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Dong

Dong

ML Engineer

Experience4 years
AvailabilityFull-time

Dong is an MSCS graduate with 4+ years of experience in writing robust, maintainable, craftsman-quality code and transforming software engineering principles into customer delightfulness.

Expert in
  • ML
  • Java
  • C
  • SQL
  • Python
Also worked with
  • Linux
  • Artificial Neural Networks
  • OpenStack
Naveen

Naveen

ML Engineer

Experience8 years
AvailabilityFull-time

Naveen is a senior software developer with 8+ years of experience. He is focused on site reliability engineering with a track record of shipping products on-time and under budget.

Expert in
  • Machine Learning
  • MongoDB
  • JSON
  • Python
  • Hadoop
Also worked with
  • DevOps
  • Docker
  • XML
  • C
  • ETL
Amine

Amine

ML Engineer

Experience3 years
AvailabilityFull-time

Amine has more than 3 years of experience in the fields of quantitative analysis and information technology. He is skilled in technologies like Python, MATLAB, Regression, etc.

Expert in
  • Machine Learning
  • Python
  • Pandas
  • MATLAB
  • Websockets
Also worked with
  • Angular
  • Vue.js
  • Django
  • Flask
Aakash

Aakash

ML Engineer

Experience3 years
AvailabilityFull-time

Aakash has 3+ years of experience in software development. He is looking to explore available opportunities within AI/ML space.

Expert in
  • Machine Learning
  • Python
  • Django
  • Back-end Development
Also worked with
  • Automation
  • Python for Data Science
  • Python Security Automation
Vivek

Vivek

ML Engineer

Experience3 years
AvailabilityFull-time

Vivek is a data science enthusiast with 3+ years of experience. He has a strong background in mathematics, statistics, and computer science.

Expert in
  • ML
  • SQL
  • Python
  • MATLAB
  • AWS
Also worked with
  • Deep Learning
  • Ansible
  • Microservices
  • Spark
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Ultimate guide to hire ML engineers

Worried about the hassles of hiring an ML engineer? Check this guide to know about skills to look for, interview questions, and more!

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Ultimate guide to hire ML engineers
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How to hire an ML Engineer? Skills to look for, interview questions, and more

Hiring an ML Engineer for your business can be a cumbersome task. It may be the most in-demand skill in the market at present, but finding a good developer is not as easy as it may seem.

Don’t worry! We're here to assist all employers who choose to recruit ML Engineers on their own. Hiring a developer on your own requires a fair amount of software development experience in general. However, if you're a non-technical manager interested in learning more about the hiring process of an ML Engineer, we've put up an excellent resource for you.

Skills to look for in an ML Engineer

At a high level, ML Engineers should have the following skills in his/her arsenal:

1. High proficiency in applied mathematics

Math is a vital skill for a machine learning engineer. It is also one of the fundamental taught subjects in elementary school; which is why it is the first skill on our list. But, if you're asking why an ML engineer has to master complex math at all, you're not alone. Math has a wide range of applications in machine learning. They employ a variety of mathematical formulas to choose the best ML method for a given set of data, and they can use arithmetic to define parameters and estimate confidence levels. Many ML algorithms are applications developed from statistical modeling processes, and they are extremely simple to comprehend if one has a strong mathematical background. Some of the important topics of maths that you need to test in your ML engineer include linear algebra, probability, statistics, multivariate calculus, distributions like Poisson, normal, binomial, etc.

2. Familiar with computer science fundamentals and programming

Another key aspect of a successful machine learning engineer is the ability to understand basic science and coding. They must understand data structures (stack, queue, tree, graph), algorithms (searching, sorting, dynamic and greedy programming), space and time complexity, and other CS concepts. The good news is that they are likely to be aware of all of this if they have completed a bachelor's degree in computer science or a similar certification program. Furthermore, they should be proficient in a variety of programming languages, including Python and R for machine learning and statistics, Spark and Hadoop for distributed computing, SQL for database administration, and Apache Kafka for data pre-processing, among others. Python is a very popular programming language especially for Machine Learning and Data Science so it’s great if they are well versed in its libraries like NumPy, Pandas, Matplotlib, Scikit-learn, TensorFlow, etc.

3. Good knowledge of machine learning algorithms

What is a crucial skill for a machine learning engineer to have? Obviously, knowing all of the common machine learning techniques. It is critical for your ML engineer to know where to use which algorithms. Supervised, Unsupervised, and Reinforcement Machine Learning Algorithms are the three most prevalent forms of ML algorithms. Naive Bayes Classifier, K Means Clustering, Support Vector Machine, Apriori Algorithm, Linear Regression, Logistic Regression, Decision Trees, Random Forests, and others are some of the more common ones. So it’s good if they have a sound knowledge of all these algorithms before beginning their journey in your ML engineering projects.

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4. Good knowledge of data modeling and evaluation

They should be able to model and evaluate data like a machine learning engineer. After all, data is the lifeblood of any machine learning effort. Understanding the data's fundamental structure and then looking for patterns that aren't visible to the naked eye is what data modeling entails. You must also determine whether your ML engineer is capable of managing the data. Regression, classification, clustering, dimension reduction, and other machine learning methods, for example, are dependent on the data. Naive Bayes is a classification technique that is well suited to vast data and speed, and a random forest is a regression algorithm that is well suited to accuracy. Similarly, a clustering algorithm for categorical variables is K mode while for probability is k means. An ML engineer needs to know all these details about various algorithms to contribute to data modeling and evaluation effectively.

