Code clearly enough that another engineer can trust it.
Many ML roles use easy-to-medium coding questions, but some ML engineering positions use full software-engineering rounds. Verify the company’s loop before deciding the depth of preparation.
More than the final code
What is the interviewer observing while you solve a coding problem?
The coding interview loop
What early coding practice really looks like
Syntax disappears
Familiar operations become harder when a timer and another person are present.
Edge cases arrive late
The first solution may work only for the ordinary example.
Reasoning stays silent
A correct result can still hide whether the approach was deliberate.
Hints feel like failure
A hint is useful diagnostic data when the missing insight is recorded and revisited.
Speed varies
Pattern recognition grows unevenly; some problems remain slow after others become natural.
Old mistakes return
An error log matters only when repaired problems are solved again later.
From the interview room: a correct silent solution was less convincing than a slightly slower solution whose assumptions, tests and complexity were made visible. Communication is part of the solution.
Core preparation areas
Python fluency
Functions, iteration, comprehensions, sorting, standard collections and clean error handling.
Arrays and strings
Counting, prefix computations, two pointers and sliding windows.
Maps and sets
Fast lookup, frequency tables, grouping and duplicate detection.
Search and ordering
Binary search, heaps, top-k problems and custom sorting.
Structured data
Stacks, queues, linked structures, trees and basic graph traversal.
Role-specific coding
SQL, data transformations, numerical operations and simple ML functions.
Practice resources
LeetCode study plans
Structured problem sets for timed coding practice.
Tech Interview Handbook
A prioritized study path and pattern-oriented guidance.
Company-specific evidence
Use recruiter guidance and recent interview-loop information to adjust difficulty and topic weight.
Example communication pattern
A focused four-week plan
| Week | Focus | Evidence of progress |
|---|---|---|
| 1 | Python, arrays, strings, maps and sets | Explain and solve common easy patterns without reference |
| 2 | Two pointers, sliding window, binary search, stack and queue | Recognize the pattern before coding |
| 3 | Trees, graphs, heaps, SQL and role-specific transformations | Complete timed mixed practice |
| 4 | Mock rounds, error log and weak-pattern repair | Solve while communicating, testing and analysing complexity |
Maintain an error log: problem pattern, mistake, missing insight, corrected approach and the date it was solved again. Repeating the repair is more useful than accumulating solved-question counts.
A 30-day operating plan
Week 1 · Position
Choose role, analyse five JDs, update LinkedIn and create a defensible master resume.
Week 2 · Prepare
Revise high-priority ML gaps and complete one system-design drill.
Week 3 · Practise
Run two technical mocks, one behavioural mock and timed coding practice.
Week 4 · Execute
Apply thoughtfully, request suitable referrals and review conversion by stage.
The process that worked for me
The durable lesson: clearing strong interview processes did not come from discovering a secret list of questions. It came from repeatedly finding weaknesses before the actual interview had the opportunity to find them.
Commit before leaving
This week: ___________________________________
First mock interview date: _______________________
Evidence that preparation improved: ______________
Closing idea: diagnose the weakest stage, improve it with evidence, and repeat. Interview preparation becomes manageable when it is treated as a feedback system rather than an endless syllabus.