Yes, machine learning is automating jobs… so to speak.
But at the same time, this very process opens up new avenues for professional development!
The result:
The machine learning jobs market is exploding!
Now, the thing is – we’ve been hearing that so much, we’ve become desensitized to the notion.
The antidote to that aimless excitement and confusion is to figure out the deeper workings of the machine learning industry. Let’s look at some facts and figures:
Machine learning facts and figures (Editor's Choice):
the global machine learning market was worth $1.6 billion in 2017 and is set to grow to $20.8 billion by the end of 2024.
spending on machine learning infrastructure has increased by over 50% per year for the past 2 years and is set to top $10 billion.
a recent survey shows that 22% of companies are already using machine learning, 28% have a project in development, while the other 50% are still afraid to get their feet wet.
The business value created by AI will reach $3.9 trillion in 2022, according to Gartner.
Deep learning can eventually deliver $3.5 trillion to $5.8 trillion in annual value to the economy, according to McKinsey Global Institute.
Sure, for the moment there’s still more demand for full-stack developers than there are jobs in AI and machine learning. Still, the market is largely untapped, which might explain why it’s growing at an 44% compound annual growth rate (CAGR), while machine learning engineer job growth stands at a whopping 344% this year.
Let’s get down to business and check out current opportunities in machine learning:
published ML paper in (e.g. ICML, NeurIPS, AISTATS, UAI or arXiv)
C++, SQL, R, MATLAB, Python
$90,00, can reach up to $125,000
Machine learning architect
Define product-driven parameters from large datasets
Ensure scalability of software and hardware
Productionize ML models
Python, R, C++
TensorFlow, Keras, Kaggle
Big Data technologies
Degree in Computer Science, Data Science, or Analytics
$107,344 can reach up to $272,000
Machine Learning Developer
Work with large, structured and unstructured data sets
Develop ML recognition and prediction apps
Test, validate and streamline ML systems
OOP coding skills
Python, C++, parallel computing environments
Solid understanding of machine learning techniques and algorithms
Degree in Computer Science, Data Science, or Analytics
$121,000, can top $165,000
NLP Engineer
Categorize and mine large text datasets
Develop NLP code
Improve communication between people and machines
Expertise in the structural and cultural aspects of language
Programming skills
Experience with NLTK or other NLP libraries
$132,000
Computer vision engineer
Develop image processing algorithms
Research, prototype and productionize new CV models
Monitor and improve CV output
.NET, C#, Python, C++
OpenCV, Tensorflow, CUDA, MATLAB,GPUImage
Solid understanding of machine learning techniques and algorithms
$158,303
Deep Learning Engineer
Mine and model data
Develop unsupervised learning algorithms
Validate output and optimize neural networks
Python, R, Lisp, Java, Prolog
TensorFlow, Pytorch, Caffe, Keras
Degree in Computer Science, Data Science, or Analytics
$131,389
Data Scientist
Collect,categorize and clean up data
Develop ML algorithms from large datasets to refine business strategies
Execute advanced SQL scripts
Degree in Computer Science, Math, Physics or other quantitative-heavy background
ERwin, Enterprise Data Warehouse, Data lakes
Proficiency in Normalized / Denormalized / Star / Snowflake / Kimball design concepts
$120,495
Business Intelligence Developer
Collect and extract data from various sources
Develop reporting and analysis tools that drive business decisions
Interpret and present the result to stakeholders
Python, R scripting
BI stack – SSIS, SSRS, SSAS, Tableau, Cognos, Power BI
Degree in Data Science, Analytics, Statistics or similar
$100,984, can top $140,000
But in case you’re wondering how to get a job in machine learning or how to switch career to machine learning here are some essential AI skills in demand to get you started.
Machine Learning Jobs: Essential Skills
As with anything, you need some foundation before you can dip your toes into machine learning. Here we go:
Programming Languages
The most popular programming languages in machine learning are Python, C++, Java, Javascript, C#, R, Shell, TypeScript, Scala, and Julia. For most machine learning projects, Python is king. That said, being fluent in more programming languages will give you a greater choice of possible employers.
Calculus, Statistics, Applied Maths, and Matrix Multiplication
To use machine learning, you must first learn to express any problem you encounter using numbers and mathematical models. Think of vector and scalar multiplication, matrix addition and subtraction, and the like. Naive Bayes, k-means clustering, and support-vector machines will be your closest friends!
Signal Processing and Analysis Tools
You’ll need to follow the latest trends in machine learning and AI and get versed in distributed systems, generative models, time series classification models, and recommendation systems to name a few.
Neural Networks and Machine Learning Frameworks
Get a competitive edge early on and gain expertise in at least one of the most widespread machine learning frameworks like TensorFlow, Azure, Caffe, Theano, Torch, Spark, Hadoop, and Kaggle.