5. Neural networks

Nobody can deny the significance of neural networks in Machine Learning. The neurons of the human brain are used to model these Neural Networks. Feedforward Neural Networks, Recurrent Neural Networks, Convolutional Neural Networks, Modular Neural Networks, and Radial Basis Function Neural Networks are just a few examples of neural networks. While it’s not necessary that your ML engineer understands all these neural networks in detail, they must know the core fundamentals.

6. Familiarity with Natural Language Processing (NLP)

Natural Language Processing is, without a doubt, crucial and integral to Machine Learning. In essence, NLP tries to teach computers human language in all of its intricacies. This is so that machines can grasp and interpret human language and, as a result, better understand human communication. Natural Language Processing is built on the foundation of many diverse libraries. These libraries contain a number of functions that can be used to help computers understand natural language by breaking the text down into its grammar, extracting key phrases, and deleting unnecessary words, among other things. The ML engineer should be familiar with some or even one of these libraries like the Natural Language Toolkit, which is the most popular platform for creating applications relating to NLP.

Create a hiring funnel

Creating a hiring funnel will provide you with numerous benefits, like assisting you in identifying the top skills and identifying an ML Engineer who will fit into your company's culture.

What Turing does for you

Candidate screening
Candidate screening

We will help you select the best talents and spot an ML engineer who will fit in your company culturally.

Test task
Test task

We verify if the candidate really wants to work at your company and is able to spend 5+ hours to prove it by rigorous tests. It helps us to see a developer's caliber.

Technical test
Technical test

Developers are asked ML related questions and made to solve tricky problems. We use open questions. The goal is not only to test developers’ knowledge – we also want to find out their way of thinking.

Giving specific feedback
Giving specific feedback

We provide explicit feedback on both the test task and the technical test after we have checked the developer's expertise.

What you do

Interview
Interview

You can interview the shortlisted developers to check if the candidate matches your requirements and is a good fit for your company.

Hired/Not-hired
Hired/Not-hired

Hire intelligently with developers sourced by software, vetted by software, matched by software & managed by software.

Top interview questions to hire ML Engineers.

Whether you're an IT recruiter or a project manager, you know that finding top developers is critical to the success of your project. Here are some sample interview questions to use when looking for a new ML Engineer to work on your online applications.

This is one of the basic things, which an ML engineer must know. The candidate would describe the ROC curve by stating the following: At various thresholds, the ROC curve is a graphical representation of the contrast between true positive rates and false-positive rates. It's frequently used as a proxy for the trade-off between the model's sensitivity (true positives) and the fall-out, or the likelihood of a false alarm (false positives).

By including the use cases of both L1 and L2 in his/her answer, the candidate might show his sturgeon competency in the subject. His/her answer may follow a statement like the following:
L2 regularization spreads error across all terms, whereas L1 regularization is more binary/sparse, with several variables allocated a 1 or 0 in weighting. Setting a Laplacean prior on the terms corresponds to L1, whereas setting a Gaussian prior relates to L2.

Such machine learning interview questions can be used to assess a candidate's ability to express sophisticated and technical details with composure, as well as their ability to summarise swiftly and efficiently. While interviewing, make sure the candidate can describe different algorithms in a way that even a five-year-old can understand.

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Here are some more ML engineer interview questions that you can ask to assess a developer’s caliber.

  • What’s a Fourier transform?
  • What’s the difference between a generative and discriminative model?
  • What cross-validation technique would you use on a time series dataset?

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Frequently Asked Questions

An ML engineer is an individual who focuses on researching, building and designing self-running AI systems to automate the predictive models. They usually work as a part of a huge data science team and communicate with data scientists, data analysts, and others. If you are looking to hire a proficient Machine Learning Engineer, reach out to Turing to make the hiring process easy.

Turing offers top-quality, cost-effective, and highly productive ML engineers who belong to the top 1% of the world's remote developers. All Turing ML engineers are selected only after going through a series of rigorous tests where their skills are depply-vetted. Daily standups are mandatory for every Turing developer as they keep the developer and the customer in alignment with the discussed goal. All Turing remote ML engineers work for at least 4 hours in your time zone for your convenience.

Machine learning is used in internet search engines, websites to make customized recommendations, banking software to detect suspicious transactions, email filters to sort out spam, and more. You can hire the best remote ML Engineers at a reasonable price with Turing, an AI-powered deep-vetting talent platform that offers top engineering talent within 4 days.

Turing has created the first and only AI-powered deep-vetting talent platform to vet remote developers. Turing tests developers based on actual skills vs. self-reported experience from traditional resumes or job interviews. Every developer at Turing has to clear our tests for programming languages, data structures, algorithms, system designs, software specialization, frameworks, and more. Each Turing developer goes through our automated seniority assessment test comprising 57 calibrated questions in 5 areas — project impact, engineering excellence, communication, people, and direction.

With Turing, you can hire the best remote developers for 100+ skills such as React, Node, Python, Angular, Swift, React Native, Android, Java, Rails, Golang, PHP, Vue, DevOps, Machine Learning, etc. Turing also offers developers based on tech stack and seniority.

You can hire the best remote ML engineers in just a few clicks with Turing. It's an AI-powered deep-vetting talent platform wherein you will find talent from all over the world who are willing to work as per your time. Turing deeply vets talent and helps you hire remote ML engineers within 4 days.

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