Section 1: The Hidden Split - Why Not All ML Roles Are the Same
Most candidates preparing for machine learning interviews make a critical assumption:
“An ML role is an ML role.”
They prepare generically:
- Revise ML theory
- Practice system design
- Work on projects
- Prepare behavioral answers
But once they enter real interview loops, something feels off.
Some interviews focus heavily on:
- Product impact
- User behavior
- Experimentation
Others emphasize:
- Infrastructure
- scalability
- pipelines
The reason is simple, but rarely discussed:
ML roles fall into two fundamentally different categories.
- AI Product Teams
- Internal ML Platform Teams
And the expectations, evaluation criteria, and interview signals differ significantly.
What Are AI Product Teams?
AI product teams build features that directly impact users.
Examples include:
- Recommendation systems
- Search ranking
- Fraud detection
- Personalization engines
- Conversational AI
These systems:
- Are user-facing
- Impact business metrics
- Require constant iteration
At companies like Netflix and TikTok, ML engineers working on product teams are responsible for improving engagement, retention, and user experience.
What Are Internal ML Platform Teams?
Internal ML platform teams build infrastructure that enables other teams to use ML effectively.
Examples include:
- Feature stores
- Training pipelines
- Model serving systems
- Experimentation frameworks
- Monitoring platforms
These systems:
- Are not directly user-facing
- Serve internal teams
- Focus on reliability and scalability
At companies like Google, internal ML platforms power hundreds of product teams.
Why This Distinction Matters in Interviews
These two roles optimize for different goals:
AI Product Teams Optimize For:
- Business impact
- User experience
- Experimentation velocity
- Iteration cycles
ML Platform Teams Optimize For:
- Reliability
- Scalability
- Reusability
- Infrastructure efficiency
This leads to different interview expectations.
The Candidate Mistake
Most candidates prepare as if:
- All ML roles are product-focused
OR - All ML roles are system design-focused
This creates misalignment.
For example:
A candidate interviewing for a platform team might:
- Focus heavily on model accuracy
- Talk about user metrics
- Ignore infrastructure concerns
This signals poor fit.
The Real Interview Question
Hiring managers are not asking:
“Is this candidate strong?”
They are asking:
“Is this candidate strong for this specific team?”
This distinction determines outcomes.
Why This Split Is Increasing in 2026
Several industry trends are amplifying this divide:
1. Maturation of ML Systems
ML is no longer experimental.
Companies now have:
- Dedicated product ML teams
- Dedicated platform ML teams
This specialization increases expectations.
2. Rise of ML Infrastructure
As ML adoption grows, so does the need for:
- Scalable pipelines
- Reliable systems
- Internal tooling
Platform teams are becoming critical.
3. Complexity of AI Products
Modern AI products:
- Require rapid iteration
- Depend on user feedback
- Need constant optimization
This creates distinct skill requirements.
The Core Difference: Impact vs Enablement
You can summarize the difference like this:
AI Product Teams
“How do we improve the product using ML?”
ML Platform Teams
“How do we enable teams to build ML systems efficiently?”
These are fundamentally different problems.
How This Changes Interview Signals
AI Product Interviews Look For:
- Business thinking
- Experimentation mindset
- Metric awareness
- Iteration ability
Platform Interviews Look For:
- System design
- Infrastructure knowledge
- Scalability thinking
- Reliability focus
We’ve seen similar differences in evaluation patterns in Machine Learning System Design Interview: Crack the Code with InterviewNode, where system thinking varies based on role context.
Why Candidates Fail Without Realizing It
Many candidates:
- Give technically correct answers
- Demonstrate strong knowledge
- Perform well in isolation
But still get rejected.
Because they fail to:
- Align with team goals
- Signal the right strengths
- Adapt their answers
This creates a perception of:
“Strong candidate, but not the right fit.”
The Hidden Signal: Alignment
At senior levels especially, hiring decisions depend heavily on:
- Role alignment
- Team fit
- Problem relevance
Even strong candidates fail if they:
- Signal product thinking in platform roles
- Signal infrastructure thinking in product roles
The Core Thesis
To succeed in ML interviews, you must understand:
What kind of team you are interviewing for, and adjust accordingly.
