A lot of our clients hire for AI roles and ask about our experience filling AI jobs. Here are five top things we think hiring managers for AI jobs should know, as well as a few success stories from our team on AI roles they’ve recruited for.
AI startups, or those who need to hire AI talent, face several specific challenges in talent acquisition, including:
Scarce AI expertise: There is a limited pool of candidates with the advanced technical skills required for AI projects, making competition fierce.
Salary constraints: Startups often cannot match the salaries and benefits offered by tech giants (Fortune 500 tech companies, or companies like Meta and Google), complicating the recruitment of top talent.
Quality over quantity: Many recruitment agencies flood startups with candidates, many of which are not a good fit, leading to wasted time and resources.
Time-pressured hiring: The need to fill roles quickly to create growth and innovation can lead to rushed hiring decisions.
Python. Python is the most used language in AI due to its extensive libraries (like TensorFlow, PyTorch, Keras) that are essential for machine learning and data science projects. Knowledge of other programming languages such as R, Java, and C++ can also be beneficial.
Familiarity with a variety of machine learning algorithms. This includes things like decision trees, neural networks, anomaly detection, and reinforcement learning) and understanding when to apply them effectively.
Deep Learning experience. For roles focusing on neural networks and deep learning, an in-depth understanding of concepts like convolutional neural networks (CNNs), recurrent neural networks (RNNs), and transformers is necessary.
Data Manipulation and analysis. Skills in data pre-processing, manipulation, and analysis are critical, as AI models require clean and well-prepared data. Proficiency in data analysis tools and libraries (e.g.,Pandas, NumPy) is essential.
Robotics. Knowledge of robotics fundamentals and software (such as ROS) for robotics-focused positions.
Machine Learning Engineer. Machine Learning Engineers specialize in designing and implementing machine learning models. They work on developing algorithms that can learn and make predictions or decisions without being explicitly programmed.
On average, we’re seeing a median salary of $180K for Machine Learning Engineering roles, with a range from $150K-250K.
Engineers with 5+ years of experience have a higher salary range ($180-220K) compared to those with 3+ years of experience, where the range starts at a lower minimum ($150-210K).
AI Engineer: AI Engineers develop AI models and systems that can perform tasks that would ordinarily require human intelligence. This role involves programming, machine learning, neural networks, and deep learning.
Since AI Engineers are a new-ish role that not every company hires for, we’ve seen particularly high salaries for this role.
Data Scientist: Data Scientists focus on analyzing and interpreting complex data to help organizations make informed decisions. They use a combination of machine learning, statistics, and data analysis techniques to uncover insights from data.
We’ve seen salaries for Data Scientists (with different levels of experience) range anywhere from $125K to $225K.
Robotics Engineer: Robotics Engineers design and build robots that can perform tasks autonomously or semi-autonomously. This role often overlaps with AI in developing algorithms that enable robots to learn from their environment and experiences.
We thought this report from tech.co was interesting, which said 72% of businesses admitting they removed at least some jobs to do Supply chain optimization. 65% of businesses also said AI would impact legal research, 64% said financial analysis, and 65% said predictive maintenance on fixed assets.
Only time will tell though which roles will be replaced by AI. Our guess? Many roles will just implement faster and more efficient processes with AI, and certain functions will need to be retrained.
At Recruiting from Scratch, we pride ourselves on our track record of connecting startups with top AI talent. Here's a couple of success stories that highlight our expertise:
One of our senior recruiters successfully placed a Lead ML Engineer at a burgeoning AI News Media startup. He managed to showcase the startup's innovative culture, commitment to ethical AI practices in Media, and the tangible impact the candidate could have on delivering real news. This personalized approach and clear alignment of values convinced the candidate to choose the startup over any tech giant, proving once again that with the right strategy, startups can indeed compete with tech giants for top AI talent.
In another instance, another of our recruiters successfully helped an AI startup in the hardware sector that needed help hiring a Senior MLFounding Engineer. Leveraging our extensive network and deep understanding of AI recruitment, we connected them with a highly skilled candidate who was seeking an agile and impact-driven work environment. The startup's transparent communication about their projects and commitment to making it easier to build hardware left a lasting impression, leading the candidate to choose the startup over other offers.
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