The New Definition of ‘Senior’ in ML Hiring: Expectations in 2026

Introduction

In 2026, “senior” no longer means what most ML engineers think it means.

For years, seniority in machine learning followed a predictable arc:

• More experience
• More complex models
• Larger systems
• Deeper specialization

If you had shipped models, led projects, and mentored others, “senior” felt like a natural title progression.

Today, that assumption is breaking.

Many experienced ML engineers are discovering, often painfully, that years of experience are no longer sufficient to signal seniority in hiring loops.

This isn’t because hiring bars are unfair.

It’s because the definition of value has shifted.

What Triggered the Redefinition of “Senior”

Three structural changes reshaped ML hiring:

1. Model Complexity Plateaued for Most Teams

Most companies are no longer differentiating on:

• Custom architectures
• Novel losses
• Training from scratch

Foundation models, managed platforms, and pretrained systems mean model choice is often obvious.

Senior engineers are no longer hired for model cleverness.

They’re hired for system reliability and judgment.

2. ML Systems Became Business-Critical Infrastructure

ML now:

• Drives revenue
• Shapes user trust
• Carries regulatory risk
• Operates continuously

When systems fail, consequences are visible.

Senior engineers are expected to prevent, detect, and recover from failure, not just build.

3. Interviews Shifted From Capability to Predictability

Hiring managers are no longer asking:

“Can this person build something impressive?”

They’re asking:

“Can I trust this person to make good decisions under uncertainty?”

This is a fundamentally different evaluation lens.

Why Many “Senior” ML Engineers Are Getting Stuck

The most common frustration I see in 2026 is this:

“I’ve been doing ML for years, but interviews feel stacked against me.”

What’s happening is not skill erosion.

It’s signal mismatch.

Many candidates still signal seniority through:

• Advanced algorithms
• Deep technical detail
• Optimization minutiae

Interviewers are now listening for:

• Tradeoff reasoning
• Failure anticipation
• Ownership language
• Business-aware decisions

When those signals don’t appear, candidates are leveled lower, or rejected, despite strong resumes.

Seniority Is No Longer About Scope Alone

In the past:

• Junior = implement
• Mid = design
• Senior = own larger systems

In 2026:

• Senior = decide under ambiguity
• Senior = own outcomes, not components
• Senior = optimize for system health over novelty

You can build large systems and still miss senior signals if:

• You avoid explicit tradeoffs
• You defer decisions to “best practices”
• You optimize locally instead of system-wide

The Hidden Question Behind Every Senior ML Interview

Every senior ML interview, regardless of company, is implicitly asking:

“If this system breaks at 2 a.m., do we trust this person’s judgment?”

That question drives:

• Follow-up depth
• Pushback style
• Evaluation criteria

Candidates who answer with certainty or perfection often score lower than those who answer with calm, bounded judgment.

Why This Change Feels Uncomfortable

This redefinition is uncomfortable because:

• It’s harder to study for
• It’s less concrete
• It can’t be memorized

You can’t cram judgment.

You have to demonstrate it.

That’s why many senior candidates feel interviews are “vague” or “subjective.”

They are, but not arbitrary.

They’re assessing decision quality.

What This Blog Will Clarify

This blog will break down:

1. What “senior” really signals in ML hiring today
2. How those signals show up in interviews
3. Where experienced candidates unintentionally fail
4. How to realign preparation for senior expectations

This is not about chasing titles.

It’s about understanding how value is measured now.

A Critical Reframe

If you remember one thing, remember this:

Senior ML engineers are not hired for knowing more.
They are hired for deciding better.

Once you prepare for that, senior interviews stop feeling opaque, and start feeling fair.

Section 1: The Core Signals That Define Senior ML Engineers in 2026

In 2026, seniority in ML is not inferred from your resume length, the number of models you’ve trained, or the complexity of algorithms you can discuss.

It is inferred from how you think when the problem is incomplete.

Hiring managers and interviewers consistently evaluate a small set of signals to decide whether a candidate operates at a senior level. These signals surface quickly, often within the first 20–30 minutes of a conversation.

If they don’t appear, experience alone does not compensate.

Signal 1: Explicit Tradeoff Reasoning (Not “Best Practices”)

Senior ML engineers do not default to:

• “Industry standard”
• “Best practice”
• “The most accurate model”

Instead, they ask:

• What are the constraints?
• What are we optimizing for?
• What are we willing to give up?