If you have a firm grasp of these topics and a passion for analytics and statistical methods, you won’t necessarily need a machine learning engineer degree. The hunger for talented people is that pronounced.
There are plenty of opportunities to start from strong interest and a research internship in one of the main areas. You can, of course, look into a Coursera or Udemy’s machine learning course.
One more thing before we turn to specific jobs in machine learning:
If you aspire to join the growing number of machine learning engineer masters, consider which of the following ML areas are closest to your heart:
deep learning (DL)
computer vision (CV)
natural language processing (NLP)
information retrieval (IR)
reinforcement learning (RL)
Now let’s look at machine learning career prospects one by one:
Top Machine Learning Engineer Jobs
Here we go!
Machine Learning Engineer
As we said, the search for this type of expertise is growing exponentially.
Job description and Salary range
Machine learning engineers rely on statistical models to create machine learning algorithms and training systems. Then they further test and optimize these and code in adjustments to enhance the learning process.
Salaries for machine learning jobs range between $80K – $165K, with some offers reaching up to a whopping $250,000 per year. Interested?
Let’s see what it takes to apply for such a position.
Daily activities and Key expertise
Analyze data to discover and extract applicable patterns and create predictive models through feature extraction, signal processing, time series analysis, linear regression or other methods.
Implement, test and constantly improve robust and scalable machine learning algorithms in collaboration with product development teams.
Look for novel approaches based on sound mathematical methods to improve and speed up the machine learning process.
Freelancing and Growth opportunities
Remote machine learning jobs abound as well, with tech giants such as Twitter and Scribd, to name a few, are currently hiring. There are also numerous, smaller freelance projects to help you find your niche and push you forward on your career path as a machine learning engineer.
Certification and Skills
Programming languages – Python, Java, C or C++ or R.
Data analysis frameworks – NumPy, SciPy, Scikit-learn, Pandas, Keras, Tensorflow, PyTorch, CNTK and NLTK.
Machine learning environments such as Azure Data Lake, Hadoop, Spark, Caffe.
Degree in maths, computer science, physics or engineering.
Ability to communicate ideas and results in a concise and convincing manner.
*Interview tip
If you’re just starting in the field, take time to explore a project of interest and go to the interview equipped with hands-on experience.
If you’re the type buzzing with high-potential ideas that need refining, this one is for you!
Job description and Salary range
Machine Learning Scientists research new opportunities and methods in ML that help train AIs more quickly and efficiently. They work to closely understand industry specifics, while also looking at the bigger picture. This way they can set strategic directions for AI applications in any environment. Offers go as high as $206,000 per year.
Daily activities and Key expertise
Collaborate with Data Science, Engineering and Sales experts and come up with new solutions.
Research and experiment with the latest breakthrough features in AI.
Publish results in scientific journals, grounding conclusions on rock-solid mathematical models and understanding of the theory behind ML.
Freelancing and Growth opportunities
Research is a great place to start. With the growing number of machine learning jobs and funding for AI, there are many opportunities for internships in ML research. What a great time to be in or just out of college!
Certification and Skills
Degree in machine learning, statistics, computer science or related fields.
Programming languages – Python, Java, C or C++ or R.
Data analysis frameworks – NumPy, SciPy, Scikit-learn, Pandas, Keras, Tensorflow, PyTorch, CNTK and NLTK.
Machine learning environments such as Azure Data Lake, Hadoop, Spark, Caffe.
Awareness of how to balance long-term vision and immediate impact.
Ability to communicate research output in a meaningful way.
*Interview tip
Be prepared to explain how variance and bias are related.
Machine Learning Architect
Here’s another machine learning niche for you if you like to think big and see things in perspective.
Job description and Salary range
Also called AI engineers, machine learning architects build scalable infrastructures and models from scratch. They find patterns in unexplored data sets and analyze them to improve existing models.
Daily activities and Key expertise
Collect and analyze data from disparate sources to identify large-scale patterns
Apply what-if, predictive and big data analysis to deliver scalable AI solutions
Schedule and manage resources for accelerated data computing
Freelancing and Growth opportunities
According to Gartner Research, machine learning architect is one of the most important emerging technical positions in the AI machine learning jobs market.
Smaller freelance projects are easy to find and a great way to test the waters. These are invaluable stepping stones on your way to becoming a senior machine learning engineer!
Certification and Skills
Degree in Computer Science, Data Science, or Data Analytics
C/C++, Python, Java, Bash programming skills
Interest in high-performance computing, deep learning frameworks, container technology and parallel file systems
Familiarity with SQL, Kafka, Spark, Hadoop, AWS, and Amazon Redshift.
