Level-Specific Expectations

As with system design interviews, behavioral interview expectations increase with seniority. This round is our chance to bring our resume to life by showing how we think and lead as ML engineers.

- **Junior ML engineers** should focus on clearly explaining their contributions and demonstrating how they applied ML techniques with guidance.
- **Senior engineers** need to showcase how they led ML projects end-to-end, made thoughtful trade-offs, and influenced product or technical decisions.
- **Staff-level engineers** should highlight strategic, cross-team contributions—like building reusable ML platforms, solving ambiguous problems, and mentoring others.

This is where we demonstrate not just *what* we’ve done, but *how* we think—our process, leadership, and impact.

If you want to learn even more from Yayun:

- Book 1 on 1 career guidance with her here: <https://calendly.com/your-friend-yayun/1on1>
- Follow her on LinkedIn: <https://www.linkedin.com/in/yayunjin/>

Master the Art of Receiving Quality Feedback (from an ex-Meta Staff Researcher)

In this time of uncertainty in the workplace and job market, what’s one skill you can develop that would **yield disproportionate rewards**?

It’s hard to pinpoint *the one* skill, but I believe the **ability to ask for, receive, and iterate on feedback** is incredibly important. Adaptability is what helps us not just survive, but thrive—and adaptability begins with how well we take in and work with feedback.

When you learn to process feedback effectively, **you develop a kind of likability and professional superpower** that organizations and leaders find hard to ignore.

We hear feedback all the time, so what exactly are we trying to get better at? Most of the feedback we receive is vague and hard to act on. Think about examples like “improve your communication” or “increase your coding speed”—where do you even start? The **key is in asking for feedback** proactively, clarifying and chunking it down into something actionable, working on it, and then circling back to check if you’ve understood and implemented it well.

In this talk, we’ll learn how to do exactly that—a process I call “**exhausting the feedback process**,” a phrase I often use in my coaching practice. We’ll also explore how to handle feedback that’s just plain off the mark (spoiler: it’s rarely useful to say that outright to the person giving it).

Yogi on LinkedIn: <https://www.linkedin.com/in/yogi1/>

Leverage Your Allies In The Process

This segment highlights a commonly overlooked advantage in the interview process: *building relationships with your recruiter and hiring manager*. They can be powerful advocates if approached thoughtfully.

- **Leverage your recruiter** by asking proactive questions about the interview structure, topics, and panel. They can offer inside tips that guide your preparation.

- **Treat the recruiter as a partner**, not just a scheduler. Showing curiosity and seriousness can reflect well on you.

- **Engage your hiring manager** with thoughtful questions about the team’s goals, challenges, and how your experience aligns with their work.

- **Recognize their influence**—hiring managers often hold sway in final decisions, even in committee-driven processes.

- **Use every conversation as a chance to stand out**, demonstrating preparation, alignment, and initiative.

If you want to learn even more from Yayun:

- Book 1 on 1 career guidance with her here: <https://calendly.com/your-friend-yayun/1on1>
- Follow her on LinkedIn: <https://www.linkedin.com/in/yayunjin/>

Ineffective Evaluation Techniques

We close this section by calling out two common but misleading ways people try to evaluate companies and why we should avoid them.

Here are the core points from the lesson:

- We urge you not to judge a company based on how much you liked or disliked your recruiter or interviewers, because you’ll almost never work with them, and your network’s lived experience inside the company is a far better signal of culture and fit.
- We caution against spending lots of time analyzing financial statements and balance sheets unless you’re actually trained in that area; as engineers, we’re likely to misinterpret the numbers and waste time.
- We recommend instead focusing on conversations about the company’s vision, growth, and people, since fast-growing companies can change dramatically in just a few years and those qualitative signals tend to be much more predictive for your career.

Getting Extra Help

If you want direct interview support from Alex and Uzair, take their live Maven course with discount code **TARO10**: <https://maven.com/alex-kliotzkin/acing-em-interviews>

Get 1 on 1 coaching from Alex here (mention Taro and get 10% off): <https://www.alexkliotzkin.com/>

To succeed in Engineering Manager interviews, we need to do more than prepare our answers—we must *practice intentionally and iteratively*. This video highlights how structured practice can dramatically improve our communication, clarity, and confidence.