In interviews, senior candidates routinely:

• Compare multiple viable approaches
• Explain why they wouldn’t choose some options
• Acknowledge second-order effects (cost, latency, risk, maintenance)

This is one of the strongest senior signals because it shows decision ownership.

Mid-level candidates often describe what they’d build.
Senior candidates explain why they’d choose one path over another.

Signal 2: Comfort Operating Under Ambiguity

Senior ML engineers are comfortable when:

• Requirements are unclear
• Data is imperfect
• Metrics conflict
• Stakeholders disagree

Interviewers deliberately create ambiguity to see how candidates respond.

Senior candidates:

• Ask clarifying questions early
• Make reasonable assumptions
• State uncertainty explicitly
• Move forward without freezing

They don’t wait for perfect information.

This comfort under ambiguity is a recurring theme in modern ML interviews and is closely tied to how open-ended problems are evaluated, as discussed in How to Handle Open-Ended ML Interview Problems (with Example Solutions).

Signal 3: Ownership Language and Accountability

Senior engineers speak differently.

They say:

• “I would be responsible for…”
• “I’d want visibility into…”
• “I’d expect this to fail if…”

They do not hide behind:

• “The team decided”
• “We usually just…”
• “Someone else handled that”

This doesn’t mean claiming sole credit.

It means showing:

• Accountability for outcomes
• Awareness of downstream impact
• Willingness to own failure modes

Interviewers interpret this as predictability under pressure, one of the most valuable senior traits.

Signal 4: Failure Anticipation Before Optimization

Senior ML engineers think about failure before success.

In interviews, they naturally discuss:

• What could go wrong
• Where the system is fragile
• How issues would surface
• What they’d monitor first

Mid-level candidates often jump straight to optimization:

• Better models
• More features
• More data

Senior candidates start with:

• Guardrails
• Monitoring
• Rollback paths
• Safe defaults

This reversal in thinking is subtle but decisive.

Signal 5: System-Level Thinking Over Model-Centric Thinking

In 2026, senior ML engineers treat the model as one component, not the centerpiece.

They reason across:

• Data pipelines
• Feature generation
• Training workflows
• Inference behavior
• Monitoring and feedback loops

In interviews, this shows up as:

• Tracing issues across components
• Explaining interactions between nodes
• Recognizing non-obvious bottlenecks

Candidates who remain model-centric are often leveled lower, even if technically strong.

Signal 6: Pragmatism Over Technical Maximalism

Senior engineers optimize for outcomes, not elegance.

They:

• Choose simpler solutions when sufficient
• Avoid premature scaling
• Push back on unnecessary complexity
• Consider long-term maintenance cost

Interviewers look for statements like:

• “This is probably good enough given the constraints”
• “I’d start simple and only add complexity if needed”
• “The risk here isn’t accuracy, it’s reliability”

This pragmatism signals experience with real systems, not just theoretical ones.

Signal 7: Clear, Structured Communication Under Pushback

Senior candidates remain composed when interviewers:

• Challenge assumptions
• Introduce counterexamples
• Change constraints mid-discussion

They:

• Acknowledge the feedback
• Adjust reasoning
• Explain the revised approach clearly

They do not:

• Become defensive
• Abandon their thinking entirely
• Overcorrect dramatically

Interviewers use pushback intentionally to test emotional and intellectual stability, a core senior trait.

Signal 8: Awareness of Business and User Impact

Senior ML engineers understand that:

• Accuracy is not the only metric
• ML decisions affect users
• Tradeoffs have real consequences

In interviews, they naturally reference:

• User experience
• Revenue or cost impact
• Risk and trust considerations
• Operational constraints

This does not require business jargon.

It requires context awareness.

Section 1 Summary

In 2026, senior ML engineers are identified by:

• Explicit tradeoff reasoning
• Comfort with ambiguity
• Ownership language
• Failure anticipation
• System-level thinking
• Pragmatic decision-making
• Calm response to pushback
• Business and user awareness

None of these depend on:

• Years of experience alone
• Advanced algorithms
• Specialized tooling

They depend on judgment.

That is the new senior bar.

Section 2: How Senior ML Expectations Show Up in Interviews (and Where Candidates Fail)

Most ML engineers who get down-leveled or rejected at the senior bar don’t fail because they lack knowledge.