Because:
- The same answer can be strong or weak depending on context
- The same skill can be valuable or irrelevant
- The same candidate can be a hire or a reject
What Comes Next
In Section 2, we will break down:
- How AI product interviews evaluate candidates
- What signals matter most
- What strong answers look like
- Common mistakes
Section 2: How AI Product Teams Evaluate ML Candidates
If you’re interviewing for an AI product team, the evaluation lens shifts dramatically compared to generic ML interviews.
The core question hiring managers are trying to answer is:
“Can this person use ML to move real product metrics?”
Not:
- “Do they know ML theory?”
- “Can they build a model?”
But:
- “Will they improve engagement, revenue, or user experience?”
This changes everything about how you should approach the interview.
The Core Evaluation Axis: Impact Through Iteration
AI product teams operate in environments where:
- User behavior changes constantly
- Data evolves rapidly
- Models degrade over time
Success is defined by:
Continuous improvement, not one-time performance
That’s why these teams prioritize:
- Iteration speed
- Experimentation discipline
- Metric awareness
This aligns closely with what we discussed in Why Hiring Managers Care More About Model Iteration Than Model Accuracy.
The 5 Core Signals Product Teams Look For
Let’s break down the key evaluation dimensions.
1. Product Thinking (Signal: Business Alignment)
Product teams expect you to think beyond the model.
They look for:
- Understanding of user behavior
- Awareness of business goals
- Ability to define success metrics
Example:
Weak answer:
“We improve accuracy using a better model.”
Strong answer:
“We optimize for user engagement by improving ranking relevance.”
This shows:
- You understand why the model exists
- Not just how it works
2. Metric Awareness (Signal: Impact Measurement)
In product teams, metrics are everything.
Interviewers expect you to discuss:
- What metrics matter
- Why they matter
- How they are measured
Examples:
- Click-through rate (CTR)
- Conversion rate
- Retention
- Precision/recall tradeoffs
Strong candidates:
- Connect ML metrics to business metrics
- Understand tradeoffs between them
3. Experimentation Mindset (Signal: Learning Velocity)
AI product teams rely heavily on:
- A/B testing
- Controlled rollouts
- Experimentation frameworks
Interviewers look for:
- Hypothesis-driven thinking
- Ability to design experiments
- Understanding of evaluation methods
Example:
“We would test this change through A/B experiments and measure impact on engagement.”
This signals:
- Scientific thinking
- Real-world experience
4. Iteration Discipline (Signal: Long-Term Value)
Product ML systems are never “done.”
Strong candidates naturally describe:
- Baselines
- Iteration cycles
- Continuous improvement
Example:
“We start with a baseline, identify failure patterns, and iterate based on user feedback.”
This aligns with End-to-End ML Project Walkthrough: A Framework for Interview Success.
5. Tradeoff Awareness (Signal: Practical Judgment)
Product teams constantly balance:
- Accuracy vs latency
- Precision vs recall
- Personalization vs diversity
Strong candidates explicitly discuss:
- Tradeoffs
- Constraints
- Decisions
Example:
“We accept slightly lower accuracy to improve response time and user experience.”
This signals maturity.
What Product Interviews Typically Look Like
1. Product-Oriented System Design
You may be asked:
- “Design a recommendation system”
- “Improve search ranking”
- “Build a fraud detection system”
Focus should be on:
- User impact
- Metrics
- Iteration
2. Case-Based Questions
Example:
- “Why did engagement drop?”
- “How would you improve recommendations?”
These test:
- Problem framing
- Hypothesis generation
- Iteration thinking
3. Project Deep Dives
Interviewers ask:
- What did you build?
- What impact did it have?
- What tradeoffs did you make?
They care about:
- Outcomes
- Decisions
- Learning
What Strong Answers Look Like
Let’s compare.
Weak Answer
“We used a deep learning model and improved accuracy by 3%.”
Strong Answer
“We improved recommendation relevance, which increased user engagement. We tested changes through A/B experiments and iterated based on observed behavior.”
Difference:
- Weak → model-focused
- Strong → impact-focused
Common Mistakes in Product Interviews
❌ Focusing Only on Models
Ignoring:
- Users
- Metrics
- Business impact
❌ Ignoring Experimentation
No mention of:
- A/B testing
- Evaluation methods
❌ No Iteration Thinking
Presenting:
- One-time solutions
- Static performance
❌ Over-Engineering
Using:
- Complex models unnecessarily
- Ignoring practical constraints
The Hidden Signal: Can You Drive Growth?
