Top companies hiring ML engineers are a common sight these days. As companies turn to artificial intelligence (AI) technology to help decrease costs, boost productivity and derive value from their data, ML engineers are becoming a valued resource. Recent studies show that about 60% of businesses said AI positively impacted their ability to be resilient during the pandemic, and about 7 out of 10 firms have raised their AI technology investments in the last year. This clearly demonstrates the rise in remote ML engineering jobs worldwide.
It’s no surprise that AI/ML engineers and developers are in high demand at firms utilizing AI, regardless of whether they’re just getting started or have a lot of expertise. Search volume for keywords, such as “how to become a machine learning engineer” and “senior machine learning engineer salary,” has grown significantly in the same period.
However, like with any new idea, bringing the promised value to life relies significantly on having the appropriate people in place to help. And elite AI talent is scarce—nearly two-fifths of businesses perceive a lack of technical competence as a major barrier to implementing AI.
Given the limited global talent pool, what distinguishes an extraordinary ML engineer from a good one? What can you do to assist these brilliant people to succeed if you can recruit them?
How to find the best ML engineers for the job?
Here are the top attributes that all machine learning engineers have in common and what you should look for when employing one:
- They are well-versed in the architecture of distributed systems. Large datasets, parallel data processing, and distributed training are essential requirements for machine learning tasks. Knowing how to use storage, network, and computing resources efficiently may dramatically speed up the process and lower expenses.
- They break down ML solutions into a modular framework. Modularity is critical for implementing machine learning systems, just as it is in software development. It allows team members to work quickly on specific sections and promotes reusability for future projects or other teams.
- They understand the importance of testing. From verifying input data to model performance to integrating code, a top-tier ML engineer will perform thorough testing at each level of the ML production process.
- They are always concerned about security. Due to automation, an enormous amount of complex data is processed and managed on the cloud, AI and ML technologies present a new set of security threats. ML engineers must approach ML development with a security mentality, ensuring that all training data, tools, and communication routes are appropriately protected and maintained.
- They have excellent communication skills. Because ML engineers operate at the crossroads of several fields, efficient communication is critical. They’ll have to communicate everything from status to dangers to trade-offs to various project stakeholders, including data scientists, developers, managers, and business users.
- They know what “good enough” means. Many things can be improved and automated in an ML system, but it’s critical to detect when the work exceeds the return. Great ML engineers keep their eyes on the project’s goals and objectives, and they know when to call it a day. They will often get a better return on investment by moving on to the next model rather than spending the time necessary to suit the current model completely.
- They express themselves clearly. Similarly, when ML engineer needs internal-focused tools and productivity improvements, they should speak out. The company constantly wants new features as fast as feasible, but it frequently lacks the tools and processes to make it happen. A single ML model takes around 1-11 weeks to deploy, and 26% of practitioners feel delays are caused by a lack of executive buy-in. It is expected from ML engineers to make difficult requests for expenditures in areas that may not pay off immediately but will allow them to be more productive in the long run.
- They are adaptable. Machine learning initiatives might run into various issues, such as obtaining enough data or developing models that aren’t precise enough to fulfill business requirements. To deliver projects, you must shift strategies quickly to overcome hurdles without becoming frustrated or losing sight of the end goal. Demonstrated tooling versatility is also a plus. Experience with some popular frameworks and libraries, such as TensorFlow, PyTorch, and sci-kit learn, is also required for ML engineering jobs.
- They are inquisitive and effective problem solvers. Things will inevitably go wrong, and the most outstanding ML engineers will have to go outside the box to solve difficulties with machine learning, data, and software. Sometimes, what appears to be a data science issue (false positives) is actually a subtle issue farther upstream in the machine learning pipeline that is causing bad results. A sound machine learning engineer must be comfortable examining a wide range of probable underlying causes and have the patience to keep asking questions.
- They are modest in their attitude. Our environment is not static, and AI is changing at a rapid pace. We strive to remember that we are continually learning and that while certifying a product as flawless is an unattainable objective, we can constantly improve. As an example, when Google eliminated gender markers from our Cloud Vision API, ML engineers had to make complex judgments over time. Internally and externally, these shifts can be challenging to explain, but exceptional machine learning engineers respect the complexities of modern technology. Average ML engineers are distinguished from excellent ML engineers by their desire for various inputs and their ability to establish a supportive atmosphere.
When you hire machine learning engineers, you should understand that hiring experts isn’t the only criterion for a successful AI/ML project. Successful ML initiatives need input from people who aren’t ML engineers but have significant business acumen with a unified vision outside of engineering. While engineering knowledge will always be essential in AI, organizations must create procedures that allow everyone—technical and non-technical alike—to participate in moving projects from test to deployed AI.