- **Mentally Rehearse with Visualization:** We should regularly visualize being in the interview, anticipating questions, and mentally walking through our stories and responses. This builds fluency and composure.
- **Write Stories for Guidance, Not Scripts:** Documenting our stories helps solidify what we want to say, but we shouldn’t memorize or regurgitate them. The goal is clarity on the *core signals* and themes we want to convey.
- **Record and Review Ourselves:** Watching ourselves on video is uncomfortable but invaluable. It reveals nervous tics, verbosity, or lack of clarity, and helps us iterate quickly.
- **Practice with Others for Realistic Feedback:** Mock interviews with someone—ideally a professional coach—simulate real pressure and provide tailored feedback. This is the most effective way to get interview-ready

Paper Reading: AutoDev: Automated AI-Driven Development

Are you interested in tools like the recently announced Devin?\
\
Join us for an enlightening session as we delve into the cutting-edge world of agentic AI-driven software development, featuring a comprehensive exploration of the AutoDev framework, Microsoft's agentic AI-driven software development framework.

This reading event will elucidate how AutoDev will revolutionize the software development process. Through enabling autonomous planning and the execution of complex engineering tasks, AI agents such as AutoDev will be fully integrated into common tools and technologies such as VSCode and GitHub. Discover how these agents can perform multiple operations on a codebase, from file editing and testing to git operations, all within a secure Docker-based environment.

Moreover, we will examine AutoDev's impressive performance on the HumanEval dataset, achieving remarkable scores of 91.5% and 87.8% for Pass@1 in code generation and test generation tasks, respectively. We will also discuss the implications of this research for the future of AI-assisted software development.

This event is a must-attend for software developers, researchers, and tech enthusiasts eager to understand the future of agentic AI-driven software development and its potential impact on the software development landscape. Whether you're interested in improving your development workflow, exploring AI applications in software engineering, or simply curious about the latest advancements in AI-driven development tools, this paper reading offers valuable insights and discussions on the forefront of technology innovation.

Mark your calendars and join us for an exciting hour of "AutoDev: Automated AI-Driven Development" exploration!

Going From Good To Unforgettable

If you want direct interview support from Alex and Uzair, take their live Maven course with discount code **TARO10**: <https://maven.com/alex-kliotzkin/acing-em-interviews>

To turn our engineering manager interviews from good to unforgettable, we need to move beyond structured responses and tap into a deeper level of storytelling that integrates our leadership philosophy, self-awareness, and clarity of thought. This video teaches us how to do exactly that by weaving together frameworks, personal reflection, and strategic communication.

- **Pair STAR with Philosophy and Frameworks:** We start with the STAR method (Situation, Task, Action, Result), but go deeper by clearly highlighting the conflict in the situation and pairing our actions with leadership philosophies and mental frameworks. This helps reveal not just what we did, but *why* we did it and how we think as leaders.
- **Answer the “Question Behind the Question” (QBQ):** Every interview question has a hidden layer—what the interviewer *really* wants to know. For example, “Have you dealt with underperformance?” isn't just about that event; it’s probing our ability to have hard conversations, uphold standards, and lead consistently.
- **Embed Systems, Frameworks, and Learnings:** Our answers should demonstrate how we operate systematically. For instance, when describing how we mediate conflict, we explain our decision-making framework and show how it was applied in real situations, reinforcing our credibility and maturity as leaders.
- **Think Beyond the Surface:** Questions like “How do you motivate your team?” are really asking about our leadership style, understanding of technical people, and ability to balance individual, team, and org-wide goals. We must address these nuances and explain how motivation is an ongoing, intentional process.
- **Use the EM Interview Formula:** The real formula for standout interviews is *(QBQ) + (Philosophy + Systems + Frameworks) + (Real Story + Learnings).* This holistic approach helps us craft answers that are authentic, insightful, and tailored to what EM interviewers truly care about.
- By leaning into this deeper method, we don’t just pass interviews—we leave a lasting impression.