Ultimately, product teams ask:
“Will this person improve our product?”
They want engineers who:
- Think in metrics
- Iterate quickly
- Make impact-driven decisions
The Senior-Level Expectation
At senior levels, expectations increase:
- You define metrics, not just follow them
- You design experiments, not just run them
- You influence product decisions
This is where candidates differentiate.
The Key Insight
In AI product interviews:
Your ability to connect ML to user impact matters more than your ability to build models.
What Comes Next
In Section 3, we will cover:
- How ML platform teams evaluate candidates
- What signals they prioritize
- How interviews differ fundamentally
- What strong answers look like
Section 3: How ML Platform Teams Evaluate Candidates
If AI product teams evaluate your ability to drive user-facing impact, ML platform teams evaluate something fundamentally different:
Can you build systems that enable others to build ML reliably at scale?
This is a shift from impact ownership → infrastructure ownership.
And it changes everything about how you should approach the interview.
The Core Evaluation Axis: Reliability at Scale
ML platform teams operate in environments where:
- Hundreds of models may run simultaneously
- Multiple teams depend on shared infrastructure
- Failures can cascade across systems
Success is defined by:
Stability, scalability, and usability, not model performance
This means your evaluation is less about:
- Model accuracy
- Experimentation
And more about:
- System design
- Failure handling
- Operational robustness
The 5 Core Signals Platform Teams Look For
1. System Design Depth (Signal: Architectural Thinking)
Platform teams expect you to think in systems, not models.
They look for:
- Clear architecture
- Modular design
- Scalable components
Example:
Weak answer:
“We train a model and deploy it.”
Strong answer:
“We design a pipeline with data ingestion, feature computation, model training, and serving layers, each independently scalable.”
This signals:
- End-to-end understanding
- Infrastructure awareness
We explored similar expectations in Scalable ML Systems for Senior Engineers – InterviewNode.
2. Scalability Thinking (Signal: Growth Readiness)
Platform teams must handle:
- Increasing data volume
- More users (internal teams)
- Higher throughput
Interviewers expect you to discuss:
- Horizontal scaling
- Distributed systems
- Bottlenecks
Example:
“We design stateless services to enable horizontal scaling.”
3. Reliability and Fault Tolerance (Signal: Production Readiness)
This is one of the most important signals.
Platform teams care deeply about:
- System failures
- Data corruption
- Pipeline breaks
Strong candidates discuss:
- Failure modes
- Recovery mechanisms
- Monitoring
Example:
“We add redundancy and monitoring to detect pipeline failures early.”
4. Reusability and Abstraction (Signal: Platform Thinking)
Platform systems serve multiple teams.
They must be:
- Reusable
- Flexible
- Abstracted
Interviewers look for:
- Clean interfaces
- Generalized solutions
- Avoidance of one-off designs
Example:
“We design reusable feature pipelines instead of custom pipelines for each model.”
5. Operational Thinking (Signal: Long-Term Ownership)
Platform teams maintain systems long-term.
They expect you to discuss:
- Monitoring
- Logging
- Versioning
- Maintenance
Example:
“We track model versions and monitor performance over time.”
What Platform Interviews Typically Look Like
1. Infrastructure-Focused System Design
You may be asked:
- “Design a feature store”
- “Build a training pipeline”
- “Design a model serving system”
Focus should be on:
- Architecture
- Scalability
- Reliability
2. Distributed Systems Questions
Topics may include:
- Data pipelines
- Streaming systems
- Batch processing
- Storage systems
3. Failure Scenario Questions
Example:
- “What happens if the pipeline fails?”
- “How do you handle data corruption?”
These test:
- Fault tolerance
- Debugging
- resilience
What Strong Answers Look Like
Weak Answer
“We train a model and deploy it using an API.”
Strong Answer
“We design a modular pipeline with data ingestion, feature transformation, and model training. The serving layer is stateless to enable scaling, and we include monitoring and fallback mechanisms to handle failures.”
Difference:
- Weak → model-focused
- Strong → system-focused
Common Mistakes in Platform Interviews
❌ Focusing Too Much on Models
Ignoring:
- Infrastructure
- Pipelines
- Scaling
❌ Ignoring Failure Modes
Not discussing:
- What can go wrong
- How to recover
❌ No Scalability Thinking
Designs that:
- Work for small scale
- Fail at large scale
❌ Over-Simplifying Systems
Giving:
- High-level answers only
- No architectural detail
The Hidden Signal: Can Others Build on Your Work?
