Stability

Even amid recent waves of layoffs, Big Tech companies continue to offer more job stability than most others in the tech industry. This stability is driven by several structural advantages, not just current economic conditions.

- **Layoffs in Big Tech are more reactive, less  routine**: Unlike smaller companies or traditional enterprises that cut staff regularly every 1–2 years, Big Tech companies like Google and Facebook went over a decade without mass layoffs. Their recent cuts were responses to extreme macroeconomic shifts, not standard operating procedure.
- **Higher baseline of job security**: Statistically, it’s harder to lose a job at a Big Tech company, especially when compared to \~80–90% of other tech firms. That said, consistent performance is expected to maintain that security.
- **Stability comes from product-market fit**: These companies have already achieved PMF with multiple products—Google, Meta, Airbnb, etc.—which ensures reliable revenue and customer demand. This reduces existential risk compared to early-stage startups.
- **Strong financials provide a cushion**: Many Big Tech firms operate some of the most profitable business models in history, particularly in digital ads and cloud infrastructure. Their massive cash reserves and consistent profit margins further insulate them from financial instability.
- **Comparison with startups is stark**: Companies still seeking product-market fit or surviving on runway from funding rounds face constant pressure to cut costs, making layoffs more common and less predictable.

Future Job Prospects

Having a Big Tech name on your resume, whether Meta, Google, Uber, or others, often leads to significantly improved job-seeking outcomes. This reputation, deserved or not, dramatically influences how recruiters and hiring managers perceive candidates.

- **Stronger Resume Signal**: A stint at a Big Tech company increases perceived credibility and opens doors. Even a one- to two-year tenure can 100x the likelihood of landing interviews at other desirable companies.
- **Perception of Operational Excellence**: Recruiters and startup founders often assume that employees from companies like Amazon or Meta carry with them the systems, processes, and best practices that helped those firms succeed.
- **Proximity to Core Value Drives Perception**: Engineers who worked on flagship products or core systems (e.g., TikTok’s recommendation engine) are perceived as especially valuable, as if they embody the company’s strategic advantage.
- **Brand Halo Effect**: Just being associated with a known entity creates a trust shortcut—recruiters assume a baseline of competence and quality due to the rigorous hiring practices and peer environment of Big Tech.
- **Standardized Hiring Signal**: The structured interview process (e.g., Leetcode-style DSA questions) at these companies helps ensure a consistent bar, reinforcing the belief that Big Tech engineers are generally stronger than a randomly selected candidate.

Talent Density

Working at a Big Tech company means joining an environment that systematically attracts top-tier talent due to its high compensation and prestige. This creates a unique ecosystem where growth, learning, and problem-solving are significantly accelerated.

- **Talent Follows Compensation**: Competitive pay naturally draws in highly skilled engineers. While exceptions exist, the vast majority of engineers in Big Tech are above-average in both technical skills and motivation.
- **Noticeable Culture Shift**: Engineers often report that, compared to previous jobs, colleagues in Big Tech care more about code quality, engineering rigor, and continuous learning.
- **High Learning Potential**: Being surrounded by experienced and skilled engineers ensures a constant opportunity to learn. Teams often include individuals who are more experienced, creating a strong environment for mentorship and knowledge transfer.
- **Collective Wisdom and Problem Solving**: The scale and maturity of these organizations mean that most technical challenges have been encountered, and solved, before. Success often comes from leveraging internal networks to find engineers who’ve already navigated similar problems.
- **Standing on Shoulders of Giants**: Engineers thrive by observing and applying best practices from tech leads and staff engineers, accelerating their own effectiveness and impact through this ecosystem of shared expertise.

Competitive Pay

Big Tech companies are widely known for offering extremely competitive compensation packages, which is often one of the primary reasons engineers aim to work at these firms. This segment outlines both personal anecdotes and structural strategies that explain why compensation in Big Tech is so high.

- **Top-Tier Compensation Strategy**: Meta’s compensation goal, for example, is to pay in the 90th percentile—more than 90% of other tech companies—making total compensation significantly higher for engineers who make the transition into Big Tech.
- **Drastic Pay Increases**: It's common for engineers switching from smaller or non-FAANG companies to experience dramatic increases in pay - often as much as 50–100%.
- **High Cost of Living Justification**: These lucrative salaries help offset the cost of living in expensive tech hubs like Silicon Valley, New York City, and Seattle, where basic housing or property ownership can be out of reach without this level of income.
- **Prestige + Pay Combo**: Many engineers underestimate the financial upside of Big Tech and initially assume a 20–30% increase is generous. In reality, the combination of prestige and substantial pay is a powerful draw.

