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Hire the top 1% of Machine Learning Engineers

Maya V

Maya V

ML Engineer

9+ years experience  •  Full-time availability

Verified Skills

check Python - 9 yrs | Advanced
check Java - 3 yrs | Expert

Other Skills

TensorFlow Prolog Spark Hadoop +3
Mohammed K

Parth T

ML Engineer

7+ years experience  •  Full-time availability

Verified Skills

check scikit-learn - 7 yrs | Expert
check Prolog - 7 yrs | Advanced

Other Skills

PHP Python Pandas Java Hadoop +3
Bhushan S

Bhushan S

ML Engineer

8+ years experience  •  Full-time availability

Verified Skills

check Python - 7 yrs | Expert
check Javascript - 8 yrs | Expert

Other Skills

Scala Java Prolog Lisp C++ +3
Shubham C

Shubham C

ML Engineer

4+ years experience  •  Full-time availability

Verified Skills

check Python - 4 yrs | Expert
check Java - 3 yrs | Advanced

Other Skills

TensorFlow Hadoop Scala Lisp +3
Nihal T

Nihal T

ML Engineer

6+ years experience  •  Full-time availability

Verified Skills

check Python - 6 yrs | Expert
check Prolog - 4 yrs | Advanced

Other Skills

Java Web scraping Scala Numpy +3
Maria J

Maria J

ML Engineer

9+ years experience  •  Full-time availability

Verified Skills

check Python - 9 yrs | Advanced
check Java - 3 yrs | Expert

Other Skills

TensorFlow Prolog Spark Lisp Hadoop +3

Hire Machine Learning Engineers For Work Like

From food to FinTech, thousands of companies use Supersourcing to hire, scale and grow faster.

Developing Complex Applications

Healthcare

Machine learning engineers can develop models to analyze medical images and identify potential health issues, such as tumors or other abnormalities. They can also develop models to predict patient outcomes and assist in the development of new drugs and treatments.

Establishing MVC Architecture

Financial Services

Machine learning engineers can develop models to detect fraud, predict stock prices, and analyze customer behavior to identify potential risks or opportunities.

Mobile App Development

Retail

Machine learning engineers can develop models to recommend products to customers, predict demand for products, and optimize pricing and inventory management.

Creating Templates

Manufacturing

Machine learning engineers can develop models to optimize production processes, predict equipment failures, and improve the efficiency of supply chains.

Developing Complex Applications

Transportation

Machine learning engineers can develop models to optimize logistics, predict traffic patterns, and improve the safety and efficiency of transportation systems.

Establishing MVC Architecture

Agriculture

Machine learning engineers can develop models to optimize crop yields, predict weather patterns, and improve the efficiency of farming operations. Marketing: Machine learning engineers can develop models to predict customer behavior, identify potential sales opportunities, and optimize marketing campaigns.

Frequently asked questions

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The entire process takes around 2-10 days. A clear job description and fast interview turnarounds can reduce this duration.

Supersourcing takes the responsibility of managing employees timesheet, availability. One Senior Account manager will be assigned to each project. We don't prefer bot on support. Our senior team is available even in weekends to support you in your business. Just an Email/WhatsApp away.

Firstly, we understand their technical knowledge through Mettl & HackerEarth. Secondly, we manually verify all data points through different sources to ensure the highest quality of talent.

We don't work with freelancers. We work with developers who are looking for full-time work but at different organisations. The verification interview is also done to ensure seamless compatibility with different companies.

Monthly to yearly, we have different options that companies can choose from.

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Other Platforms Vs Supersourcing

Sourcing

Duration 10 Days

Posting your requirement in multiple job Boards

You have to create an account in multiple job boards and paying for its subscription

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Duration 1 Day

Just share your requirements with us

Role • Experience • Communication • Technical Skills • Etc.

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Duration 20 Days

Spending your enormous time in screening unskilled candidates

You will be wasting most of your time and money in the screening process

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Duration 2 Days

Our AI will do the screening and match the top 1 profile for you

Programming

Communication

Situation-Based Questions

Only 3% of programmers can crack

Super curated profiles don't require interviews. 100% matching with your requirement. Supersourcing guarantees quality.

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Duration 10 Days

Onboarding

After completing all the steps, you will send an offer letter to the candidate, unsure of whether they will accept it or not.

