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Globus Medical Software Engineer Interview Guide

Globus Medical Software Engineer interview guide: reported rounds, questions and how candidates describe the process.

The 60-second brief

The Software Engineer at Globus Medical plays a pivotal role in the development of innovative medical devices and software solutions that enhance patient care. This position is integral to the company's mission of advancing surgical and spinal technologies by providing robust software that ensures precision, safety, and efficiency in medical procedures. As a Software Engineer, you will contribute to projects that directly impact the lives of patients and healthcare professionals, making your work both meaningful and rewarding. At Globus Medical, you will engage with cross-functional teams, collaborating closely with hardware engineers, product managers, and clinical experts. Your contributions will directly influence the design and functionality of products, ensuring they meet the highest standards of quality and reliability.

This guide is scoped to a Software Engineer candidate at Globus Medical.

Focus areas

C++
Class design (OOP)
C++ basics / fundamentals

The Globus Medical interview process

Globus Medical candidates report 5 rounds over 4-6 weeks. The stages below are what candidates describe, not a published process.

Initial Screening

The process begins with initial screenings to assess candidate qualifications.

What to demonstrate

The process begins with initial screenings to assess candidate qualifications
Depth in C++

How to prepare

Be able to walk your CV end to end in two minutes, and say why this company specifically.
Have your salary expectations, notice period and location constraints ready, and ask for the rest of the loop in writing.

Phone Interviews

Candidates engage in phone interviews with HR and technical managers.

What to demonstrate

Candidates engage in phone interviews with HR and technical managers
Depth in C++

How to prepare

Answer aloud and timed: Describe a challenging technical problem you encountered and how you resolved it.
Answer aloud and timed: How do you ensure code quality and maintainability?

In-Person Interviews

Candidates participate in in-person interviews with engineering teams.

What to demonstrate

Candidates participate in in-person interviews with engineering teams
Depth in C++

How to prepare

Answer aloud and timed: What methodologies do you have experience with (e.g., Agile, Waterfall)?
Answer aloud and timed: How would you design a software system for a medical device?

Technical Evaluation

The interviews include technical questions to evaluate knowledge and skills.

What to demonstrate

The interviews include technical questions to evaluate knowledge and skills
Depth in C++

How to prepare

Answer aloud and timed: What factors do you consider when designing scalable systems?
Answer aloud and timed: Can you describe a time when you had to make trade-offs in system design?

Behavioral Evaluation

Candidates face behavioral questions to assess cultural fit within the company.

What to demonstrate

Candidates face behavioral questions to assess cultural fit within the company
Depth in C++

How to prepare

Prepare three examples from your own work, each with a decision you made and an outcome you can quantify.
Re-read the description of the behavioral evaluation above and write down what you would ask to confirm before it.

Where candidates lose points

Research the company

Familiarize yourself with Globus Medical's products, mission, and recent innovations. This knowledge will help you align your answers with the company's goals.

Practice coding challenges

If your role involves programming, practice common coding problems and be prepared for live coding interviews.

Prepare for behavioral questions

Reflect on past experiences and how they relate to the values and culture at Globus Medical. Use the STAR method (Situation, Task, Action, Result) to structure your responses.

Show passion for healthcare

Demonstrating a genuine interest in medical technology and patient care can set you apart from other candidates.

Questions & practice

Write a function to reverse a string.

Medium · Coding / Algorithms

Approach

Restate the input: its shape, its size, and what is guaranteed about it.
Name the brute-force solution and its complexity before improving on it.
Choose the data structure from the access pattern, not from familiarity.
State the target complexity and say which constraint rules the naive version out.

Follow-up

How does this change if the input no longer fits in memory?
What is the worst case, and how likely is it on real data?

Collapse a redelivered event batch into per-aggregate high-water marks

Easy · hashing · at-least-once · aggregation

You drain a batch of up to 5,000,000 events, each (aggregate_id BIGINT, aggregate_version INT, event_type, payload). The log guarantees order within one aggregate only; the batch merges 64 partitions, and a relay failover has redelivered a range, so an older version for an aggregate can appear after a newer one. Given a map of last_applied_version per aggregate, produce the events worth applying, at most one per (aggregate_id, version), plus the count discarded. Target O(n) time. State the memory for 2,000,000 distinct aggregates and what you do when it does not fit.

