Tech loves buzzwords.

I’ve heard AI Engineer, ML Engineer, Research Engineer, and Software Engineer all used to describe working in the same problem space. While these are technically different roles, the actual work is rarely that distinct.

Consider the reality of building an AI agent:

1. You write code to enable an AI to autonomously make decisions and solve problems. This is the job of an AI Engineer.

2. This agent introduces new safety concerns to your now non-deterministic system. You train an anomaly detection model to monitor the agent for any unintended actions. This is the job of an ML Engineer.

3. To learn how to train that model, you research the current approaches and how to adapt it to your specific application. You might even conduct research on your own to validate your approach. This is the job of a Research Engineer.

Just to build a simple agent, you must wear all three hats. Yet, agent development has become a common task for software engineers.

AI for Software Engineers has a simple mission: To teach and clarify the knowledge software engineers need regarding AI. This means technical explanations, build tutorials, contextualizing current events, career resources, and more.

Hi, I’m Logan 👋 I work on Google’s machine learning platform for ads. I improve the developer experience so we can experiment and research ML applications faster. I’ve been very fortunate to work on many cool things so far in my career: AI agents, developer tools, ML infra, and ML research applied to medicine to name a few. I draw upon this experience to help you learn.

Subscribe to learn with us! Paid subs also get extra career resources and articles.

Need further convincing?

Here’s what subscribers say about Machine Learning for Software Engineers:

• “Logan is a long-time ML engineer that does an awesome job of explaining the intersection of ML/engineering (and beyond) in a digestible manner.” - Cameron R. Wolfe, Ph.D., Deep (Learning) Focus

• “Logan from [AI for Software Engineers] is doing an amazing job demystifying machine learning!” - Sahar Mor, AI Tidbits

• “I never miss an issue. A great source of information, tools and updates.” - Riccardo Vocca, The Intelligent Friend

• “A solid mix of AI news and ML engineering insights, written by an ML engineer at Google.” - MLOps Community

• “Some of the most insightful AI perspectives and technical expertise from a prominent Google data scientist. (Though the Spider-Man shirt? Still on the fence about that one. 🤷)” - Sergei Polevikov, AI Health Uncut

And more:

Articles to get started

I maintain a list of a few popular articles on my website here.

Other resources

I’ve also written a streamlined road map to learn ML fundamentals for free and I’m building up a repository of hands-on ML learning resources to specifically work in tandem with this newsletter.

If newsletters aren’t your thing, I totally understand! You can also treat Machine Learning for Software Engineers as a blog and find me on your favorite social platform:

• YouTube

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We also have a community Chat here on Substack for subscribers.

Never hesitate to reach out. I’d love to chat. 😊

Always be (machine) learning,

Logan