Paper Reading: DeepSeek R1 - Incentivizing Reasoning Capability in LLMs via Reinforcement Learning

Session led by [Mike A](https://www.linkedin.com/in/malik-mike-a-32b63818b/) 

Join us for an insightful session on the groundbreaking paper, DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning. In this session, we will explore DeepSeek-R1, a state-of-the-art reasoning model that pushes the boundaries of reinforcement learning (RL) applied to large language models (LLMs). 

This paper introduces two novel models, DeepSeek-R1-Zero and DeepSeek-R1, showcasing advancements in reasoning capabilities through RL-driven self-evolution. Unlike traditional models relying heavily on supervised fine-tuning (SFT), DeepSeek-R1-Zero develops its reasoning abilities purely through RL, while DeepSeek-R1 combines RL with a multi-stage training pipeline for enhanced performance. 

The paper highlights exceptional benchmarks achieved by DeepSeek-R1 on math, coding, and STEM-related reasoning tasks, where its performance rivals that of leading closed-source models such as OpenAI's o1-1217. Additionally, we’ll discuss distilling these capabilities into smaller, more efficient models to make advanced reasoning accessible for diverse applications. 

Whether you're a researcher, developer, or enthusiast in AI and LLMs, this paper reading will provide an in-depth understanding of the novel reinforcement learning techniques driving DeepSeek-R1 and its implications for the future of AI-driven reasoning systems. Don't miss this opportunity to engage with cutting-edge advancements in the field! \[2501.12948\] DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning 

GitHub - deepseek-ai/DeepSeek-R1 

Mark Chen X Post: <https://x.com/markchen90/status/1884303237186216272?s=12&t=BSlMYtugAr8LLWXLnMLLrQ> 

GRPO Diagram: <https://www.reddit.com/r/LocalLLaMA/comments/1i78sfs/deepseek_r1_grpo_code_open_sourced/#lightbox> 

The Bitter Lesson: <http://www.incompleteideas.net/IncIdeas/BitterLesson.html> 

Gradient Descent Image: <https://miro.medium.com/v2/format:webp/1\\\*f9a162GhpMbiTVTAua_lLQ.png> 

V3: <https://arxiv.org/abs/2412.19437> 

DeepSeek Math: <https://arxiv.org/abs/2402.03300> 

rstar-math: <https://arxiv.org/abs/2501.04519> 

Verify Step-by-Step: <https://arxiv.org/abs/2305.20050> 

DeepSeek catch-up chart: <https://media.licdn.com/dms/image/v2/D4E22AQG552O63UNJWg/feedshare-shrink_800/B4EZTN\\\[…\\\]41824000&v=beta&t=ubyKQniaCJTL37PzIOJi9YZRo1AF8yipuauSioyn59U> 

UC Berkeley Student $30 replication of Aha: <https://x.com/jiayi_pirate/status/1882839370505621655> 

Deep Agent R1-V replication: <https://x.com/liangchen5518/status/1886171667522842856> 

s1: <https://arxiv.org/abs/2501.19393> 

DeepSeek-r1 cost breakdown: <https://semianalysis.com/2025/01/31/deepseek-debates/> 

R1 Deep Dive: <https://fireworks.ai/blog/deepseek-r1-deepdive> 

All in Podcast: <https://podcasts.apple.com/us/podcast/all-in-with-chamath-jason-sacks-friedberg/id1502871393?i=1000687638652> 

Peter Gostev LinkedIn: <https://www.linkedin.com/in/peter-gostev/> 

LLM Agents Learning: <https://llmagents-learning.org/sp25>

[Paper Reading] ROSE: Robust Caches for Amazon Product Search

Session led by: [Dharin Shah](https://www.linkedin.com/in/dharinshah/)

ROSE introduces a robust caching system that tolerates misspellings and typos while maintaining the efficiency of traditional caches. The core innovation is a randomized hashing schema that allows ROSE to index and retrieve a vast set of queries with constant memory and time. This system is robust against query intent, typos, and grammatical errors, offering theoretical guarantees of its performance.

