The future of AI isn't just about building smarter models; it's about making sure those models do what we actually want them to do. That's why careers in AI safety, alignment, and control are about to become some of the most sought-after roles in tech. A Google DeepMind senior research engineer recently shared insights into how the field is evolving and why fresh talent is desperately needed .

What Are the Four Stages of Modern AI Model Development?

Understanding how large language models (LLMs) are built helps explain why safety roles are becoming critical. Vaibhav Tulsyan, a Senior Research Engineer at Google DeepMind, outlined the complete pipeline during a recent talk at UC Davis Graduate School of Management . The process involves four distinct phases that have evolved significantly over the past few years.

• Pretraining: The initial phase where models learn patterns from massive amounts of text data across the internet and other sources.
• Supervised Fine Tuning: Humans provide examples of desired behavior to teach the model to follow instructions more reliably.
• Reinforcement Learning: Models learn through feedback signals, similar to how humans learn from rewards and penalties.
• Reinforcement Learning with Verifiable Rewards (RLVR): The newest stage where models are trained using rewards that can be mathematically verified as correct, ensuring alignment with human values.

Tulsyan stressed the importance of RLVR as models become increasingly advanced . This technique represents a fundamental shift in how companies approach the safety problem: instead of hoping models behave correctly, engineers can now build systems where correct behavior is mathematically verifiable.

Why Are AI Safety Roles Growing Faster Than Traditional Engineering?

The expansion of AI safety careers reflects a broader reality in the industry. As artificial general intelligence (AGI) capabilities draw closer, the need to ensure these systems remain aligned with human intentions becomes paramount. Tulsyan emphasized that an AGI world still needs to be maintained by engineers and safety specialists .

The core pillars driving this growth include alignment, interpretability, and control. Alignment means ensuring AI systems pursue goals that match human values. Interpretability means understanding why AI systems make the decisions they do. Control means maintaining human oversight over powerful systems. According to Tulsyan, these three pillars are fields which will grow in size as AI expands its capabilities .

How to Position Yourself for an AI Safety Career

• Embrace Fresh Perspectives: Tulsyan noted that all companies value the unbiased opinions of fresh graduates, making entry-level roles in AI safety more accessible than many assume. Early-career professionals often bring novel approaches that experienced engineers might overlook.
• Combine Technical Skills with Domain Knowledge: Understanding both the engineering side and the business implications of AI safety makes you more valuable. The ability to translate between technical teams and business stakeholders is increasingly rare.
• Learn About Verification and Formal Methods: As RLVR becomes standard practice, expertise in verifiable rewards and formal verification techniques will be highly sought after in the job market.
• Stay Current on AI Alignment Research: Following academic papers and industry developments in AI safety, interpretability, and control will help you understand where the field is heading.

The reassuring news for job seekers is that entry-level roles are not disappearing, despite concerns about automation. Tulsyan explained that with the assistance of LLMs, some companies are finding that fresh graduates can solve some problems just as well, if not better, than those who are five to ten years their senior . This levels the playing field for newcomers willing to learn quickly.

What's Happening to Traditional Software Engineering Roles?

Routine code generation is indeed being automated away. Tulsyan acknowledged that although routine code work within languages like Python or Java could be done by LLMs, this frees up engineers and data professionals to focus on novel areas of technology and expanding the areas around them . Rather than eliminating jobs, this shift redirects talent toward higher-value work.

"Software generation is disappearing, but that's not a bad thing," Tulsyan explained, noting that the automation of routine coding work creates space for engineers to tackle more complex and creative challenges in AI safety and alignment.

Vaibhav Tulsyan, Senior Research Engineer at Google DeepMind

This transition mirrors historical technological shifts. When calculators automated arithmetic, mathematicians didn't disappear; they moved into more sophisticated work. The same is happening in software engineering, where AI is handling boilerplate code while humans focus on architecture, safety, and innovation.

Why Should You Care About RLVR and Verifiable Rewards?

For anyone considering a career in AI, understanding RLVR is becoming essential. This technique represents the frontier of how companies are solving the alignment problem. Rather than training models and hoping they behave correctly, RLVR allows engineers to build systems where correct behavior can be mathematically proven. This shift has profound implications for job security and career growth in the field.

The practical impact is significant. Companies like Google DeepMind are already using these techniques in production systems. Tulsyan's work on post-training Gemini's coding abilities and building Big Sleep, an AI security agent designed to locate critical bugs within open-source projects, demonstrates how these safety techniques are being deployed in real-world applications . This means the skills you develop now in RLVR and AI safety will be directly applicable to high-impact work.

The bottom line is clear: the next wave of AI security and safety is opening doors for data professionals and engineers willing to specialize in this area. Entry-level positions are available, the field is growing rapidly, and the work directly impacts how AI systems behave in the real world. For anyone considering a career in tech, focusing on AI safety, alignment, and verification techniques positions you at the center of one of the most important technological challenges of our time.