It’s reported that there has been an increase in Machine Learning Engineer job postings of 74%, however, there has only been an increase of qualified candidates of 49%, suggesting a significant lack of talent on the market.
Based on this it’s clear that top Machine Learning talent is in very high demand, with candidates having more roles to go for and likely to receive numerous enticing offers.
We have compiled this short guide that delves into some of the main questions candidates have when deciding on their next career move.
Type of work
- Can you talk through a clear plan of what the work is, why it’s being done and the impact it will have on the business?
- Does the work represent a match for what the candidate is looking to focus on in the mid-long term? The best candidates want to work in specialist roles, whether this is computer vision, natural language processing, reinforcement learning, etc, so it is important that the work you are offering can provide that candidate with the genuine opportunity to be a specialist.
Your company culture
- What would your current team say about your culture?
- What steps do you take to unite a remote workforce?
- If your position is in-office, what separates your work environment from other companies?
- How do you foster a culture of cross-functional team collaboration?
- What training and development opportunities are there for more junior engineers?
- What is the company’s stance on team off-sites?
Opportunities for growth
- What does progression look like for ML engineers?
- What budget is there for training?
- What time is set aside for personal development, and research projects?
- What is does the first 90 days look like in this role?
Technology stack
- How do you ensure that the technology stack is up to date and meets the needs of the Machine Learning and Data Science team?
- How open are you to feedback around the current tech stack, and suggestions on how to make it better?
- Who ultimately has the final say with regard to vendor and tool selection?
How impactful is the work
- Are you able to articulate the significance of the ML function and the impact of their work within the broader context of the business?
- It’s crucial to integrate the work of ML Engineers effectively to ensure that their efforts are not forgotten or left unused.
- How will success be measured in this role?
- How will you motivate engineers if the work they are tasked with, doesn’t end up being deployed into production?
Compensation & benefits
- How confident are you that your compensation aligns with the current market?
- How often are benefits reviewed?
- If you have a bonus, how often is this paid?
- Is your package attractive to candidates post-study (i.e. covering education costs)?
- What is the PTO policy?
- Can you provide a sign-on bonus to mitigate missed bonuses from leaving a role?
Companies Reputation
- If you operate in a sector that may have ethical considerations (Alcohol, gambling, tobacco etc), how are you mitigating the candidate’s concerns?
- What do you do to improve the company’s public image?
- What do your Glassdoor reviews say? While changes may leave past employees with negative reviews, how would you mitigate the concerns of future employees?
- Is there any bad press that you need to be aware of?
Your sustainability
- The recent rise in layoffs makes candidates cautious about changing roles, this can either be for big corporations or start-ups as both have made layoffs
- What does your employee tenure look like?
- How can you reduce concerns about being laid off in the future?
- What does your company’s cash runway look like?
- What impact is the rise in AI going to have on your business?
While there may be available talent, hiring the right candidate hasn’t necessarily been made easier. If you want to attract “top” talent then ensure that you have a “top” opportunity!