They fail because the signals interviewers are listening for never appear.

Senior expectations show up in interviews in predictable patterns. Once you know where to look, interviews stop feeling vague, and start feeling diagnostic.

Where Interviewers Actually Test “Senior”

Senior expectations rarely appear as:

• Harder math
• Trick questions
• Exotic architectures

Instead, they show up as:

• Open-ended prompts
• Constraint changes
• Follow-up pressure
• “What would you do next?” questions

These are judgment tests, not recall tests.

Pattern 1: Open-Ended System or ML Design Questions

Example prompt:

“Design an ML system to detect fraud / rank content / recommend products.”

What mid-level candidates do:

• Jump into model choice
• Describe features
• Optimize accuracy early

What senior candidates do:

• Clarify the goal and constraints
• Ask about failure tolerance
• Identify business risk
• Choose a reasonable baseline
• Explain tradeoffs explicitly

Where candidates fail:

• Treating the question as a build exercise instead of a decision exercise
• Optimizing prematurely
• Avoiding explicit tradeoffs

Interviewers aren’t grading the architecture, they’re grading how you reason.

Pattern 2: “What Would You Do If…” Follow-Ups

Senior interviews rely heavily on follow-ups:

• “What if data quality degrades?”
• “What if latency doubles?”
• “What if metrics conflict?”
• “What if this fails silently?”

Mid-level reaction:

• Add complexity
• Suggest retraining
• Propose a new model

Senior reaction:

• Pause
• Reframe the problem
• Consider monitoring and rollback
• Decide whether action is even required

Where candidates fail:

• Treating every issue as a modeling problem
• Assuming intervention is always necessary

Senior engineers know that doing nothing can be the correct decision.

Pattern 3: Ambiguous or Underspecified Questions

Example:

“How would you evaluate this model?”

There is no single correct answer.

Interviewers are watching:

• Whether you ask clarifying questions
• Whether you define success criteria
• Whether you acknowledge limitations

Mid-level mistake:

• Listing metrics mechanically
• Reciting textbook definitions

Senior signal:

• Connecting metrics to decisions
• Explaining when metrics fail
• Discussing tradeoffs and blind spots

This distinction is central to how modern interviews are evaluated, as explained in The Hidden Metrics: How Interviewers Evaluate ML Thinking, Not Just Code.

Pattern 4: Pushback and Constraint Changes

Interviewers intentionally challenge candidates:

• “That won’t scale.”
• “We don’t have labels.”
• “We can’t tolerate false positives.”

This is not confrontation.

It’s calibration.

Where candidates fail:

• Becoming defensive
• Overcorrecting dramatically
• Abandoning their original reasoning

Senior behavior:

• Acknowledge the constraint
• Adjust the approach calmly
• Explain why the new direction still makes sense

The ability to recover gracefully is a strong senior signal.

Pattern 5: Ownership and Responsibility Probes

Interviewers often ask:

• “What would you monitor?”
• “What would you do if this went wrong?”
• “How would you explain this to stakeholders?”

Mid-level answers:

• Focus on implementation
• Assume someone else handles ops or comms

Senior answers:

• Anticipate failure modes
• Discuss alerting and rollback
• Explain communication strategy
• Frame decisions in terms of impact

Where candidates fail:

• Avoiding ownership language
• Treating operations and communication as “someone else’s job”

Senior engineers are evaluated on end-to-end responsibility, not just delivery.

Pattern 6: Business and Product Context Questions

Senior ML interviews often include:

• “Why does this metric matter?”
• “Who is impacted if this fails?”
• “What’s the cost of being wrong?”

Common failure mode:

• Treating these as distractions from “real ML”

In 2026, this is backwards.

Senior ML engineers are expected to:

• Align ML decisions with product goals
• Understand downstream consequences
• Balance technical and business tradeoffs

Ignoring context is interpreted as immaturity, not focus.

Pattern 7: The “What Would You Do Differently?” Question

This question appears deceptively simple.

Interviewers are listening for:

• Self-critique
• Learning mindset
• Real-world experience

Weak answers:

• “I’d just use a better model”
• “I’d add more data”

Strong answers:

• Identify an assumption that might break
• Explain what they’d watch in production
• Acknowledge uncertainty

Senior candidates demonstrate reflective judgment, not hindsight perfection.

