Machine Learning Engineer Onboarding Plan: A Win-Win Guide (& Free Template)
Embarking on the journey of integrating a new Machine Learning Engineer into your team is an exciting and critical venture.
"... I've never met an engineering team who had a very well-organized confluence and whose engineering documentation was actually up-to-date at any given time. That's just a natural consequence of developing software; things just change too frequently," says Kristen Buchanan, CEO and founder.
This complexity is what makes hiring and onboarding engineers challenging. Without a centralized system to organize everything, it can easily lead to confused and frustrated new employees.
In this guide to creating a tailored machine learning engineer onboarding plan, we'll walk you through the essential steps to ensure your new engineer is effectively integrated into the team.
This plan is not just about acquainting the new engineer with company policies and their desk; it's about immersing them in the company's tech culture, methodologies, and the specific tools and projects they'll be working on.
From initial introductions and orientation in the first week to deeper dives into specific projects and technologies in the following weeks, the plan (and free template!) is structured to build confidence and competence.
👀 What does an effective ML engineer onboarding process look like?
A successful onboarding has four major phases:
- role-specific training;
- ongoing development.
Preboarding is often neglected, but this is the perfect time to ease them into the roles. The period before the first day of work is often nerve-wracking for new employees.
➡️ Feel free to use our ready-to-use preboarding template!
Ensure that your new hires completely understand what is expected of them and that the managers establish role clarity. This can prevent new joiners from feeling overwhelmed, anxious, or burned out.
Experience-based onboarding is all about giving new employees the confidence they need to succeed in the role. Whether that's establishing meaningful connections or eliminating confusing processes (including a fragmented workflow).
An automated onboarding platform saves your HR time and actively engages your new hires by acting as their 24/7 guide. They will know what to do for the day, who to ask for help, and when to attend meetings or coding training sessions.
As you are preparing the first draft of their onboarding plan, consider the following questions:
- What kinds of projects should they handle first?
- How do their roles and expertise fit into the overall business goals?
Tip #1: Enlist your main stakeholders to find the answers to these questions.
Tip #2: Make sure you document all details. You can include all elements in the onboarding checklist or use an onboarding portal.
Tip #3: Don't underestimate the power of having an onboarding buddy. A familiar face establishes trust and confidence. These peers can help your engineers navigate their new roles and the office culture and best practices.
➡️ We have a ready-to-use onboarding buddy program template you can use!
📝 Machine learning engineer onboarding 30-60-90 day plan template
A 30-60-90 day plan ensures your new hire's first three months go smoothly.
Tip #1: Using a standardized plan helps People Ops and Managers create a consistent experience for future hires.
Tip #2: However, customizing your employee onboarding template based on specific roles/teams/departments is essential.
Below is an ML engineer onboarding 30-60-90 day checklist to give you some great ideas.
Preparation and preboarding: Day 0
This stage is crucial as it sets the environment and vibe for the new joiners.
- Send out necessary onboarding documents, company policies, and handbooks to be signed.
- Share and answer questions on the company work-from-home policy.
➡️ Have you yet to define your company's remote and flexible work policy? You can refer to our WFH policy template.
- Prepare accounts and access rights, particularly to the firm's IT software and systems. A self-guided process works best, especially for remote workers. This can include access to code, data, model version control systems, and local and remote working environments.
- Notify colleagues and book onboarding meetings.
- Send a welcome email and package. It would be great to customize the package depending on your new joiners' interests. For example, you can offer a gift card from their favorite coffee shop.
➡️ Check out 10 ideas to welcome your employees.
- Plan out the first week of orientation and a rough draft of the 30-60-90-day plan. Ensure that all key stakeholders, such as team leaders, mentors, department managers, etc., contribute to setting priorities and goals.
- Define onboarding metrics for success.
The first 30 days: Focus on compliance and clarity
The first month on the job is all about getting to know the company's business goals, clients, and technology stack or systems.