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Duration 2 Days

Our team will take care of everything

Onboarding Support

Dedicated Account Manager

Standard Timesheet

Timely Productivity Reports

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Traditional method of Hiring is slow, costly with no guarantee of joining

Time Spent

30 Days

Hiring Cost

$30,000+

Supersourcing is smart, technology driven and designed for future

Less than a week

5 Days

Zero upfront cost

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Machine Learning Engineers

What is Machine Learning?

Machine learning is a method of data analysis that automates analytical model building. It is a branch of artificial intelligence that uses statistical techniques to enable computers to learn from data and make predictions or decisions without being explicitly programmed to do so.

In machine learning, a model is trained on a data set. This training data is used to teach the model to identify patterns and relationships in the data. Once the model is trained, it can be used to make predictions or decisions about new data, without the need for human intervention. There are several types of machine learning:

Supervised Learning:

This type of machine learning is used for classification and regression tasks. The model is trained on labeled data, where the correct output is provided for each input.

Unsupervised Learning:

This type of machine learning is used for clustering and dimensionality reduction tasks. The model is trained on unlabeled data, and the goal is to identify patterns and structure in the data.

Reinforcement Learning:

This type of machine learning is used for decision making and control tasks. The model learns to make decisions based on feedback from its environment.

Semi-supervised Learning:

This type of machine learning is a combination of supervised and unsupervised learning, where the model is trained on a dataset that includes both labeled and unlabeled data.

Machine learning is widely used in various industries such as healthcare, finance, transportation, and manufacturing, to improve operations, increase efficiency, and make better data-driven decisions.

Why Should You Hire Machine Learning Engineers?

Businesses should hire machine learning engineers because they have the skills and expertise to design, develop, and deploy machine learning models and systems that can analyze and make predictions or decisions from data.

Machine Learning engineers also have the ability to select appropriate algorithms and technologies for specific use cases and evaluate and improve the performance of models over time. They are able to take the raw data and turn it into actionable insights and predictions. They also design and implement the infrastructure and tools needed to build, test, and deploy these models, which is critical for the successful implementation of machine learning in a business setting. They can also create and maintain data pipelines, data warehousing and data governance, which are essential for the smooth functioning of any machine learning system. In short, hire machine learning engineers as they play a critical role in making machine learning a practical and valuable tool for businesses to improve their operations, increase efficiency and make better data-driven decisions.

How to Shortlist Machine Learning Engineers?

Shortlisting and hiring machine learning engineers can be a complex process, as there are many factors to consider when assessing a candidate's qualifications and experience. Here are some tips to help you shortlist a machine learning engineer:

Review their educational background

Look for candidates who have a degree in a field related to computer science, mathematics, or statistics. A master's or PhD degree in a related field is a plus.

Look for relevant work experience

Look for candidates who have a solid background in machine learning and have worked on projects that are relevant to your business.

Assess their technical skills

Look for candidates to hire Machine Learning engineers who have a strong understanding of machine learning algorithms and tools, such as Python, TensorFlow, and scikit-learn. Also, check if they have experience with big data technologies such as Hadoop and Spark.

Check their experience in deploying models

A good machine learning engineer should have experience in deploying models to production, and have knowledge of various cloud platforms such as AWS, Azure and GCP.

Evaluate their problem-solving skills

Look for candidates who have a strong ability to analyze and solve problems, and who have experience working with large and complex data sets.

Test their understanding of data

Look for candidates who have a strong understanding of data science concepts such as data pre-processing, feature engineering, and data visualization.

Look for experience with deep learning

If your business requires deep learning experience, look for candidates who have knowledge of deep learning frameworks such as PyTorch and Keras.

Look for experience with other related skills

Look for candidates who have experience working with other related skills like computer vision, natural language processing, and time series analysis.

Check their communication skills

Machine learning engineers need to be able to communicate their findings and recommendations to non-technical stakeholders, so look for candidates who have strong communication skills.

Interview the candidates

Finally, conduct in-person or virtual interviews with the candidates to evaluate their understanding of machine learning and their ability to communicate their thought process clearly.

By following these tips, you can shortlist and hire Machine Learning engineers who are well-suited to your business needs and who have the skills and experience to help your organization succeed.

