"The best way to predict the future is to create it," a timeless quote by Peter Drucker. Modern business is shifting, propelled by the engine of artificial intelligence and machine learning (AI/ML), particularly Generative AI. Companies hungry for the competitive edge offered by these technologies are facing a formidable roadblock: the acute scarcity of qualified AI/ML talent.
The demand for senior machine learning engineers, and other AI specialists is skyrocketing, but the pool of qualified candidates remains distressingly shallow. Traditionally relied-upon methods for attracting skilled individuals, including offering high salaries and recruiting from competitors, are proving to be unsustainable and may not yield desired results.
One of the reasons why companies are struggling to find qualified candidates is that there simply aren’t enough skilled professionals available. Senior ML engineers generally have 5+ years of experience. This means that 5 years ago, there should have been thousands of ML graduates going into thousands of entry-level ML jobs….those jobs didn’t exist and neither did the graduates. So, this is a problem with a physical limitation that can’t be remedied unless we invent a space and time machine! Therefore, companies have three choices: compete for talent and pay ridiculous salaries, wait to hire (which isn’t really a solution and can cost your company), or train talent that’s a “near neighbor” and ‘upskill’ them to be AI/ML engineers.
Upskilling Advantages
Upskilling is a hidden oasis in this talent desert, and the companies that figure out how to upskill well (and by that we don’t mean using MOOCs), will be the ones that win in the ongoing war for AI/ML talent.
Upskilling offers a wealth of benefits: readily available talent, reduced recruitment costs, reduced salary costs (compared to those eye-watering $300-600K salaries), increased employee engagement and retention, and a pipeline of homegrown talent ready to hit the ground running.
Upskilling, if done well, can be a strategic solution to the ML talent shortage. Upskilling a near neighbor such as transforming a software engineer into a ML engineer, can be effective and implemented using the concept of the "Intensive Training Start" program.
Unleashing Hidden AI/ML Talent Gems: Upskilling 'Near Neighbor' Talent
When the talent market doesn’t provide adequate or timely talent, one of the smartest solutions is to find talent with a very similar skill set, and train them into the jobs and roles you need. Taking a skills-centric approach to finding talent changes the pool of potential candidates, and in the case of ML engineers, expands the talent pool significantly. This begs the question “What does a ML engineer do?”, which leads to an analysis of the skills required to be successful, then using that analysis to find talent with similar skills.
“Near neighbor” talent means talent that has a large cross-over skill set with Machine Learning Engineers. Most likely, very specific technical skills will not be in the overlap zone, but these are often skills that are easier to learn than skills such as ‘writing high quality code’ or ‘anticipating problems’ or ‘design system and architecture for enterprise applications.’
This approach also has a huge strategic advantage of tapping into the skills and knowledge of existing employees. They already understand your company culture, processes, products, solutions, and specific data, making them valuable assets when equipped with the right AI/ML skills. You might have a handful of software engineers or financial analysts who would love to transition into a ML role at your company via an intensive training program - in which case, you’re no longer recruiting for as many ML positions and can find talent that’s easier to hire elsewhere.
50% of Surveyed Companies Plan to Invest in On-The-Job and Internal Training Departments Related to AI Adoption SOURCE
Look closer at your data scientists, analysts, backend developers, software engineers, and statisticians – these individuals often possess the core foundations for AI/ML success. Their experience with algorithms, familiarity with data manipulation, experience building and deploying software, working in a professional engineering environment, thinking critically, and strong quantitative skills provide an incredibly solid foundation upon which to upskill into machine learning.
Be Wise About Defining Overlapping Skills of Near Neighbor Talent
Companies often use job descriptions as the major means of looking at what skills are required for a job, but doing so would be a serious error.
Most job descriptions have an enormous amount of implied skills not listed. For example, when a software engineer job says “proficient in C/C++” what they really means is:
- Demonstrates ability to code efficiently in C and also C++
- Writes high quality code that’s easy to understand and maintain
- Capable of efficiently debugging code in C and C++
- Displays significant comprehension of each language, their strengths and weaknesses
- Can exploit languages to their advantage
- Displays deep understanding of data structures, operating systems, memory, and performance when writing code such that it optimizes to the correct priorities
Companies would do well to look at performance review criteria, promotion criteria, skills frameworks and taxonomies, and competency frameworks to identify skills that are required for ML engineer roles. This builds a comprehensive base from which to look for near neighbor talent.