Day 1: Provide a warm, professional reception
- Celebrate with a team intro message.
- Say hi in person (or on a call).
- Host a (virtual) lunch.
- Hand out the access card/ID/key, hardware, and other tools.
- Introduce new hires to their onboarding buddy or mentor.
Week 1: Get new employees on track
- Have them sign off on compliance policies.
- Introduce them to the firm's onboarding portal, where they can track their progress and manage tasks.
- Carry the first 1:1 conversations to set expectations.
- Inform about the timing and standards for your probationary performance review.
- Finalize the 30-60-90-day plan, including suggesting day-to-day resources like internal chat rooms and communication portals.
- Encourage them to start familiarizing themselves with the firm's Machine Learning Operations (MLOps) and Development Operations (DevOps), including timelines and processes.
- Send a small message of encouragement and appreciation at the end of the week.
The rest of the first 30 days
- Introduce team and collaborators from other departments. Some key stakeholders you should consider are the Data Science team lead, the IT system admin, and senior ML engineers.
- Keep introductory meetings short to prevent information overload.
- Deliver regular day-to-day tips, such as bite-sized messages via Slack, Teams, or email, to deliver useful real-time info. (This is a best practice from Google's onboarding playbook.)
- Introduce them to specific machine learning projects the company has worked on, including successes and challenges.
- Share case studies or whitepapers related to the company's ML applications.
- Encourage them to better understand existing ML algorithms, frameworks, and libraries and how these can be improved.
- Start sharing new employee feedback through regular feedback. Consider scheduling a mid-probation review.
➡️ Download our free mid-probation review template, your shortcut to new hire success.
🤔 Unsure about how to handle performance reviews during the onboarding period? Check out our detailed guide on conducting new hire performance reviews that boost performance and engagement.
- Ask for feedback as well, such as an employee Net Promoter Score (eNPS) survey. An eNPS enables you to gauge how effective the onboarding process is.
🆘 Conflicted over when and how to give feedback to new employees during onboarding? We have gathered multiple practical tips to help you.
Key performance and learning goals for the first 30 days
- Gain a foundational understanding of the company's ML infrastructure and data pipelines.
- Learn the company's code review process and version control systems.
- Complete basic ML operational training.
- Successfully set up and run a simple ML model using company data.
Days 30-60: Focus on training
After the first month, your ML engineer should be able to work more independently and start to explore more tasks.
- In the second month, it's time for your engineers to have more hands-on exposure to the company's actual workflows. For example, they can finally be assigned to a live ticket, where they will be working with another engineer or developer.
- The new hire and co-developer can then regularly conduct pair programming sessions for hands-on mentorship. Remember that they might be slower at resolving tickets at first, so don't give them super urgent ones.
- Assign a small-scale project to apply ML models to real company data.
- Involve them in data preprocessing and cleaning activities to understand the data quality and structure.
Tip: Ensure the new engineer is not drowned by back-to-back tickets or tasks. Give them time to experiment with hands-on modeling or statistical analyses, learn from the experience, and document what they've learned.
- After your engineer becomes comfortable with the workflow processes, they can start on more complex sprints or task combinations. These can include determining the correct data sample size for specific models. They can still have a co-developer to work with them, but the new hire can be the one to close the ticket.
Tip: Gradually increase the responsibility of the new ML engineer during sprints. They can first be involved in planning, then in resolving issues, then in refining.
Key performance metrics for this stage
- Successfully completing a mini-project with a peer review.
- Demonstrating an understanding of the company's data and its application in ML models.
- Becoming proficient in the company's ML tools and languages (e.g., Python, R, TensorFlow).
Activities for the 60-day milestone
- Reflect on 60-day goals. What went well? What did not go as expected?
- Set goals for the next 30 days.
- Address any significant areas of concern or improvement through the second-month probationary review.
- (If relevant) Define additional training needs.