Platform teams ask:
“Will this person enable other engineers?”
They want engineers who:
- Build reusable systems
- Reduce friction
- Improve efficiency
Product vs Platform: Key Difference
Product Teams
- Optimize for user impact
- Focus on metrics
- Iterate quickly
Platform Teams
- Optimize for system reliability
- Focus on infrastructure
- Enable others
Understanding this distinction is critical.
The Senior-Level Expectation
At senior levels, platform engineers are expected to:
- Design large-scale systems
- Anticipate failures
- Improve team productivity
Not just:
- Build components
The Key Insight
In platform interviews:
Your ability to design reliable systems matters more than your ability to build accurate models.
What Comes Next
In Section 4, we will cover:
- The biggest mistakes candidates make when switching between these roles
- How to adjust your answers based on team type
- Subtle signals that hurt alignment
- Real-world failure patterns
Section 4: Why Candidates Fail Due to Misalignment (And How to Fix It)
By now, the distinction is clear:
- AI Product teams → impact, metrics, iteration
- ML Platform teams → infrastructure, reliability, scalability
Yet many strong candidates still fail.
Not because they lack skill.
But because they send the wrong signals for the role.
This section focuses on the most common, and subtle, failure patterns caused by misalignment.
The Core Insight
Most rejections in ML interviews are not:
“This candidate is weak.”
They are:
“This candidate is strong, but not for this team.”
That distinction is critical.
Failure Pattern #1: Product Thinking in Platform Interviews
This is one of the most common mismatches.
Candidate behavior:
- Talks about user metrics
- Focuses on model accuracy
- Emphasizes experimentation
Example:
“We can improve CTR by optimizing the ranking model.”
In a platform interview, this misses the point.
Why This Signals Misalignment
Platform teams care about:
- Infrastructure
- System reliability
- Scalability
Hiring managers think:
“This person is thinking like a product engineer, not a platform engineer.”
How to Fix It
Shift your framing:
From:
“How does this improve the product?”
To:
“How does this system enable other teams?
Failure Pattern #2: Platform Thinking in Product Interviews
The reverse mismatch also happens.
Candidate behavior:
- Focuses heavily on architecture
- Talks about pipelines and infrastructure
- Ignores user impact
Example:
“We design a scalable pipeline with distributed processing.”
Without mentioning:
- Metrics
- User experience
- Business impact
Why This Signals Misalignment
Product teams think:
“Can this person actually improve our product?”
Without impact thinking, the answer feels unclear.
How to Fix It
Shift your framing:
From:
“How does the system scale?”
To:
“How does this improve user outcomes?”
Failure Pattern #3: One-Size-Fits-All Answers
Many candidates prepare generic answers and reuse them everywhere.
Example:
- Same system design explanation
- Same project story
- Same tradeoff discussion
Why This Signals Risk
Hiring managers think:
- “This person doesn’t understand our needs.”
- “They are not adapting.”
This reduces perceived fit.
How to Fix It
Before answering, ask yourself:
“What does this team optimize for?”
Then tailor:
- Metrics
- Tradeoffs
- Depth
Failure Pattern #4: Misaligned Tradeoffs
Candidates often highlight the wrong tradeoffs.
Example in Product Interview
Candidate says:
“We prioritize scalability over everything.”
But product teams may prioritize:
- User experience
- Latency
- Engagement
Example in Platform Interview
Candidate says:
“We prioritize accuracy.”
But platform teams prioritize:
- Reliability
- Consistency
- Reusability
Why This Signals Misalignment
Tradeoffs reveal your priorities.
If your priorities don’t match the team’s, you feel like a risky hire.
How to Fix It
Align tradeoffs with team goals:
- Product → user impact
- Platform → system reliability
Failure Pattern #5: Wrong Depth Allocation
Candidates often go deep in the wrong areas.
In Product Interviews
Going deep on:
- Infrastructure details
- Distributed systems
Instead of:
- Metrics
- experimentation
In Platform Interviews
Going deep on:
- Model tuning
- Feature engineering
Instead of:
- Architecture
- scaling
Why This Signals Misalignment
Depth signals what you value.