Why Experienced Candidates Still Miss These Signals

Even seasoned ML engineers fail senior interviews because they:

• Optimize for correctness instead of reasoning
• Hide uncertainty instead of bounding it
• Over-focus on technical detail
• Avoid stating tradeoffs explicitly

None of these indicate lack of ability.

They indicate misaligned signaling.

Section 2 Summary

Senior ML expectations show up in interviews through:

• Open-ended design prompts
• Constraint changes and pushback
• Ambiguous evaluation questions
• Ownership and failure discussions
• Business and product framing

Candidates fail not by being wrong, but by:

• Skipping tradeoffs
• Over-optimizing
• Avoiding ambiguity
• Failing to own decisions

Once you recognize these patterns, senior interviews become predictable and navigable.

Section 3: Why Experienced ML Engineers Get Down-Leveled (and How to Avoid It)

Down-leveling in 2026 is rarely a surprise to hiring teams, but it is often a shock to candidates.

Engineers with 6–10+ years of ML experience are increasingly offered:

• Mid-level roles instead of senior
• Senior roles instead of staff
• “Strong hire, wrong level” feedback

This is not because experience stopped mattering.

It’s because experience alone no longer communicates seniority.

Down-Leveling Is a Signal Mismatch, Not a Skill Judgment

Hiring committees don’t down-level candidates to be conservative.

They do it because the interview evidence does not support the senior risk profile.

The internal question is not:

“Is this person good?”

It’s:

“Is there enough signal that this person will make the right calls without close oversight?”

When the answer is “maybe,” leveling drops.

Reason 1: Experience Is Described, Not Demonstrated

Many experienced candidates rely on statements like:

• “I led multiple ML projects”
• “I owned end-to-end pipelines”
• “I mentored junior engineers”

These are claims, not signals.

Interviewers are looking for:

• How you decide when priorities conflict
• How you handle incomplete data
• How you trade accuracy for reliability
• How you respond when a system fails

If your answers stay descriptive instead of decision-focused, the committee cannot justify a senior level, regardless of years.

How to avoid it:
Anchor every story in decisions you made, not responsibilities you held.

Reason 2: Over-Indexing on Technical Depth, Under-Indexing on Judgment

Experienced ML engineers often default to:

• Deep dives into algorithms
• Optimization details
• Architectural sophistication

Ironically, this can hurt senior evaluation.

Why?

Because senior ML interviews are not testing how much you know.
They’re testing how you choose.

When candidates:

• Optimize prematurely
• Add complexity without constraints
• Avoid stating tradeoffs

Interviewers infer that the candidate:

• May struggle to prioritize
• May over-engineer
• May require guidance

This is a classic down-leveling trigger.

How to avoid it:
State constraints early. Explain why you’re not choosing more complex options.

Reason 3: Lack of Failure Ownership

Senior ML engineers are expected to anticipate and own failure.

Down-leveled candidates often:

• Talk about success paths only
• Attribute failures to data or stakeholders
• Avoid discussing what went wrong

Interviewers interpret this as:

• Limited production exposure
• Weak accountability
• Risk under pressure

In contrast, senior candidates naturally say:

• “This would likely fail here…”
• “The first thing I’d watch is…”
• “If this regressed, I’d roll back before retraining…”

This difference is subtle, but decisive.

How to avoid it:
Proactively discuss failure modes and recovery plans, even if not asked.

Reason 4: Speaking as a Contributor, Not an Owner

Another common down-leveling pattern is language.

Experienced candidates often say:

• “The team decided…”
• “We usually did…”
• “Someone else handled monitoring…”

Interviewers are listening for ownership language, not collaboration disclaimers.

This does not mean taking undue credit.

It means demonstrating:

• Accountability for outcomes
• Willingness to make calls
• Comfort with responsibility

When ownership is unclear, committees hedge with lower levels.

How to avoid it:
Use “I” when discussing decisions you influenced or owned. Be precise about scope.

Reason 5: Misaligned Seniority Expectations Across Companies

A subtle but common issue: seniority is relative.

A “senior” ML engineer at:

• A startup
• A small data team
• A research-heavy org

May not map cleanly to senior expectations at:

• Large tech companies
• Platform teams
• Business-critical ML orgs

Candidates assume titles transfer directly. Interviewers do not.

This mismatch often leads to down-leveling despite strong performance.