Extensive experiments on real-world datasets highlight ROSE's effectiveness and efficiency, showcasing significant improvements in Amazon's search engine performance across key business metrics.\
\
In this session we deep dive into the query caching layer at Amazon that allows for far better and robust search performance

[**https://drive.google.com/file/d/13YwkeCc1XwnRVDVkcuE7MKt2r7aqld24/view**](https://drive.google.com/file/d/13YwkeCc1XwnRVDVkcuE7MKt2r7aqld24/view)

Paper Reading: Refactoring vs Refuctoring: Advancing the state of AI-automated code improvements

Paper Discussion led by [Adi Pratheek](https://www.linkedin.com/in/pratheekadi/)

"**Refactoring vs Refuctoring**" is the conclusion of a benchmark study of the most popular Large Language Models (LLMs) and their ability to generate code for refactoring tasks. This paper aims to illustrate the current standards and limitations, and seek to show new methodologies with higher confidence results.

**What's in store?**

In this paper reading session, we will be examining:

- The performance of the most popular Large Language Models (LLMs) on code refactoring
- How does an AI err?
- A novel innovation by CodeScene for fact-checking the AI-output and augmenting the proposed refactorings by measuring the confidence levels

Companies build pretty substantial codebases over a period of time (sometimes decades). As and when the companies grow, a significant amount of time and energy are constantly invested by development teams on code refactoring (also covered in this paper).

But, how well do the existing AI tools/models actually perform this task? Are we there yet to confidently say "**AI will replace software developers**"? Join this session where we dive deep into the paper to find more answers.

Link to the paper: [**Refactoring vs Refuctoring: Advancing the state of AI automated code improvements**](https://codescene.com/hubfs/whitepapers/Refactoring-vs-Refuctoring-Advancing-the-state-of-AI-automated-code-improvements.pdf)\
Event Link: <https://www.jointaro.com/event/paper-reading-refactoring-vs-refuctoring-advancing-the-state-of-ai-automated-code-improvements/>

Paper Reading Session: Simple Testing Can Prevent Most Critical Failures

Paper Discussion led by [Sudhi Awasthi](https://www.linkedin.com/in/sudhiawasthi/)

In this session, we will be discussing this paper titled "[Simple Testing Can Prevent Most Critical Failures: An Analysis of Production Failures in Distributed Data-Intensive Systems](https://www.usenix.org/system/files/conference/osdi14/osdi14-paper-yuan.pdf)".

Large, production quality distributed systems still fail periodically, and do so sometimes catastrophically, where most or all users experience an outage or data loss. This paper presents the result of a comprehensive study investigating 198 randomly selected, user-reported failures that occurred on Cassandra, HBase, Hadoop Distributed File System (HDFS), Hadoop MapReduce, and Redis, with the goal of understanding how one or multiple faults eventually evolve into a user-visible failure and how simple testing can prevent majority of them.\
\
Event Link: <https://www.jointaro.com/event/paper-reading-session-simple-testing-can-prevent-most-critical-failures/>

Paper Reading: Mamba - Revolutionizing Sequence Modeling

Session led by: [Akshika Wijesundara, PhD](https://www.linkedin.com/in/akshika/overlay/about-this-profile/)

*Abstract:*

Explore the Mamba paper's groundbreaking approach to sequence modeling efficiency in this event. Uncover how Mamba addresses computational inefficiencies in transformers, especially for long sequences.

*Overview:*

Discover the motivation behind Mamba, highlighting its improvements over subquadratic-time architectures struggling with language tasks. Learn about Mamba's content-based reasoning and enhancements for superior performance.

*Key Features:*

1\. **Selective Structured State Space Models (SSMs):** Mamba introduces adaptive SSM parameters for content-based reasoning, allowing selective information handling for discrete modalities.

2\. **Hardware-Aware Parallel Algorithm:** Despite forgoing efficient convolutions, Mamba's hardware-aware parallel algorithm maintains fast inference and linear scaling in sequence length.

3\. **Architecture Overview:** Dive into Mamba's streamlined architecture without attention or MLP blocks, achieving efficiency without sacrificing effectiveness.