Skills frameworks must look at “non-technical” skills as well, also called soft skills. These are often key when it comes to finding good Senior-level talent as these skills are harder to learn, take longer, and are more difficult than technical skill development.
An analysis of Senior ML Engineer skills with that of a Senior SWE skill will show that, regardless of their discipline, what often separates a senior from a junior is:
- The ability to anticipate problems based on the company’s tech stack, architecture, etc.
- Showing ownership - of your code, your product area(s), of problems that arise, of improvements, etc.
- Taking initiative - being constantly proactive, leading by example, helping to lead other engineers in tasks, upgrades, projects, etc.
- Raising the bar - focused on being an excellent engineer, always pursuing excellence and improvement, pushing for and implementing best practices, being rigorous
- Strong debugging and problem-solving skills
Looking comprehensively at what skills are required and what skills lie with near neighbors can help you to identify talent that you could quickly upskill into Machine Learning roles via intensive training that replicates what ML engineers do on the job.
How to Use an Intensive Training Program to Upskill Talent
An Intensive Training Start program is designed for rapid skill acquisition through an immersive learning experience, accelerating to a minimum competency level in a short timeframe. Unlike bootcamps that focus on introductory skills or MOOCs that only deal with knowledge and no application, our programs focus on advanced training, using projects that reflect what engineers would actually build in the workplace. The focus is gaining on-the-job competencies, not passing an exam.
The intensive training program is typically 2-3 months long, offering a focused and accelerated learning experience over traditional educational avenues such as university degrees or online courses. Unlike bootcamps, which often lack depth and practical application, a successful intensive training program seamlessly integrates theoretical knowledge with real-world application and problem solving, ensuring talent is trained with the specific skill set needed for successfully performing a Machine Learning Engineer role.
The Benefits of Investing in an Intensive Start Training Program
An Intensive Training Start program isn't just faster than spending 3-6 months trying to hire ML talent; it's also cheaper in the short and long run.
Investing in your existing talent is also a strategic move. You'll foster loyalty, boost employee engagement, and build a team with a shared history and understanding. It also means that, with the right training, you will fill talent gaps now knowing that in 2-3 years, you’ll have Senior and Staff-level ML talent, which will only become more and more difficult to find.
Using an intensive training period allows participants to focus solely on new skill acquisition for an intensive period, knowing that their purpose for doing so is to hit the ground running when they meet their colleagues and manager. This streamlined approach compresses the learning curve by packing focused instruction with immediate on-the-job application. This maximizes time and resources and propels individuals towards immediate contributions within their roles.
Qwasar's Intensive Start Training Program
The Qwasar model prioritizes intensive, skills-based, and competency-driven training, focusing on replicating the job of a ML engineer rather than focusing purely on knowledge with very little application.
Research has shown, “self-directed learning adds to the individuals’ workload — effort rarely acknowledged or rewarded by the organization. And widely available digital learning platforms offer generic resources, which will limit the value extracted from the workers’ gained skills.” SOURCE
However, true skill development requires an immersive and practical approach.
To ensure participants actively develop, not just passively consume, essential AI/ML skills, we prioritize hands-on learning and real-world projects. This approach ensures not only an understanding of AI/ML concepts but also builds tangible competencies relevant to real-world business needs.
Qwasar’s Intensive Training Start program offers a revolutionary approach to upskilling your team in AI/ML. This unique program dedicates 100% of an individual's time on a reduced salary to intensive training for the first 2-3 months of their transition, immersing them in AI/ML such as statistics, data analysis, building machine learning models, working with large and complex data sets, neural networks, etc.
After the first 2-3 months, participants transition to a 90-10 model where 10% of their time is spent learning and the other 90% is spent applying these freshly acquired skills to real-world projects within their current role as AI/ML engineer on full salary, guided by a dedicated manager, principal engineer, or another senior engineer.
Get Started Sooner Rather Than Later
For any inquiries, questions, or if you're ready to upskill your employees, please feel free to contact our team. We're here to assist you in solving tech talent challenges.
By investing in your existing workforce, you cultivate AI/ML talent, developed for your unique needs and culture. Internal upskilling is a strategic game-changer.
Share your thoughts, questions, and experiences with us – let's spark a conversation about unlocking the hidden talent in our own backyards. Internal upskilling is an important topic that affects both individuals and organizations. By sharing our experiences and insights, we can learn from each other and work together to address the AI/ML talent gap.