Approach

One pass, one hash map from aggregate_id to the highest version kept, and a discard counter. An event whose version is at or below last_applied_version for its aggregate is dropped without further work, which is the whole reason the event carries its version rather than a delta. O(n) expected time, O(d) space in distinct aggregates.
Keep the maximum, never the last occurrence. The redelivered range means the final appearance of an aggregate in the batch can be an older version than one seen earlier in the same batch, so last-wins applies stale state over newer state and the projection regresses with no error anywhere.
Cost the memory instead of calling it large: an 8-byte key plus a 4-byte version is 12 bytes of payload, and an open-addressed table held at a 0.7 load factor costs roughly 17 bytes per entry before per-slot metadata, so 2,000,000 aggregates is tens of megabytes in a native layout and several times that in a runtime that boxes both key and value.
If the distinct set exceeds memory, partition on hash(aggregate_id) mod P and reduce each partition independently. Every event for one aggregate hashes to the same partition, so the per-partition result is exact and the merge is concatenation rather than a second reduction.

Follow-up

The payload is a patch rather than a snapshot, so applying only the highest version loses the intermediate changes. What changes in your reduction?
How do you detect that version 7 arrived while version 6 was never delivered, and what should the consumer do about the gap?

Archive a resource graph without breaking live references or recursing

Medium · graph traversal · topological order · tenant isolation

Resources reference other resources within a tenant; for the largest tenant the reference table holds up to 2,000,000 nodes and 8,000,000 edges. Archiving a resource must archive everything reachable from it that nothing outside the set still references, refuse when a live external referrer exists, and terminate when references form cycles, which they legitimately do. Produce the archive order and the refusal list, targeting O(V+E). Say what stops the traversal crossing a tenant boundary, and why recursion is the wrong control structure at this size.

Approach

Load the subgraph with the tenant predicate on both endpoints of the edge, not only on the side you started from. Scoping the left table alone is the classic cross-tenant leak: one mis-entered edge then pulls another tenant's resources into the traversal and, worse, into the archive.
Traverse iteratively with an explicit stack. A 2,000,000-node graph can hold a chain deep enough to exhaust a native stack in the low tens of thousands of frames, and that failure is a process crash rather than an error you can return.
Treat cycles as data rather than corruption: compute strongly connected components with Tarjan in O(V+E) using its own explicit stack, then condense. The condensation is a DAG, so a topological order over it gives the archive order, and every member of a component archives in one transaction because no order within a cycle is valid.
Decide refusals with reverse edges. A candidate is archivable only if every in-edge originates inside the candidate set, so build the transpose or count in-degrees restricted to the visited set, and emit each blocked resource with the id of the external referrer, which is the only part of the answer an operator can act on.

Follow-up

The graph is read in one query and the archive writes a minute later. What can change in between, and how do you make the write safe?
The candidate set is 400,000 resources. Is that one transaction, and if not, what does a half-finished archive look like to a reader?

How would you optimize a database query to improve performance?

Medium · Coding / Algorithms

Approach

Name the grain you start from and join outward from it.
Check whether any join is one-to-many before aggregating, or the sums inflate.
Say which index the query would use, and what makes it unusable.
Handle the rows that do not match: that is usually the actual question.

Follow-up

How does the query change if that join becomes one-to-many?
What happens to this when the table is ten times larger?

Explain why the owner filter ignores the listing index

Medium · composite index · query plans · sort elimination

The only index on resource is (tenant_id, status, updated_at DESC, resource_id DESC). A new endpoint returns one user's resources across all statuses, newest created first: WHERE tenant_id = $1 AND owner_user_id = $2 ORDER BY created_at DESC LIMIT 20. On a tenant with 2M rows it takes 900 ms and EXPLAIN shows a sort above a large scan. Explain precisely why the existing index cannot serve it, give the index that can, and state which of these the new index still will not help: owner_user_id alone across tenants; the same query ordered by updated_at. PostgreSQL 16.

Approach

Separate the two jobs an index does. For filtering, a composite btree is seekable only on a left prefix, so with no predicate on status the scan can at best range over tenant_id and test owner_user_id per row; PostgreSQL 16 has no btree skip scan to jump the unconstrained column.
For ordering, the index is sorted by (status, updated_at) within a tenant and not by created_at, so the LIMIT cannot stop early: every matching row is read and then sorted. That is the 'Sort Method: top-N heapsort' line, and it is why the plan reads 2M rows to answer with 20.
Derive the replacement from the access path — equality, equality, then the ordering column: CREATE INDEX CONCURRENTLY ON resource (tenant_id, owner_user_id, created_at DESC). The scan seeks to the (tenant, owner) range and walks 20 entries in order, so the Sort node disappears along with the row-read.
Treat INCLUDE (title, status) as conditional, not free. An index-only scan still visits the heap for any row whose page is not marked all-visible, so on a table taking 1.2k writes/second the win depends on autovacuum keeping the visibility map current, and the wider index costs more on every insert.

Follow-up

90% of rows are status='active'. Would a partial index WHERE status = 'active' change your answer, and for which of the three queries?
A dashboard runs this for 40 owners in one page load. What changes about the design?