4\. **Performance Metrics:** Witness Mamba's 5× higher throughput than Transformers, with linear scaling, showcasing its real-world improvements in language, audio, and genomics tasks.

5\. **Comparison with Transformers:** See Mamba outperforms Transformers of the same size and match larger Transformers, especially in language modeling.

Join us in unraveling the secrets of Mamba, poised to redefine sequence modeling standards. Anticipate a paradigm shift in efficiency and performance in deep learning.\
\
paper link: \[[**https://arxiv.org/abs/2312.00752**](https://arxiv.org/abs/2312.00752)\]

Take Some Time To Reflect

We wrap up by reframing layoffs as a rare opportunity to pause, reflect, and intentionally design the next chapter of your career. Here are the core points from the lesson:

- A layoff often comes with severance and time, which is an incredibly valuable resource that gives you space to step off the hamster wheel and think clearly.
- Taking days or even weeks to reflect is worthwhile, since deep self-introspection about career direction, values, and long-term goals is difficult but high leverage.
- Being laid off can surface important questions you may have ignored while grinding, including whether you enjoy your role, your function, or even the tech industry at all.
- Boredom and distance from day-to-day work can spark creativity, learning, and exploration that are nearly impossible when you’re constantly shipping and firefighting.
- With persistence, reflection, and conversations with trusted people, a layoff can become a pivot point that leads to a more aligned, fulfilling next step—even if that path looks very different than before.

Leverage Your Unfair Advantage

We explain how to accelerate your job search after a layoff by intentionally leveraging the advantages already on your résumé. Here are the core points from the lesson:

- We should leverage company brand as an unfair advantage, since recognizable names (Big Tech, well-known startups, or respected niche companies) carry trust and credibility that immediately raise our odds versus the average applicant.
- We can increase our chances by targeting adjacent companies, customers, or competitors where our background naturally fits, instead of applying randomly to roles with no contextual advantage.
- We should confidently lean into domain and product association—using the halo effect of teams, products, or problem spaces we worked on—because perceived relevance often matters more than perfect technical alignment.

[Office Hours] Doubling Your Impact As An Engineer By Doubling Your Empathy

Session led by [Ryan Kuck](https://www.linkedin.com/in/ryan-kuck/)

A core theme on Taro is that by increasing empathy you will build better, stronger relationships with your teammates. Additionally by having empathy for your manager, you will simplify your communication with them and make it easy for them to see your impact. And while it is tempting to put up a pull request as soon as the code is done, taking extra time to divide it into small, thoughtfully crafted pull requests that make OTHER people's life easier will make you a joy to work with. All of this is *slightly* counterintuitively about taking time to understand someone else better to make their life better which actually is a practice that makes your life a lot better.

Let's get together and swap stories about this! If you landed a promotion from thinking about the user's perspective and building a new feature for them, or you built an internal tool that saved other folks from having to do a repetitive task, please join and share! Also welcome is anyone who has a challenging work situation and is wondering if increasing empathy could help improve it.

There Is Nothing Wrong With You

We close by reframing layoffs as a survivable, even formative, career event rather than a personal failure. Here are the core points from the lesson:

- Being laid off is not a reflection of your ability or worth, since layoffs are driven by executives making decisions with incomplete information and often feel effectively random from an individual’s perspective.
- There is no value in self-blame or overanalyzing what you could have done differently, because once a layoff happens, the only productive move is to look forward.
- With severance as temporary financial runway, layoffs can be treated as a reset point to reflect, recover, and intentionally choose your next step, knowing this is a normal part of a long tech career.

Embodying The Leader

If you want direct interview support from Alex and Uzair, take their live Maven course with discount code **TARO10**: <https://maven.com/alex-kliotzkin/acing-em-interviews>

In this lesson, we learn how to effectively showcase leadership qualities during engineering manager interviews by understanding the most commonly assessed leadership abilities. Interviewers are assigned specific attributes to evaluate, and crafting thoughtful, real-world stories that highlight these traits is essential.