Experience with Linux performance tools
Ability to communicate technical solutions to a non-expert audience
Machine Learning Developer
Emphasis on developer this time!
Job description and Salary range
Machine learning developers look to improve machine learning algorithms. They write code that aims to fulfill real-world goals. This type of developer also creates and maintains ML categorization systems.
Daily activities and Key expertise
Create, test, validate and optimize code to help achieve business strategies.
Implement impact-driven software solutions.
Communicate with stakeholders to create workflows and rule engines.
Freelancing and Growth opportunities
Oh yes! Opportunities for both are plenty, especially if you’re versed in Python. Once again, this is a great place to start with machine learning jobs.
Certification and Skills
Proficiency in coding languages, package building and optimizing code with Git.
Familiarity with SOLID, YAGNI, DRY and SoC principles
Understanding of machine learning frameworks like Hadoop,Postgres, Cassandra, Flume, Hive, Impala, Kafka.
Experience with machine learning approaches like k-means, Naive Bayes, Decision Forests
Degree in Computer Science, Statistics or related.
Strong analytical skills and passion for problem solving.
*Interview tip
Prepare yourself to explain how you can apply code re-usability approaches in ML.
Next:
Natural Language Processing Engineer
Here’s one for all those who like both kinds of languages – programming and natural!
Job description and Salary range
NLP engineers, like any other type of ML engineer, design new algorithms from large language data sets. Those algorithms can then lead to more advanced forms of information search, including semantic search. The average salary of the computational linguist topped $100,000 this year.
Daily activities and Key expertise (list)
Collect, categorize and analyze large text corpora.
Design and improve parameters for natural language processing, both spoken and written.
Develop software that improves communication between people and machines, such as Automatic Text Recognition (ATR) and Text to Speech (TTS) and Tokenization.
Freelancing and Growth opportunities
There’s lots of demand for remote and freelancing computational linguists. Starting with a project where you annotate and classify data is a great entry level machine learning job! If you’re already proficient in programming languages, this will make it even easier to become an NLP Engineer.
Certification and Skills
Sound understanding of syntax, morphology, semantics and discourse
Degree in Computational Linguistics, Linguistics or Social Sciences
Fluency in multiple languages is a plus
Programming languages like Python, Java, C/C++, R
Experience with learning platforms such as PyTorch, Theano, Caffe, or TensorFlow.
Ability to identify new areas of research and communicate ideas to an interdisciplinary team.
6.Computer Vision Engineer
Do you like to have your online shopping trophies delivered by a drone right to your doorstep? The computer vision engineers are making it happen!
Job description and Salary range
Computer vision engineers design the ML algorithms that recognize the meaning of objects and events within images and videos.
In practice, this mean computer vision is used for image identification, face recognition, autonomous driving, and optical imagery. Salaries in the industry top $200,000 annually.
Daily activities and Key expertise
Work closely with clients/stakeholders to define image processing targets.
Create and implement new computer vision algorithms from large image datasets.
Test and improve computer vision solutions.
Freelancing and Growth opportunities
With machine learning jobs demand on a steady rise, there are plenty of freelance and computer vision projects that train networks for various purposes. Among others, these include package detection, words and numbers extraction, and GAN implementation of fashion images.
Certification and Skills
Familiarity with CUDA and NVIDIA GPUs. Experience with OpenCV, SciPy and NumPy. Proficiency in Python or C++. Knowledge of TensorFlow and GPUImage. Feature detection, matching, filtering skills. Strong statistical and analytical skills with attention to detail.
7.Deep Learning Engineer
The deep learning algorithms aim to emulate how the human brain works. The implications of this are greater than those of any other niche in machine learning. If that sounds tempting, deep learning jobs might be your thing.
Job description and Salary range
Deep learning engineers dive into big data to extract and refine ML models that deliver results. They use large sets of semi-structured and unlabelled data to create neural nets. The latter allow for unsupervised computer learning.
Daily activities and Key expertise
Data labeling and versioning.
Predictive model construction, training, evaluation, versioning and deployment.
Hardware scaling and predictions’ monitoring.
Freelancing and Growth opportunities
Deep learning jobs will continue to affect our daily lives and hopefully improve them. Freelance and remote projects are plenty and can help sharpen your skills and make you eligible for some of the best machine learning jobs in the US.
Certification and Skills (list)
Degree in Software Engineering, Computer Science or similar.
Experience with MySQL, SQL-lite or Postgres.
Full-stack software development capabilities.
Proficiency in general-purpose programming languages like Java, C/C++, C#, Objective C, Python, JavaScript, or Go.
Knowledge of Django REST
Passion for AI and building neural networks.
*Interview tip
Know the difference between supervised learning, deep learning and reinforcement learning.
8.Data Scientist
This one seems to be a classic already.