Big Tech companies are widely known for offering extremely competitive compensation packages, which is often one of the primary reasons engineers aim to work at these firms. This segment outlines both personal anecdotes and structural strategies that explain why compensation in Big Tech is so high.

- **Top-Tier Compensation Strategy**: Meta’s compensation goal, for example, is to pay in the 90th percentile—more than 90% of other tech companies—making total compensation significantly higher for engineers who make the transition into Big Tech.

- **Drastic Pay Increases**: It's common for engineers switching from smaller or non-FANG companies to experience dramatic increases in pay—often as much as 50–100%. Even the speaker was surprised by the magnitude of the raise upon joining Meta.

- **High Cost of Living Justification**: These lucrative salaries help offset the cost of living in expensive tech hubs like Silicon Valley, New York City, and Seattle, where basic housing or property ownership can be out of reach without this level of income.

- **Historical Offers Data**: In 2014, new grad offers ranged from $164K to $270K annually from Big Tech firms—Google, Facebook, and others. Salaries have only increased since then, due to ongoing talent wars and inflation.

- **Prestige + Pay Combo**: Many engineers underestimate the financial upside of Big Tech and initially assume a 20–30% increase is generous. In reality, the combination of prestige and substantial pay is a powerful draw.

Decoding the One Big Beautiful Bill Act for Tech Workers

New tax law, same tax code for tech workers… mostly.\
\
President Trump signed the One Big Beautiful Bill Act into law on July 4, 2025.\
\
In terms of the impact to your taxes, this bill largely maintains the status quo by making permanent most of the tax changes introduced on a temporary basis through the Tax Cuts and Jobs Act in 2017.\
\
It does, however, add a few new wrinkles to the tax code.\
\
I’m hosting a live webinar next week to break it all down for tech professionals like you.\
\
🎙️ Webinar: Decoding the One Big Beautiful Bill Act for Tech Workers\
🗓️ Date & Time: July 24, 2025 from 2-2:45 PM Pacific Time\
📍 Where: Live on Zoom\
💡 Especially relevant for:\
👉 Anyone in a high-income bracket that pays a lot in state & local tax\
👉 Folk planning to make Roth conversions in the next few years\
👉Those considering retiring at 65+ in the next few years\
👉 Anyone considering taking a career break or on an ACA plan\
\
We’ll cover:\
\
✅ Changes to the deductibility of state and local taxes\
✅ How the new bill affects ISO exercises and your likelihood of paying AMT\
✅ What's up with Section 174?\
✅ Changes to QSBS\
✅ "No tax on tips/overtime"\
✅ Deductibility of new U.S. car loan interest\
✅ The new "Trump account"\
✅ Impacts to Medicaid\
✅ The new bonus deduction for seniors\
✅ Updates to clean energy and EV credits\
✅ How the new rules impact ACA health insurance subsidies

How To Properly Measure Your Data

In this lesson, we explore the essential discipline of **measuring data effectively**, a foundational step in the DMAIC process. Drawing from Amazon’s own journey, we explain why rigorous data management, real-time reporting, and a culture of confronting the brutal facts are key to achieving operational excellence.

### Key Takeaways:

- **We built a disciplined system for data measurement**: Measurement goes far beyond selecting metrics—it requires clean data, reliable pipelines, centralized data warehouses, governance, security, and standardized reporting tools like Tableau or Looker.
- **We invested in an unbiased analytics culture**: Jeff Bezos asked our CFO to form an independent analytics team whose sole job was to uncover the truth, not protect orgs or executives. These analysts had both financial and operational alignment but prioritized integrity in reporting.
- **We created mechanisms to confront reality**: Inspired by Churchill and Jim Collins, we institutionalized a culture of *brutal facts*—establishing metrics and systems that allowed leadership to face problems head-on, rather than hide or distort them.
- **We operationalized this with the Weekly Business Review (WBR)**: The WBR report was auto-generated by 7 a.m. every Monday, standardized, and packed with hundreds of metrics—enabling every department to identify trends, anomalies, and action items before the company-wide WBR each Wednesday.
- **We learned that slow is fast**: Building accurate, automated, real-time measurement systems took time and leadership buy-in. But in the long run, these systems helped us move faster and make better decisions at scale.

Ultimately, this lesson shows that **data measurement is not a tech problem—it’s a leadership imperative**. By treating it as a strategic function, we created a system that helped Amazon scale with confidence and clarity.