- **We prepare stories around 9 common leadership abilities**, including growing talent, delivering under pressure, and influencing stakeholders, knowing these form the backbone of most behavioral interview questions.
- **We ensure each story highlights not just outcomes, but the judgment and systems we used**, especially in situations involving conflict, ambiguity, or strategic risk.
- **We practice showcasing our resilience and decision-making**, such as dealing with underperformers, making fast-paced calls under time pressure, or navigating conflict with senior stakeholders.
- **We recognize that good answers require emotional maturity**, thoughtful communication, and the ability to reflect on learnings—not perfection or bravado.
- **We strive to embody senior leadership through our communication style**, avoiding over-explaining, exaggeration, or emotional volatility, and focusing instead on clarity, calmness, and authenticity.

Leadership Bar

If you want direct interview support from Alex and Uzair, take their live Maven course with discount code **TARO10**: <https://maven.com/alex-kliotzkin/acing-em-interviews>

In this lesson, the instructor explains the core attributes that engineering manager (EM) interviewers, especially directors and above, look for when hiring. Unlike IC roles, EM hiring is predominantly trust-based and focused on leadership capabilities over technical depth.

- **Behavioral Over Technical**: 80% of the EM hiring decision is based on behavioral criteria like trustworthiness, resilience, and ownership, while only 20% is focused on technical competency such as architecture and system design.
- **Understand the Decision-Maker’s World**: Directors and VPs prioritize reliability, communication skills, and the ability to handle uncertainty and people problems over deep technical expertise.
- **Subjective Evaluation**: EM interviews aren’t about getting “right” answers. Instead, they're conversational and context-driven, evaluating fit with the team, culture, and leadership expectations.
- **Demonstrate Ownership and Initiative**: Candidates must show extreme ownership, proactive problem-solving, and the ability to deliver results, even outside formal responsibilities.
- **Adapt Communication**: EMs need to adjust messaging depending on the audience, from engineers to upper leadership, showing mastery in stakeholder communication.

STAR

If you want direct interview support from Alex and Uzair, take their live Maven course with discount code **TARO10**: <https://maven.com/alex-kliotzkin/acing-em-interviews>

Engineering manager interviews focus heavily on **behavioral assessments**, and using a structured response framework like **STAR** can help clearly demonstrate leadership competencies and impact. STAR stands for **Situation, Task, Action, and Result** and is widely recommended for leadership roles due to its clarity and depth.

- **Situation**: Set the context of your story—when, where, and why it happened—so the interviewer understands the environment and background.
- **Task**: Define your responsibility in that situation and any challenges you faced while addressing it.
- **Action**: Detail the specific steps *you personally* took, what you owned, and how you worked with others.
- **Result**: End with measurable outcomes like cost savings, quality improvements, time to market, or customer/team impact. Show the success and its scale.

Paper Reading: Self-Discover - Empowering Large Language Models with Autonomous Reasoning Structures

In this talk, we delve into Self-Discover, an innovative framework revolutionizing Large Language Models (LLMs) by enabling them to autonomously discern task-specific reasoning structures, thereby empowering them to tackle complex problems without explicit prompting. 

At the heart of Self-Discover lies a self-discovery process, wherein LLMs independently select and amalgamate various atomic reasoning modules, such as critical thinking and step-by-step reasoning, into explicit structures to guide their decoding process. The broad applicability of these self-discovered structures across diverse model families—from PaLM2-L to GPT-4 and from GPT-4 to Llama2—underscores the framework's versatility and adaptability, suggesting seamless integration with an even wider range of advanced AI models. This holds promise for a unified approach to autonomous reasoning in artificial intelligence.\
\
Whether you're an AI researcher, engineer, or simply curious about this achievement in AI research, join us as we explore the intricacies of Self-Discover and its potential implications for prompt engineering and advancing the capabilities of Large Language Models in complex reasoning tasks.\
\
Paper: [**https://arxiv.org/pdf/2402.03620.pdf**](https://arxiv.org/pdf/2402.03620.pdf)

Lucia on LinkedIn: <https://www.linkedin.com/in/lucia-mocz-ph-d-35a98a1b6/>

Tech Career Growth Videos

Our instructional videos provide software engineers with insights needed for career growth. Masterclasses go incredibly in-depth on a specific tech topic while case studies are real examples from established engineering leaders.