Days 60-90: Focus on accountability and role proficiency
In the last onboarding phase, your new joiners take on even more autonomy. They should take full responsibility for their work while also being proactive in improving the team, process, or company.
You can decide whether day 90 will end your formal onboarding or probationary period. From there, you transition your new ML engineer into ongoing training and development to get them ready for continued success in their role.
Main objectives for this period
- Troubleshoot issues independently and consistently contribute to projects.
- Participate in brainstorming or process improvements.
- Engage in quarterly training to keep updated on the latest ML developments. Some sectors, like Natural Language Processing (NLP) and Computer Vision (CV), are fast-developing fields.
Key performance metrics for this stage
- Effectively collaborating in a cross-functional project.
- Contributing to the improvement or optimization of an existing ML model.
- Becoming confident in independently handling ML tasks.
- Gaining a deeper understanding of advanced ML techniques and their business applications.
Day 90 milestone activities
- Assess onboarding goals and metrics.
- Collect feedback from the new hire's peers and supervisors.
- Conduct the 90-day review to evaluate the employee's performance, integration, and alignment with company values and goals during this initial period.
🚀 Shortcut your next reviews with our free 90-day review template.
- Collect feedback on your onboarding process with an onboarding survey.
- Identify and discuss any areas of concern and interest.
- Set new goals for the next 6 months. Consider also adding goals to reach until concluding the first year in your organization.
➡️ If you need more templates, we have you covered. Check out our free and essential onboarding templates.
✈️ Why onboarding is important for machine learning engineers?
ML product teams, including engineers and data scientists, have highly technical skills and backgrounds. In particular, they are responsible for developing deep-learning algorithms capable of making accurate predictions. To achieve this, they have to build artificial intelligence (AI) tools and systems requiring large data sets and complicated mathematical modeling.
Designing ML systems requires regularly assessing, analyzing, and organizing data, performing tests, and refining the algorithms. Talk about a lot of knowledge-building! But this is what motivates ML engineers.
Toptal's Director of Engineering and AI Lead, Pedro Alves Nogueira, says, "Most of the people that we have are very inquisitive. They have been doing machine learning for some time now, even as freelancers. They know how to deal with clients. They know how to communicate really well. They don't really wait for the client to tell them what to do. They just get access to the data and go into it pretty quickly."
When given a new environment and puzzles to solve, ML engineers often like to dive into coding immediately and look for solutions. However, they must be given the proper context of the projects they are about to work on and their real-world applications.
Without an onboarding strategy, it would take a long time for newly hired engineers to get up to speed on the processes, the work culture, and the business objectives.
For technical teams who tend to like being in the center of the action, an automated onboarding workflow that lets them know exactly how they are progressing is ideal.
Another reason for having a clear onboarding plan is that most ML engineers like to work remotely or have flexible working arrangements. This preference is almost a given in the technology field, and remote workers require a more streamlined onboarding method.
➡️ Is your company growing at the speed of light? Check out onboarding plans for product designers, data analysts, and information security analysts.
➡️ Fast-track your machine learning talent to success with Zavvy
Offer proper guidance to your ML engineers by automating your onboarding workflows.
You can create a detailed employee onboarding plan that can inform and engage through Zavvy's employee onboarding software. Plus, through our preboarding software, you can set up a welcoming environment that can soothe first-day jitters.
With Zavvy's templates, you get a jump start on structured, enlightening, and fun onboarding plans.
And, to cap it all off, you'll be able to track new hire progress, automatically schedule events, send onboarding surveys, and include social learning features.
🚀 Once onboarding ends, Zavvy helps you smoothly transition into long-term training and development plans and feedback cycles for your ML engineering team.
With personalized learning paths, hands-on projects, and continuous feedback, your ML team will be equipped to stay at the forefront of machine learning advancements and drive impactful innovations.
📅 Book a demo to discover how our enablement tool will take your people's growth and performance to the next level.