Career standstill after graduation? Discover realistic ways to break into AI/ML, academia, or entrepreneurship without the pressure of the typical career climb.

If you’ve found yourself facing a career standstill after graduation, you’re not alone. It’s a pretty common feeling, especially when you’re passionate about a field like AI and machine learning but aren’t quite sure how to break through or move forward. I recently talked with someone who graduated with a stats degree in 2023, currently doing data engineering work but really passionate about AI/ML. And like many, they were wondering if the standard path — a master’s degree in computer science — was worth the high cost and time, or if self-learning, entrepreneurship, or even going back into academia might be better.

Understanding the Career Standstill

Being stuck isn’t just about not having a job—it’s about feeling uncertain if the direction you’re on aligns with what you really want. For this person, although the current role includes some technical work, their heart was in AI/ML research, which they had actually experienced during a college internship. But without a clear next step, the options can feel overwhelming:

• Master’s in Computer Science: Often seen as the “go-to” option for a career in AI/ML. It provides structured learning and some networking opportunities but comes with a notable price tag and time commitment. Plus, not all employers offer tuition assistance, which means bigger personal financial investment.

• Self-Learning and Entrepreneurship: And then there’s the option to bootstrap your skillset on your own—learning coding, AI concepts, and building personal projects or startups. This route can be less conventional but appeals to those who want hands-on experience and freedom to explore multiple angles of tech and business.

• Academia: Returning to academic research can be fulfilling for those who enjoyed undergraduate research but it’s also worth noting that the academic path often means more years of study and can have uncertain job prospects after.

Is Graduate School the Only Path to AI/ML?

Many believe a graduate degree is necessary to work in AI or machine learning, but that’s not always true. Plenty of self-taught professionals and bootcamp graduates make it, especially if they’re disciplined about building projects and contributing to open source or communities.

If you consider graduate school, weigh the costs carefully—financial, time, and energy. Look at scholarships, employer tuition reimbursement programs, or part-time programs that allow you to keep working.

For more on this, check out resources like the AI section on Coursera or Stanford’s AI program overview.

Entrepreneurship: Starting Something New

If climbing the corporate ladder isn’t appealing, entrepreneurship could be a way to blend your tech background with business ambitions. Building your own project or startup takes resilience and patience but can offer rewards beyond just a paycheck—like creative control and flexibility.

To get started, try small projects that solve real problems. Join startup communities or look for mentorship programs—for example, Y Combinator’s Startup School provides free resources and a helpful network.

Academia: Research Beyond Graduation

Returning to academia is an option that offers deep dives into your passion but comes with trade-offs. Research roles can be fulfilling if you enjoy discovery and teaching, but academic positions may require a PhD plus postdoctoral work, and the career track is highly competitive.

If this route interests you, consider talking with professors or grad students to get a feel for the lifestyle and career opportunities. The Chronicle of Higher Education is a great place to read about academic careers.

Navigating Your Next Steps

Feeling stuck after graduation is normal when faced with such important choices. Here are some quick tips:

• Reflect on what excites you most day-to-day.
• Research each option thoroughly, including financial and lifestyle impacts.
• Seek out mentors or communities that can offer guidance.
• Remember, no path is permanent—you can always pivot later.

The key is to keep moving forward in a way that feels authentic to you. Whether that means grad school, self-teaching, entrepreneurship, or academia, each route has its own benefits and challenges. The important part is finding what fits your goals and values best.

If you’re at a career standstill, don’t let that feeling freeze you—use it as a chance to explore and experiment with your options. You might just find a path that suits you better than you ever expected.

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For more advice on career development in tech, check out O’Reilly’s career resources and LinkedIn Learning’s career courses.