How to avoid it:
Prepare for role-specific senior expectations, not title-based ones, especially at companies that emphasize decision-making and risk management, as outlined in What FAANG Recruiters Really Look for in ML Engineers.

Reason 6: Avoiding Explicit Tradeoffs to Sound Confident

Many experienced candidates fear that stating tradeoffs:

• Makes them sound unsure
• Weakens their position

So they present one “confident” solution.

In 2026, this backfires.

Interviewers expect senior engineers to:

• Acknowledge uncertainty
• Compare viable paths
• Choose deliberately

A single-path answer with no tradeoffs is interpreted as immaturity, not confidence.

How to avoid it:
Explicitly compare options, even briefly, and explain your choice.

Reason 7: Senior Signals Appear Late or Not at All

Finally, some candidates do have senior judgment, but it appears:

• Only near the end of the interview
• Only if heavily prompted
• Only in one round

Leveling decisions require consistent signal across interviews.

If senior traits appear inconsistently, committees default lower.

How to avoid it:
Surface senior signals early and often:

• Frame problems
• State assumptions
• Discuss risk
• Own decisions

Do not wait to be asked.

Section 3 Summary

Experienced ML engineers get down-leveled when they:

• Describe experience instead of demonstrating judgment
• Over-optimize technically
• Avoid discussing failure
• Speak without ownership
• Assume titles transfer
• Hide tradeoffs
• Surface senior signals too late

Avoiding down-leveling is not about:

• More preparation
• More credentials
• More complexity

It’s about making senior decision-making visible.

Once that happens, leveling conversations change dramatically.

Section 4: How to Prepare for Senior ML Interviews Without Over-Studying

Senior ML interview preparation fails when it looks like junior preparation, just more of it.

If your prep plan is:

• More algorithms
• More frameworks
• More practice problems

You are optimizing the wrong axis.

In 2026, senior interviews reward decision quality, not coverage. Preparing effectively means training how you think, not what you memorize.

Stop Studying “New Stuff” by Default

A common senior-candidate trap is chasing novelty:

• New model architectures
• New tooling stacks
• New buzzwords

This creates two problems:

1. Shallow understanding under follow-ups
2. Missed opportunities to demonstrate judgment

Interviewers do not award points for novelty. They award points for fit under constraints.

What to do instead:
Pick a small, familiar set of tools and approaches and practice explaining why you’d choose them, and when you wouldn’t.

Depth beats novelty at the senior bar.

Replace Content Review With Decision Drills

Senior interviews are decision drills disguised as technical questions.

You should practice:

• Framing ambiguous problems
• Stating assumptions explicitly
• Comparing options quickly
• Choosing a path and defending it

A simple drill:

1. Take a common ML problem (ranking, detection, forecasting).
2. Write down three viable approaches.
3. For each, note:
   • Pros
   • Cons
   • Failure modes
   • When it’s the wrong choice
4. Choose one and explain why, out loud.

This trains the exact muscles interviewers test.

Practice Saying “It Depends” (Correctly)

“It depends” is a senior phrase, but only when followed by structure.

Senior candidates use it to:

• Clarify constraints
• Surface tradeoffs
• Narrow the decision

Junior candidates use it to avoid commitment.

Practice the senior version:

“It depends on X and Y. If X matters more, I’d choose A; if Y dominates, I’d choose B. Given the constraints you mentioned, I’d start with A.”

This shows judgment, not indecision.

Rehearse Failure Before Success

Most candidates rehearse:

• Ideal designs
• Happy paths
• Perfect data

Senior interviews probe the opposite.

You should rehearse:

• What breaks first
• How issues surface
• What you monitor
• When you roll back
• When you don’t intervene

A good senior answer often starts with:

“The biggest risk here isn’t the model, it’s…”

This anticipatory framing immediately signals seniority.

Use Fewer Examples-Explain Them Better

Senior candidates hurt themselves by:

• Listing many projects
• Jumping between examples
• Overloading detail

Interviewers prefer:

• One or two concrete examples
• Clear decisions
• Honest tradeoffs

Pick one project you know deeply and practice explaining:

• Why you chose the approach
• What you didn’t do (and why)
• What failed or almost failed
• What you’d change now

This aligns with how senior signals are evaluated across interviews, as discussed in How to Discuss Real-World ML Projects in Interviews (With Examples).

Train Your Opening Moves

Senior signals should appear early.