If you go deep in irrelevant areas, it suggests poor prioritization.
How to Fix It
Use this rule:
Go deep where the team derives value.
Failure Pattern #6: Misaligned Project Framing
Candidates often present projects incorrectly.
Product Team Expectation
- What was the user impact?
- What metrics improved?
- What experiments were run?
Platform Team Expectation
- What system did you build?
- How did it scale?
- How reliable was it?
Common Mistake
Describing the same project the same way for both roles.
How to Fix It
Reframe your project:
- Product lens → impact & iteration
- Platform lens → system & scalability
Failure Pattern #7: Not Researching the Team
Many candidates:
- Don’t understand the team
- Don’t ask clarifying questions
- Assume generic expectations
Why This Signals Risk
Hiring managers think:
“This person is not intentional about their role choice.”
How to Fix It
Before or during the interview:
- Ask what the team focuses on
- Understand their challenges
- Adjust accordingly
Failure Pattern #8: Ignoring Subtle Interview Cues
Interviewers often hint at what they care about.
Examples:
- “How would you measure success?” → product focus
- “How would this scale?” → platform focus
Weak candidates ignore these cues.
Why This Signals Risk
- Lack of adaptability
- Poor listening
- Misalignment
How to Fix It
Listen carefully and adjust:
Let the interviewer guide your depth.
The Deeper Pattern
All misalignment issues stem from:
Failure to adapt to context.
Strong candidates:
- Recognize the role
- Adjust their thinking
- Tailor their answers
Weak candidates:
- Use generic responses
- Ignore context
- Miss signals
The Alignment Framework
Before answering any question, ask:
- Is this a product or platform team?
- What do they optimize for?
- What signals matter most here?
- Where should I go deep?
This takes seconds, but changes everything.
The Key Insight
You are not being evaluated in isolation.
You are being evaluated relative to the team’s needs.
What Comes Next
In Section 5, we will cover:
- How to consistently adapt across both types of interviews
- A unified strategy to prepare for both roles
- How to position yourself effectively
- Long-term career implications
Section 5: How to Prepare for Both - The Adaptive ML Interview Strategy
By now, you understand the core divide:
- AI Product Teams → Impact, metrics, iteration
- ML Platform Teams → Systems, scalability, reliability
And you’ve seen why candidates fail:
Not because they lack skill, but because they fail to adapt to context.
Now the final question:
How do you prepare in a way that lets you succeed in both types of interviews?
Because in reality, you often don’t control:
- Which team you interview with
- How clearly the role is defined
- What specific signals interviewers prioritize
You need a strategy that is:
Flexible, adaptive, and signal-aware.
The Core Mindset Shift
Most candidates prepare like this:
“I need to master ML interviews.”
Strong candidates prepare like this:
“I need to adapt my signals based on the team.”
This is a subtle but powerful shift.
The Adaptive Strategy Framework
To succeed across both roles, focus on five capabilities:
- Dual Framing Ability
- Context Detection
- Signal Switching
- Depth Reallocation
- Consistent Core Strengths
Capability 1: Dual Framing Ability
You should be able to explain the same system in two ways:
Product Framing
- Focus on user impact
- Highlight metrics
- Emphasize iteration
Example:
“We improved recommendation relevance, which increased engagement through iterative experimentation.”
Platform Framing
- Focus on system design
- Highlight scalability
- Emphasize reliability
Example:
“We built a scalable recommendation pipeline with modular components and monitoring for reliability.”
This is one of the highest-leverage skills you can develop.
Capability 2: Context Detection
Before answering deeply, identify:
What kind of team is this?
Look for signals:
Product Signals
- Mentions of users, metrics, experiments
- Questions about engagement or impact
Platform Signals
- Mentions of pipelines, infrastructure, scaling
- Questions about systems and reliability
If unclear, ask:
“Should I focus more on product impact or system design?”
This itself signals seniority.
Capability 3: Signal Switching
Once you detect context, adjust:
In Product Interviews
Emphasize:
- Metrics
- Iteration
- User impact
- Experimentation
In Platform Interviews
Emphasize:
- Architecture
- Scalability
- Reliability
- Reusability
This doesn’t mean changing your answer entirely.
It means shifting emphasis.
Capability 4: Depth Reallocation
Where you go deep should depend on the role.