To dive deeper into Input Metrics Mastery and learn more from Bill and Colin, follow these links:

- Take the full course at their website: <https://workingbackwards.com/>
- Buy the book: <https://www.amazon.com/Working-Backwards-Insights-Stories-Secrets/dp/1250267595>
- Bill's LinkedIn: <https://www.linkedin.com/in/bill-carr/>
- Colin's LinkedIn: <https://www.linkedin.com/in/colinbryar/>

Amazon Case Study: Detail Pages

In this lesson, we apply everything we've learned about input metrics by walking through a real-world case from Amazon—how we evolved our **selection metric** to better align with customer needs. This story illustrates the complexity, rigor, and iteration involved in making metrics truly actionable and impactful.

### Key Takeaways:

- **We started by identifying strategic input metrics**: Inspired by Amazon’s **growth flywheel**, we identified controllable levers—like low prices, broad selection, and fast delivery—that directly improved the customer experience and fueled long-term growth.
- **We didn’t wait for perfect metrics to begin**: We initially used simple metrics like the number of product detail pages to represent selection, even if they were imperfect. Early measurement got teams moving in the right direction.
- **We learned through missteps**: Our first metric for selection encouraged teams to add lots of SKUs—regardless of demand. Sales didn’t grow, and excess inventory piled up. This forced us to iterate.
- **We refined our metric to reflect actual customer experience**: Over time, we evolved to more nuanced metrics like **detail page views for in-stock items**, and ultimately to **Demand-Weighted Coverage FastTrack In-Stock Percentage (DWC FT %)**—a metric that captured whether customers could *find*, *want*, and *get* an item quickly.
- **We aligned goals with customer value**: Category managers were now measured by how well their products met real customer demand and delivery expectations—not just raw catalog size.
- **We replaced estimates with precise real-time measurements**: When anecdotes didn’t match our metric outcomes, we realized the problem wasn’t in customer feedback—it was in how we were measuring. We moved from snapshot-based approximations to real-time software tracking, which required serious engineering investment.
- **We treated metric-building as an ongoing process**: Measurement accuracy improved over years of trial and error, and nearly every customer-facing metric eventually transitioned from rough proxies to live calculations.

### To dive deeper into Input Metrics Mastery and learn more from Bill and Colin, follow these links:

- Take the full course at their website: <https://workingbackwards.com/>

- Buy the book: <https://www.amazon.com/Working-Backwards-Insights-Stories-Secrets/dp/1250267595>

- Bill's LinkedIn: <https://www.linkedin.com/in/bill-carr/>

- Colin's LinkedIn: <https://www.linkedin.com/in/colinbryar/>

Get Hands-On Experience With Frontline And Customers

In addition to data and structured methods, **immersive, first-hand experiences and direct customer research** gave us some of the most impactful input metrics and ideas at Amazon. These approaches helped ground our innovation efforts in real customer needs and operational realities.

### Key Takeaways:

- **We gained critical insight from frontline experience**: During Amazon’s early years, leadership—including Jeff Bezos—regularly worked in fulfillment centers and answered customer service calls during peak seasons. These visceral experiences helped us better understand operational pain points and identify new input metrics.
- **We institutionalized hands-on learning**: The value was so great that we made it a requirement—every Amazon employee spends time in frontline roles each year. This creates empathy, sharpens customer focus, and surfaces measurable opportunities for improvement.
- **We used customer research to drive innovation**: Surveys, focus groups, and third-party data sources gave us clear, actionable feedback. A simple survey on why customers didn’t shop more often revealed four key barriers—shipping cost, delivery speed, selection, and price—which directly shaped the creation of Amazon Prime and our long-term priorities.
- **We traced key customer complaints back to inputs**: From that early research, we began measuring and improving core inputs like in-stock rate, selection breadth, delivery speed, and pricing—cementing them in our flywheel as long-term business drivers.
- **We leveraged sales and customer interactions in B2B contexts**: Especially in business-to-business scenarios, we worked closely with sales teams and met customers in person to gather input-rich feedback, helping us refine offerings and discover metrics specific to client pain points.

Together, these methods reinforce the importance of **direct, empathetic engagement with customers and operations**—not just to build understanding, but to generate input metrics that we can track, improve, and build entire services around.

To dive deeper into Input Metrics Mastery and learn more from Bill and Colin, follow these links:

- Take the full course at their website: <https://workingbackwards.com/>
- Buy the book: <https://www.amazon.com/Working-Backwards-Insights-Stories-Secrets/dp/1250267595>
- Bill's LinkedIn: <https://www.linkedin.com/in/bill-carr/>
- Colin's LinkedIn: <https://www.linkedin.com/in/colinbryar/>

Analyze

In this phase, we dive into understanding how our **inputs drive outputs**, using structured analysis to detect patterns, pinpoint issues, and reduce variability. Building a culture of disciplined, data-driven evaluation was essential to improving business performance at Amazon.

