John Deere's tech-side interview loop, especially through its India Technology Center (JDTCI) and US software teams, is a fairly compact 2-round process: a first-stage online technical test (Cocubes or HackerRank, mixing CS-fundamentals MCQs with 1-3 coding problems) followed by a technical/behavioral round that leans hard on the candidate's resume and past projects, plus a few standard DSA and OOP questions. US software-engineering interns and new-grad SWEs report a hybrid pattern of one HackerRank OA plus a longer on-site or virtual panel that pairs a git/TDD-style engineering exercise with STAR-style behavioral questions, closer to Capital One's 'power day' format than to a FAANG loop. Timelines are moderate (about 10-25 days end-to-end) and interviewer feedback is described consistently as polite and structured.
These 6 writeups cover software engineering, machine learning, embedded systems and other roles, India JDTCI-heavy, some US SWE. 3 of them were posted under a forum handle, so we could not verify the authors’ identities.
New grad. 2 rounds. Shared by Anonymous on blog, 2022-09-08
1Video technical (medium)Technical60-minute video call with two coding problems: detect a cycle in an undirected graph, and find the maximum path sum between any two leaves of a binary tree. The interviewer expected optimized approaches (Floyd's algorithm for the graph, post-order traversal tracking left/right root-to-leaf sums for the tree).Covered: Graphs, Trees, Data structures & algorithms
2Video technical (easy)TechnicalAnother 60-minute video call with two problems: an Euler totient function variant returning integers whose phi values sum to N, and cycle detection in a singly linked list with two-pointer plus loop-start identification.Covered: Number theory, Linked lists, Data structures & algorithms
1Online technical testOnline assessmentNine-question test split into seven CS-fundamentals MCQs and two coding problems. Standard undergrad CS material rather than competitive-programming style.Covered: CS fundamentals, Data structures & algorithms, Aptitude
2Technical interview (Skype)TechnicalVirtual interview centred on OOP fluency (for example, which data structure to back an undo/redo stack), plus one strongly-connected-components question and one unbounded knapsack question. A resume/project deep-dive followed, and it closed with a lightweight design prompt asking the candidate to sketch the basic structure of an app to help immigrants find jobs during the lockdown.Covered: OOP, Data structures & algorithms, Graphs, Dynamic programming, Past projects, Low-level design
1Hiring manager conversationHiring managerA 30-minute call with the hiring manager covering the role, team structure and the candidate's background. Framed as mutual-fit rather than screening.Covered: Behavioral, Resume deep-dive, Team fit
2On-site behavioural + technical (2 hours)PanelFirst hour was behavioural, probing collaboration, growth mindset and technical curiosity, with the interviewer explicitly stating why each question was being asked and coaching the candidate to answer in individual-contribution terms. Second hour was a paired technical exercise: a Git flow challenge (branch, commit, push) and a TDD challenge on HackerRank where pre-written Java unit tests had to be made to pass.Covered: STAR stories, Behavioral, Git, Testing, Test-driven development
AI/ML Engineer (India, third-party sourced)
Not selected
Experienced. 2 rounds. Shared by Sumit Pardhiya on blog, 2025-11-10
1Recruiter intakePhone screenSourced via a Naukri invite from Deere's hiring team, then handled by a third-party recruiter who reviewed the resume, confirmed interest, and scheduled the first round about three days later.Covered: Resume deep-dive, Role fit
2Technical interviewTechnicalRoughly one hour. Opened with introduction and a project deep-dive (which the candidate found comfortable), then moved into progressively harder ML/DL questions - model evaluation, bias-variance, regularisation, then deeper advanced ML - and closed with one relatively easy DSA problem which the candidate solved.Covered: Past projects, Machine learning, Deep learning, Data structures & algorithms
1Online test (Cocubes, 60 minutes)Online assessmentRoughly 30 aptitude questions, 15 C-programming questions (pointers, structures, unions) and 10 ECE core questions covering material up to the fourth semester. CGPA cutoff of 8.0 was applied on top of the score.Covered: Aptitude, C programming, Pointers, Operating systems, Networking
2Technical interview (telephonic/Skype)TechnicalOne-on-one resume-driven discussion focused on the candidate's projects and general electronics-and-communication fundamentals, with follow-up questions calibrated to the projects listed.Covered: Resume deep-dive, Past projects, Domain knowledge
1Panel-day (three back-to-back interviews)PanelCandidate reported that Deere schedules three back-to-back interviews for US new-grad SWE, comparing the format to Capital One's 'power day', with no advance breakdown of which rounds are technical vs behavioural.Covered: Behavioral, Past projects, Team fit
What Deere is hiring for now
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John Deere's tech-side interview loop, especially through its India Technology Center (JDTCI) and US software teams, is a fairly compact 2-round process: a first-stage online technical test (Cocubes or HackerRank, mixing CS-fundamentals MCQs with 1-3 coding problems) followed by a technical/behavioral round that leans hard on the candidate's resume and past projects, plus a few standard DSA and OOP questions. US software-engineering interns and new-grad SWEs report a hybrid pattern of one HackerRank OA plus a longer on-site or virtual panel that pairs a git/TDD-style engineering exercise with STAR-style behavioral questions, closer to Capital One's 'power day' format than to a FAANG loop. Timelines are moderate (about 10-25 days end-to-end) and interviewer feedback is described consistently as polite and structured.
What topics does Deere test in interviews?
Commonly reported topics include Data structures & algorithms, OOP, Past projects, Resume deep-dive, Behavioral, STAR stories.
These guides summarize public, first-hand interview experiences shared by candidates. They describe the shape of each loop and the topics that came up, not a leaked question bank, and every experience links back to its original source. Processes change often. Treat this as directional prep, not a script.
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