Job Search Strategy And Career Path Of Machine Learning Job

Career Path Of A Machine Engineer

There are different paths one can choose as a Machine Learning Engineer. 

1. Becoming a Senior Machine Learning Engineer: One can become a senior Machine Learning Engineer by with working at the same place and acquiring the environment or by working for years in Machine Learning Engineer job and get a solid experience. The number of years you work as a Machine Learning Engineer increases strengthen your base and enrich your experience.

2. Become a Data Scientist: One can even choose to become a Data Scientist. However, there is a lot of overlap between what a machine learning engineer works on data science. If an individual wants to become a machine learning researcher, there's a lot of opportunities there, and that could be just through industry or even going back to get a Machine Learning certification. If you don't already have one, then you can also transition to being an engineer.

3. Work in Data Pipelines: If one wants to work on let's say databases or data pipelines or even web backends or front-ends, then the sky is the limit. 

4. Non-Technical Roles: One can also opt for non-technical roles in this field related to management and project management.

One can choose a lot many options in this field possess a touch of technical knowledge. Soft skills are an essential factor to be taken into consideration. One can achieve a lot in it depending on its interests. If you think you are great at something, then go, learn and brush your skills in the same because many opportunities are waiting for you in Machine Learning Engineer Job.

Best Companies For Aspiring machine Engineers

There are a lot of great companies that are doing work in machine learning such as, Google and Facebook who hire lots of researchers and doing a lot of work. There are lots of great companies doing excellent machine-like research labs and startups. So, there is no shortage.

Things one should look while looking for a company:

1. Identify the problems: It is good to identify the issues you work on and find the companies in your interest area. Rather than looking at a vast scope, filter your needs and then search. For example: If you want to be a computer vision and want to work in it then you should look for a company that has those sorts of problems. If you're going to be working on text data or financial data, then there are a lot of companies and a lot of exciting startups that are doing this sort of work. 

2. Do Your Research: It is good to have an idea of the kind of things that you want to be working on and then do your due diligence and some research before applying for a company.

3. Ask Yourself Questions: When you get there, it is good to ask yourself a few questions before appearing for an interview. You must see what these companies are and where you can find them and how you can potentially get in front of someone and be able to pit yourself for an interview.What sort of data do you have? Do you have the necessary infrastructure to begin working on the machine?

It's just perfect for getting a picture of what you'll be working on before you make the plunge into a new company.

4 Best Job Search Strategy

The best job search strategies for Machine Leaning job are:

1. Avoid Cold Applying: It is doubtful if you send a bunch of resumes to people that some of them would not be even seeing it. Few companies receive so many applications that it becomes tough for the hiring manager also to read all resumes.

2. Look for Referrals: The best way to get a job is through a referral whether it's someone you know a family member or in your friend circle. You can speak to them and give an idea of what you are working on.

3. Start Networking: You can reach out to people on LinkedIn and other meetups and tell about your strength and even interact with them by asking a couple of questions with your brief introduction. Expert says that reaching out to people in meetups is definitely a great way to introduce yourself and you do not even feel pressurized at all. Networking is crucial.

4. Have an Online Presence: Showing an online presence is important. If you have completed your Machine Learning certification then you can write a blog, start a website or tweet about it. Be the ones who share useful content on social media. Create your LinkedIn presence as it is a good way that recruiters might reach you there. 

Just like any other technical role, one can have the machine learning engineer as a reputed one. In machine learning career, they can choose opt for one of the two parts of this profile. The first one is as an individual contributor and the second one is the managerial role. As an individual contributor, one starts solving bigger problems or more abstract problems with time, which are related to machine learning. For example, when the fresh candidates just join a company, they might be given a working model. Now, they can try to tweak some parameters such that the accuracy of the model increases. This is a level of the problem that they are solving presently and once they keep growing, it might be like they want to solve some bigger problems like the credit card problem. They may try to come up with a new approach and once they grow further in career, they would want to identify what bigger is waiting in future. As there are problems that can appear five years down the line, they might want to solve that problem on their own. The second path or the managerial role is where someone probably goes a little higher than implementing the new approaches. Instead, they want to empower other machine learning engineers and scientists. For instance, it is a team of ML (machine learning) engineers and data scientists to solve the technical issues while the manager takes the role of more mentorship and guidance to improve their career, while of course achieving the bigger objective of the problems that are being solved.

Four Challenges Faced by the Experts in Machine Learning Career

1. The first challenge in machine learning career shared by the experts is data. It is perhaps one of the biggest problems in ML engineering. A lot of times when the ML engineers formulate problems that they want to drive certain metrics such as revenues or click-through rates, they don't necessarily have explicitly labeled data set by the user. Sometimes, when they do have access to implicit data set, they don't necessarily know whether some of the implicit signals can be treated as a positive label or a negative label. Hence, having access to the data set becomes challenging with the typical techniques that the companies end up using.
2. The second challenged discussed by the experts is defining the success criteria of the problem. A lot of times the machine learning problem’s success criteria becomes the end goal of the project. For instance, in projects like increasing conversion rates for an Amazon, coming up with the offline metrics to solve such problems becomes challenging as one doesn't necessarily know if that offline metric will correlate well with the project’s end goal. As a result, defining the real success criteria and iterating faster on top of that becomes a real challenge. 
3. The third challenge, a little more technical in nature, is designing systems and separating between the online and offline components of the system. In some problems, there is some batch job that one can do offline whereas there is certain work that needs to be done when the user comes online or makes a real request. Now, the offline and online work leads to focus upon some crucial tricks, which becomes a true challenge.
4. The fourth one is time management. A lot of times people get carried away when they are solving machine learning problems due to the log research. When one has a certain model and it demands accuracy, one has to invest days and weeks into it. Hence, as a machine running engineer, one needs to be conscious of the time.

Interesting Machine Learning Projects Done by the Expert

The experts discuss about some of the interesting projects they have worked upon in their machine learning career path. One such interesting project is supporting all the machine learning work under an umbrella in an office. This huge project includes dividing online and offline components, training models, writing data pipelines and implementing some of the models so that that can be used directly as a library. According to the experts, reading machine learning books and attending courses might give a better insight to this career to all the aspiring candidates.

Machine learning career path is a technical one that has two parts, as an individual contributor and as an ML engineer. The four challenges of this role are data analysis, defining success criteria, online and offline components and time management. An interesting machine learning project that the experts often talk about is supporting all the machine learning work under an umbrella in an office.

Like every other technical career, mechanical engineering is a reputed one that consists two parts, as an individual contributor and as an ML engineer. Data analysis, defining success criteria, online and offline components and time management are the biggest challenges in this career.