while the maximum is up to INR 1,60,000. Average robotics software engineer salary in India is INR 3.5 lakhs annually.

Average Robotics Engineer Salary in Other Countries

USA

The robotics programming salary in the United States is about $70,000 for an entry-level employee while around $1,44,000 yearly for a highly experienced robotics engineer. Candidates with a medium level of experience can expect a salary of around $101,208 annually. The highest-paying state is Nevada, the city is San Francisco, CA, and the highest-paying company is Meta.

Australia

Australian $80,000 per year. The entry-level salary is about $45,000, and the experience-level salary is $1lakhs.

UK

The fresher candidate can expect around £25000 as a robotics salary initially. Further, the robotics salary goes to about £59,000 after attaining expertise. The average base salary is approximately £39,000 for mid-level robotic engineers.

Factors Affecting Robotics Engineer Salary

The salary of a robotics engineer can be influenced by several factors, including:

Education and Experience

The salary section highlights the requirement for a better education level and experience. The maximum salaries are worth giving your hard work and time to the field. Higher degrees like Master’s and Ph.D. pose more benefits, while the experience of 10 to 15 years allows for earning a high position and robotics salary.

Industry and Company

The aerospace, military, and healthcare industries pay robotics and automation salaries more than other robotic industries. Similarly, the salary difference for similar experiences varies among companies. Candidates with 0 to 7 years of experience earn about INR 2.5 lakhs to 3.5 lakhs depending on the company.

Location

The robotics salary varies among different states and countries. The technology hub pays a higher average robotics engineer salary than general industries in non-technical hub areas or rural regions. Smaller cities also pay according to the location, which is lower than an employee working in metropolitan cities.

Certification

A specialization certificate in robotics, programming, control systems, embedded systems, and user interface will definitely increase the expected earnings of robotics engineers. The robotics and automation salary, robotics programming salary and in other fields will also be a preference to such candidates based on the job role.

Job Roles in Robotics Engineering

The average robotics engineer salary for each role is as follows:

Job roleAverage SalaryResponsibilities
Robotics TrainerINR 2.7 lakhsEducate the teams about concepts, practices and technologies concerning robots. It is an academic role.
Robotic Process Automation DeveloperINR 5.4 lakhsResponsible for creating tools and programs to automate repetitive tasks and processes. They design, develop and test RPA solutions per the client’s requirements. They also integrate RPA with different systems.
Senior Robotics EngineerINR 6 lakhs The job profile focuses on designing, developing and testing robotic systems, controls and devices. Their role might be specific or in combination concerning the mechanical, software, design, electrical or other relevant branches.
Automation EngineerINR 5.7 lakhs Serving similar roles, they are also directly associated with developing control software and focusing on safety, efficacy, efficiency and compliance standards.
Senior Software EngineerINR 14 lakhsThese are more closely concerned with programming, bugs, the robot’s operating system, user interface and similar tasks.
Embedded Systems EngineerINR 4 lakhs They design embedded systems, hardware and software, integrating systems and related systems. The embedded systems are incorporated into the machine and devices. The engineer also checks the safety and reliability of their designs.
Senior Developer/ProgrammerINR 7.4 lakhsThey hold expertise in developing multiple software applications and rectifying errors. They often lead the teams and deal with multiple projects at a time. Their work also requires collaboration between different departments and understanding their workings, which is required in their projects.

Skills and Education Required for Each Role

  • Robotics Trainer: At least a bachelor’s degree in engineering, robotics or another relevant field. Communication skills, passion for teaching, dealing with academic works, research interest and will to discover or invent new things.
  • Robotic Process Automation (RPA) Developer: At least a bachelor’s degree in computer science, engineering, or another related field. Experience working on
  • Automation Engineer: One can begin their career through a bachelor’s degree in automation, mechanical or electrical engineering. Strong problem-solving skills, the ability to work with automation tools and technologies and the capability to design new such systems are some of the required skills.
  • Robotics Engineer: The education and skills requirements are similar to automation engineers. The additional requirements are the ability to design, test and integrate the robotic components. Passion for developing robots will take people a long way into the field.
  • Software Engineer: A bachelor’s degree in computer science is a must in combination with a command of programming languages. Java and C++ are among the most desirable languages. Besides, experience in working with robotic systems is also required.
  • Embedded Systems Engineer: Besides a bachelor’s degree in computer, electrical and other related engineering, experience is a must. Handling operating systems, microprocessors, software optimization skills, real-time operating systems and others are required in embedded systems engineering.
  • Senior Developer/Programmer: At least a bachelor’s degree in computer science. Required skills include understanding the user demands, analytical thinking, dealing with SQL queries, mathematical skills and problem-solving capacity.

Become a Robotics Engineer Today

Robotics engineers are one of the highly demanded professionals. There is a rising usage and incorporation of robotics and automation in daily life and all industries. Consequently, skilled and experienced employees who can innovate and optimize are required. Our Blackbelt AI ML program provides you with the combination of the right tools, guidance and expert educators to fulfill your dreams. So why wait for what you want to be? Enroll today!

Frequently Asked Questions

Q1. How much do robotics engineers earn?

A. Robotics engineers are in high demand and the average salary of a robotics engineer in India is ₹4,53,236 per annum. Entry-level employees earn around ₹2,50,000 per year, have excellent opportunities to grow their careers and make even more money as they gain experience.

Q2. What is the highest salary of a robotics engineer? 

A. According to Ambition Box, the highest salary that an Automation and Robotics Engineer can earn is ₹16.3 Lakhs per year.

Q3. What is the salary of a robotics engineer in ISRO?

A. The average Engineer salary at the Department of Space, ISRO is ₹9,00,000 per annum. Engineer salaries at ISRO can range from ₹1,88,000 – ₹16,33,628 per year.

Q4. Is robotics a high-paying field?

A. The average annual salary for robotics engineers in the US is $85,936. Based on data from Glassdoor, robotics engineers also receive an average additional compensation of $14,337, which may include profit sharing, bonuses, or commissions. Overall, the estimated annual salary for robotics engineers averages $100,273.

Q5. Which branch is best in robotics?

A. The “best” branch in robotics depends on individual interests and career goals. For example, electrical engineering can be beneficial for working with sensors and control systems, while mechanical engineering can be valuable for designing robot structures and mechanisms. Combining skills and knowledge from different branches is often advantageous in robotics.

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