### Key Takeaways:

- **We focus on understanding input-output relationships**: The Analyze phase helps us distinguish true patterns (signal) from random fluctuations (noise) and enables us to control variability in performance.
- **We use structured mechanisms for analysis**: DMAIC demands that we move beyond ad hoc methods by creating formal processes and tools for analyzing metrics across the company.
- **We built a culture of rigorous data review**: Regular, structured reviews became part of our operating rhythm, reinforcing analytical thinking throughout our teams.
- **We institutionalized the Weekly Business Review (WBR)**: One of our most powerful tools, the WBR was developed to enable comprehensive and recurring analysis, and it remains a central part of how we stay aligned and data-informed across all business units.

To dive deeper into Input Metrics Mastery and learn more from Bill and Colin, follow these links:

- Take the full course at their website: <https://workingbackwards.com/>
- Buy the book: <https://www.amazon.com/Working-Backwards-Insights-Stories-Secrets/dp/1250267595>
- Bill's LinkedIn: <https://www.linkedin.com/in/bill-carr/>
- Colin's LinkedIn: <https://www.linkedin.com/in/colinbryar/>

Measure

In the **Measure** phase of the DMAIC process, we focused on building a consistent, reliable foundation for capturing and interpreting data. This phase was critical in enabling accurate decision-making and accountability across our rapidly growing organization.

### Key Takeaways:

- **We standardized measurements across the company**: To avoid confusion and misalignment, we worked for years to unify definitions, formulas, and data structures across multiple data warehouses and teams.
- **We eliminated self-serving metrics**: Jeff Bezos directed our CFO, Warren Jensen, to form an independent analytics team that would report the unbiased truth, rather than letting individual teams present metrics that suited their own narratives.
- **We invested in strong data infrastructure**: Reliable measurement required robust systems for data capture, storage, access, and reporting—along with clear accountability for maintaining these systems.
- **We built a culture of impartial data analysis**: By establishing dedicated analysts and leaders to oversee data integrity, we ensured that our business decisions were driven by objective, consistent information—not wishful thinking.

To dive deeper into Input Metrics Mastery and learn more from Bill and Colin, follow these links:

- Take the full course at their website: <https://workingbackwards.com/>
- Buy the book: <https://www.amazon.com/Working-Backwards-Insights-Stories-Secrets/dp/1250267595>
- Bill's LinkedIn: <https://www.linkedin.com/in/bill-carr/>
- Colin's LinkedIn: <https://www.linkedin.com/in/colinbryar/>

Motivated IC Auto-Didact

In this segment of the course, we begin translating theory into practice by applying the essence of management—creating positive and sustainable change—to real-life situations. We're guided through four exercises that challenge us to think creatively and proactively like effective managers.

- We approach each scenario by recalling that our role as managers is to **generate positive, lasting improvement** in our organization’s performance.
- We recognize that the scenarios are vague on purpose, so we must **draw from our own experiences** to interpret and respond thoughtfully.
- In the first exercise, we examine how to support a **highly motivated, self-learning individual contributor**, and consider what impact we can create to further enable their growth and contribution.
- We are encouraged to identify the **Delta**—the change we can influence or initiate—and write down our strategy for each case, reinforcing our understanding through reflection and application.

If you want to learn more from Gilad, he shares free leadership advice daily on [LinkedIn](https://www.linkedin.com/in/giladnaor/?utm_source=taro&utm_medium=course&utm_campaign=cta) and weekly on the[ δeltas newsletter](https://posts.managementdeltas.com/?utm_source=taro&utm_medium=course&utm_campaign=cta)

Final Thoughts

Congratulations on making it to the final lesson of this course! Simply by reaching this point, you have already set yourself apart as a motivated and proactive machine learning candidate.

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/>

Challenge Yourself, Be Authentic

This closing segment reinforces the importance of authenticity and adaptability during the interview process, especially in ML interviews where surprises are common.

- **Be ready for the unexpected**—interview questions may push you outside your comfort zone, but your response and thought process matter more than perfection.
- **Think out loud**—articulate your assumptions, logic, and uncertainties to help interviewers understand how you approach problems.
- **Show your passion**—talk about what genuinely excites you in ML, whether it's recommender systems, data scale, or experimentation.
- **Connect your interests to the company’s mission or team goals**—this helps show alignment and genuine motivation.
- **Remember, interviewers are hiring a person, not just a resume**—let your curiosity and drive come through.