Product Role Depth
Go deep on:
- Metrics
- Tradeoffs affecting users
- Experiment design
- Iteration cycles
Platform Role Depth
Go deep on:
- System architecture
- Scaling challenges
- Failure modes
- Monitoring
Use this rule:
Depth should align with value.
Capability 5: Consistent Core Strengths
Some signals are universal.
No matter the role, you must demonstrate:
- Structured thinking
- Clear communication
- Tradeoff awareness
- Ownership mindset
These are foundational.
We discussed these universal signals in What Makes a Candidate “Low Risk” in ML Hiring Decisions.
The Unified Answer Structure
You can combine both approaches into one adaptable structure:
- Problem Definition
- High-Level Approach
- Core Components
- Tradeoffs
- (Product or Platform Depth)
- Iteration / Monitoring
Example (Adaptable Answer)
“We design a recommendation system with candidate generation and ranking. We balance accuracy and latency based on constraints. For product impact, we measure engagement through A/B testing. From a platform perspective, we ensure scalability through modular design and monitoring.”
This allows you to:
- Cover both angles
- Adjust emphasis dynamically
How to Prepare Practically
Step 1: Reframe Your Projects
Take 2–3 past projects and rewrite them:
- Once from a product lens
- Once from a platform lens
This builds flexibility.
Step 2: Practice Dual Answers
For common questions:
- “Design a recommendation system”
- “Explain your project”
Practice answering:
- As a product engineer
- As a platform engineer
Step 3: Build Signal Awareness
During mock interviews, ask:
- What signals am I sending?
- Are they aligned with the role?
Step 4: Study Both Domains
You don’t need to specialize deeply in both.
But you should understand:
- Product metrics and experimentation
- Platform architecture and scaling
The Interviewer’s Perspective
At the end of the loop, hiring managers ask:
- Does this candidate fit our team’s needs?
- Will they contribute effectively?
- Do they understand our problems?
Your ability to adapt answers directly influences these decisions.
The Career-Level Insight
This distinction is not just about interviews.
It reflects two career paths:
Product ML Path
- Focus on impact
- Work closely with product teams
- Iterate rapidly
Platform ML Path
- Focus on systems
- Work across teams
- Build infrastructure
Understanding both makes you more versatile, and more valuable.
The Final Synthesis
Strong candidates:
- Recognize context quickly
- Adjust signals naturally
- Maintain clarity and structure
- Balance product and platform thinking
They don’t memorize answers.
They adapt intelligently.
Conclusion: It’s Not About Being Right - It’s About Being Relevant
The biggest mistake candidates make is assuming:
“If my answer is technically correct, I will pass.”
But interviews are not just about correctness.
They are about relevance.
The same answer can:
- Pass in one interview
- Fail in another
Depending on alignment.
To succeed, you must:
- Understand the team
- Adjust your thinking
- Signal the right strengths
Because in ML hiring:
The best candidate is not the smartest one.
It’s the one who fits the problem.
FAQs: AI Product vs ML Platform Interviews
1. How do I know if a role is product or platform?
Look at:
- Job description
- Team description
- Interview questions
Or ask directly.
2. Can I prepare for both at the same time?
Yes. Focus on:
- Core ML fundamentals
- Then layer role-specific thinking
3. Which is harder: product or platform interviews?
Neither is inherently harder. They test different skills.
4. Can I switch between product and platform roles?
Yes, but it requires adapting your skill set and thinking style.
5. What is the biggest mistake candidates make?
Using the same answers for both roles.
6. Do product roles require strong system design?
Yes, but focused on user impact rather than infrastructure depth.
7. Do platform roles require ML knowledge?
Yes, but focused on system integration rather than model tuning.
8. How important are metrics in product interviews?
Extremely important. They define success.
9. How important is scalability in platform interviews?
Critical. It is one of the main evaluation criteria.
10. Should I mention A/B testing in platform interviews?
Only if relevant, don’t force product concepts into platform answers.
11. Should I mention infrastructure in product interviews?
At a high level, but don’t overemphasize it.
12. How do I practice alignment?
Reframe the same problem from different perspectives.
13. What mindset should I adopt?
“What does this team care about?”
14. Are hybrid roles common?
Yes, especially in smaller companies.
You may need to demonstrate both skill sets.
15. What is the ultimate takeaway?
Success in ML interviews depends on alignment, not just ability.