Job description and Salary range
Data scientists dig deep into data to discover patterns. Their clients can then use the awareness of those patterns to be more effective in achieving their goals.
The data scientist has to develop mathematically-sound models, that produce actionable results. Currently median base salary for a data scientist in the US is $130,000.
Daily activities and Key expertise
Identify data sources, access and import them.
Clean up fuzzy data, categorize it and make it “model-ready”.
Present outputs in a visually appealing and engaging manner.
Freelancing and Growth opportunities
It would be really hard to find a company these days that doesn’t need data scientists. To get some practice under your belt, start with smaller datasets and freelance projects to improve your machine learning career prospects!
Certification and Skills
Degree in Engineering, Statistics, Mathematics, Economics or related. Passion for applying linear algebra, calculus, probability and statistics approaches to problem solving. Proficiency in SAS Enterprise Miner/ Enterprise Guide/ Base SAS. Experience with JavaScript, Python and R. Familiarity with data visualisation tools like Tableau. Excellent communication and storytelling skills.
*Interview tip
Know the difference between data science, deep learning and machine learning.
9.Business Intelligence Developer
Job description and Salary range
Business intelligence developers extract from data insights to improve strategic planning and business processes. They gather, integrate, analyze and present data that’s helpful for decision making. Salaries averaged $98,302 this year.
Daily activities and Key expertise
Work with relational and multidimensional databases to answer queries and generate reports.
Create BI dashboards that help improve business decision making.
Freelancing and Growth opportunities
Freelancing, remote and growth opportunities are many in the business intelligence field as well. Build a solid portfolio with smaller projects and it’ll be easier to discuss interests and expertise at the interview for your dream job!
Certification and Skills
Experience with Data Warehouse, SQL, relational database.
Power BI, DAX, and security rules.
Knowledge of SSAS/SSIS/SSRS/ ETL (Extract, Transform, Load), Report Builder
Programming skills in relevant languages like Python, R, C#, JavaScript, VB.
Passion for communicating data-driven observations to stakeholders.
Degree in Information Technology, Computer Science or business related fields.
Conclusion
The question stands whether AI will create more jobs than it will replace and more importantly – how we’re going to transition.
Machine learning engineer jobs will certainly continue to gain importance as we continue to generate more and more data. Companies need an effective and affordable way to make sense of it – and machine learning is second to none.
A white paper by Mckinsey has defined the 5 issues that machine learning is yet to solve:
labelling input and training data – the data to be fed to train the algorithm needs to be properly categorized. Developing methods to identify and label data more accurately is one of the greatest challenges in machine learning.
generating or accessing large data sets that have been properly organized is crucial to supervised learning – and advances in one-shot learning will try to solve this challenge.
understanding and interpreting output is sometimes a challenge, since it can be hard to reverse engineer the process and see what features the trained algorithm picks on to make conclusions. This adds a whole new level of mathematics to the field.
transferring models – those machines are getting smarter by the day, but they still haven’t figured one thing out: how to recognize situations analogous to ones analyzed before and apply similar computational models.
bias in algorithms – or the human factor, as we may call it. Numbers might be objective, but the people who define what the input parameters are …. are still people. The burning question of algorithm biases will long be on the table. Maybe you can add your two cents to it?
So, keep up to date with machine learning trends!
Frequently Asked Questions
What skills are needed for machine learning jobs?
To start a career in machine learning you need strong analytical skills, proficiency in statistical modelling and programming languages and a passion for translating everyday problems into computer training algorithms.
What does a machine learning engineer do?
Machine learning engineers stand at the intersection of data science and software programming. They write code that builds on data science and deep learning and create algorithms that can make sense of data. Business then uses the meaningful information to cut costs and increase profits.
What is a machine learning scientist?
Machine learning scientists research and validate novel ML approaches, often in multi-disciplinary environment. They look at the bigger picture to extract new features, add functionalities and create new ML algorithm models.
How much do machine learning jobs pay?
A lot. Entry level machine learning engineer salary averages $97,579 this year, while a senior machine learning engineer salary can reach up to $180,000 and more. If you’re looking to get a piece of the action, make sure to constantly sharpen your programming and data analysis skill. New breakthroughs happen all the time, and those who stay at the cutting edge will reap the rewards.
Is coding necessary for machine learning?
The short answer is, yes! Python is the most popular language in machine learning, giving you access to the widest choice of jobs. Then come C++, Java, JavaScript, C#, Shell, R, TypeScript, Scala, and Julia.
Still, if you’ve already landed one of the machine learning jobs, that doesn’t mean you have to do everything from scratch. There are plenty of high-quality libraries that can do the heavy lifting for you. The most imported ML packages are Numpy, Scipy, Scikit-learn and TensorFlow.
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