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/>

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/>

Creating A Customized Study Plan

**In this segment, we’re guided through how to create an effective, targeted prep plan for ML interviews. Preparation should be intentional, role-specific, and company-informed.**

- **Create a tailored study plan** based on your target role. Spend 1–2 weeks on behavioral prep, overlap topics wisely, and prioritize deliberate, focused practice.
- **Do company-specific preparation** by researching their tech stack, ML frameworks, and use cases. Read blogs, papers, and talk to current employees to understand their challenges and how they approach ML.
- **For product-focused ML roles**, focus on business impact, case studies, model selection trade-offs, and deep feature engineering—interviewers value end-to-end thinkers.
- **For infrastructure-focused ML roles**, focus on scalable systems, distributed training, MLOps best practices, feature stores, and real-world reliability scenarios like traffic spikes.
- **For research-oriented ML roles**, go deep into recent papers, experimental design, evaluation strategies, and be ready to explain your own contributions with clarity—even to non-experts.

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/>

Mock Interviews Are Non-Negotiable

To wrap up our preparation strategy, we dive into how to *structure* our prep effectively—because randomly studying “a bit of everything” doesn’t work.

- **Mock interviews are non-negotiable**: They’re the fastest way to sharpen your skills, build confidence, and improve how you communicate under pressure.
- **Avoid practicing in real interviews**: We should use mocks to make mistakes and learn, not high-stakes opportunities.
- **Find partners in your network or online**: Leverage communities or platforms like ADP List to schedule mock sessions with others.
- **Record your mocks**: Watching ourselves afterward reveals blind spots we may not notice in the moment—this step alone can lead to major improvements.

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/>

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/>

Project Presentation

Some companies include a **project presentation round** in their ML interview process, where we’re expected to walk interviewers through a real project from start to finish. This is a powerful opportunity to showcase our end-to-end technical skills, communication ability, and strategic thinking.

- **Use visuals** to anchor the conversation—simple diagrams in Excalidraw or Google Docs help explain architecture clearly and keep interviewers engaged.
- **Explain concepts like you’re teaching a junior engineer**, avoiding jargon and offering quick context before diving into complex ideas (e.g., why negative sampling is needed).
- **Stay composed during interruptions**, as these are often tests of our communication clarity rather than signs of concern.
- **Show real trade-off thinking**—don’t just name-drop methods; explain *why* one approach was selected over others based on constraints like latency or scalability.

This round is about more than technical depth—it’s about demonstrating that we can **lead projects, make informed decisions, and communicate them with confidence and clarity**.

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/>

The STAR+R Method For ML Experiences

In this segment, we explore how to present our machine learning project during a behavioral interview using an enhanced **STAR** method—with an extra **R** for **Reflection**. This approach helps us communicate our work clearly and effectively while demonstrating technical depth and self-awareness.

- **S – Situation**: We start by setting the context—what was the problem, when did it occur, and what were the constraints (e.g., latency limits, scale requirements, integration needs).
- **T – Task**: We define our role and specific goals. For example, improving a ranking model to boost session engagement by a measurable target.
- **A – Action**: We go deep on what we actually built. This includes model architecture choices (e.g., deep neural networks, two-tower systems), feature engineering, and design trade-offs. We explain why certain techniques were selected and how they fit into the production stack.
- **R – Result**: We share the measurable impact of our work—like a 2.2% engagement lift—and tie it to broader business or platform outcomes (e.g., full rollout after a successful A/B test).
- **R – Reflection**: Finally, we add a layer of introspection, discussing what we learned or would do differently next time—showing growth, humility, and systems thinking.

This STAR+R framework not only helps structure our storytelling but also leaves a lasting impression of us as thoughtful, impact-oriented ML professionals.

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/>

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.

Stability

Future Job Prospects

Talent Density

Competitive Pay

Competitive Pay

Decoding the One Big Beautiful Bill Act for Tech Workers

How To Properly Measure Your Data

Amazon Case Study: Detail Pages

Get Hands-On Experience With Frontline And Customers

Analyze

Measure

Motivated IC Auto-Didact

Final Thoughts

Challenge Yourself, Be Authentic

Leverage Your Allies In The Process

Creating A Customized Study Plan

Mock Interviews Are Non-Negotiable

Level-Specific Expectations

Project Presentation

The STAR+R Method For ML Experiences