List of Skill Sets in a Machine Learning Engineer

A Machine Learning engineer typically needs to have skills in the following areas:

  • Strong programming skills, particularly in Python and experience with libraries such as TensorFlow, PyTorch, and scikit-learn.
  • Strong understanding of machine learning algorithms and concepts, such as supervised and unsupervised learning, deep learning, and neural networks.
  • Experience with data preprocessing, cleaning, and feature engineering.
  • Strong understanding of the mathematical concepts underlying machine learning, such as linear algebra, calculus, and probability theory.
  • Experience with data visualization and data analysis tools, such as Matplotlib, Seaborn, and Pandas.
  • Strong problem-solving skills and ability to think creatively.
  • Experience with cloud computing platforms such as AWS, GCP or Azure.
  • Strong communication and collaboration skills, as machine learning projects often involve working with cross-functional teams.
  • Understanding of software engineering best practices, such as version control and testing, is also useful for machine learning engineers.

What is The Cost of Hiring Machine Learning Engineers?

The cost to hire machine learning engineers can vary greatly depending on factors such as location, experience, and skill level.

In the United States, the average salary for a Machine Learning engineer is around $120,000 per year. However, experienced Machine Learning engineers with specialized skills and a strong track record can command much higher salaries, with some earning upwards of $200,000 or more.

In India, the average salary of a Machine Learning Engineer is around 8-12 LPA (Lakh per annum)

The cost of hiring a Machine Learning engineer can also vary depending on the type of hiring arrangement. For example, hiring a full-time employee will typically be more expensive than hiring a contractor or freelancer on a project basis.

List of Deliverables For Machine Learning Engineers

Data preprocessing and cleaning

Machine Learning engineers are responsible for preparing and cleaning data sets to be used in machine learning models. This includes tasks such as handling missing data, removing outliers, and transforming data into a format that can be used by machine learning algorithms.

Feature engineering

Machine Learning engineers are responsible for creating new features from raw data that will improve the performance of machine learning models. This includes tasks such as creating new variables, combining existing variables, and selecting relevant features for a given model.

Model selection and evaluation

Machine Learning engineers are responsible for selecting the appropriate machine learning algorithm for a given problem, and for evaluating the performance of different models. This includes tasks such as tuning model hyperparameters, comparing different algorithms, and choosing the best model based on performance metrics.

Model training and deployment

Machine Learning engineers are responsible for training machine learning models using data and deploying them to production. This includes tasks such as defining the training pipeline, monitoring the training process, and fine-tuning models to improve performance.

Model monitoring and maintenance

Machine Learning engineers are responsible for monitoring the performance of deployed models, and for making updates and adjustments as necessary. This includes tasks such as monitoring model performance metrics, identifying and fixing bugs, and retraining models as needed.

Data visualization and reporting

Machine Learning engineers are responsible for creating visualizations and reports that help stakeholders understand the performance of machine learning models. This includes tasks such as creating interactive dashboards, generating performance metrics, and creating visualizations that help stakeholders understand model results.

Collaboration and communication

Machine Learning engineers are responsible for working with cross-functional teams and communicating their findings to stakeholders. This includes tasks such as working with data scientists, software engineers, and domain experts, and communicating findings in a clear and concise manner.

Model optimization and improvement

Machine Learning engineers are responsible for continuously improving the performance of models. This includes tasks such as researching new algorithms, implementing new techniques, and experimenting with different approaches to improve performance.

FAQs on Hiring Machine Learning Engineers

What qualifications should a machine learning engineer have?

A Machine Learning engineer should have a strong background in computer science, mathematics, and statistics. They should have experience with programming languages such as Python and machine learning libraries such as TensorFlow, PyTorch, and scikit-learn. They should also have a strong understanding of machine learning algorithms and concepts, as well as experience with data preprocessing, cleaning, and feature engineering.

What experience should a machine learning engineer have?

A Machine Learning engineer should have experience with building, deploying, and maintaining machine learning models in a production environment. They should also have experience with data preprocessing, cleaning, and feature engineering, as well as experience with machine learning libraries such as TensorFlow, PyTorch, and scikit-learn.

What is the role of a machine learning engineer?

The role of a Machine Learning engineer is to design, build, and deploy machine learning models that solve real-world problems. They are responsible for selecting the appropriate machine learning algorithm for a given problem, and for evaluating the performance of different models. They also play an important role in maintaining and updating deployed models, and in working with cross-functional teams to communicate their findings to stakeholders.

What are some common challenges when hiring a machine learning engineer?

Some common challenges when hiring Machine Learning engineers include finding candidates with the right qualifications and experience, and assessing their abilities in a short amount of time. Additionally, Machine Learning engineers are in high demand, so there may be competition for top talent. Finally, the field of machine learning is constantly evolving, so staying up-to-date on the latest trends and developments can be a challenge.

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