Practice starting answers with:

• Problem framing
• Constraints
• Success criteria

Instead of:

“I’d use model X with features Y…”

Start with:

“First I’d clarify the goal and constraints. The main tradeoff here is…”

Interviewers often form level hypotheses in the first 10 minutes. Don’t wait to sound senior.

Time-Box Prep to Avoid Over-Optimization

Senior candidates often over-prepare because:

• They know more
• They see more edge cases
• They fear missing something

Set explicit limits:

• Fixed prep window (e.g., 2–3 weeks)
• Defined outcomes (“I can reason through X”)
• Clear stop conditions

Over-preparing reduces clarity and increases second-guessing.

Practice Pushback, Not Perfection

Senior interviews include pushback by design.

You should practice:

• Being challenged
• Adapting calmly
• Revising assumptions

In mock interviews, ask your partner to:

• Change constraints mid-answer
• Question your choice
• Introduce a failure scenario

The goal is not to be right.

It’s to be composed and deliberate.

What Senior Candidates Should Explicitly Stop Doing

Stop:

• Memorizing rare algorithms
• Chasing every new trend
• Over-engineering solutions
• Avoiding uncertainty
• Waiting to be asked about tradeoffs

These behaviors signal insecurity, not seniority.

Section 4 Summary

To prepare for senior ML interviews in 2026:

• Stop optimizing for coverage
• Train decision-making, not recall
• Practice tradeoffs and ambiguity
• Rehearse failure and recovery
• Use fewer examples, explained better
• Surface senior signals early
• Time-box preparation
• Practice pushback

Senior prep is about alignment, not accumulation.

Once you train the right signals, interviews become far less exhausting, and far more predictable.

Conclusion: Senior ML Engineers Are Hired for Judgment, Not Just Experience

In 2026, the word “senior” in machine learning no longer describes how long you’ve worked or how complex your models are.

It describes how you decide when the answer isn’t obvious.

Senior ML engineers are trusted to:

• Make tradeoffs under ambiguity
• Anticipate failure before it happens
• Optimize for system health, not local elegance
• Balance accuracy, cost, latency, and risk
• Own outcomes, not just components

This shift explains why:

• Some highly experienced engineers get down-leveled
• Some less experienced candidates pass senior bars
• Interviews feel more subjective than before

They are not subjective.

They are judgment tests.

Once you stop trying to prove how much you know, and start showing how you think, senior interviews become clearer and fairer.

The new senior bar is not higher.

It is different.

FAQs on Senior ML Hiring in 2026

1. Does “senior” still correlate with years of experience?

Loosely. Years help, but judgment and decision quality matter more.

2. Why do some candidates with fewer years pass senior interviews?

They surface tradeoffs, ownership, and system thinking more clearly.

3. Is deep algorithm knowledge still required at the senior level?

Baseline knowledge is expected, but algorithm depth alone won’t carry the interview.

4. Why do interviews feel more ambiguous now?

Because ambiguity is how judgment is tested.

5. What’s the fastest way to sound senior in interviews?

Frame problems, state constraints, and explain tradeoffs early.

6. Should I apply for senior roles if I’m unsure I meet the bar?

If you meet most role expectations, yes, let the interview calibrate.

7. Is down-leveling a failure?

No. It reflects perceived risk, not your overall capability.

8. Can down-leveling be reversed later?

Often yes, once trust is established internally.

9. How much system design should senior ML engineers know?

Enough to reason end-to-end, not to design infra from scratch.

10. Do senior ML engineers need business knowledge?

They need awareness, not an MBA.

11. What’s the biggest red flag at the senior bar?

Avoiding explicit tradeoffs or uncertainty.

12. How do interviewers test ownership?

Through failure scenarios, monitoring questions, and follow-up pressure.

13. Should senior candidates aim for perfect answers?

No. Calm, reasoned answers outperform perfect ones.

14. How do I know if I’m “senior enough” for a role?

If you can make and defend decisions under ambiguity, you’re close.

15. What mindset shift matters most in 2026 ML hiring?

Stop proving expertise. Start demonstrating judgment.

Final Takeaway

In 2026, senior ML engineers are not defined by:

• Titles
• Tenure
• Technical maximalism

They are defined by trustworthiness under uncertainty.

If you prepare to demonstrate judgment, not just knowledge, you’ll find that senior interviews stop being opaque and start being navigable.

That is the new definition of senior.