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MD-Staff Software Engineer Interview Guide

MD-Staff Software Engineer interview guide: reported rounds, questions and how candidates describe the process.

The 60-second brief

As a Software Engineer at MD-Staff, you are at the heart of our mission to streamline and automate the complex credentialing and medical staff management processes for healthcare organizations. Your work directly impacts the efficiency of hospitals and the safety of patient care by ensuring that medical practitioners are verified, compliant, and ready to serve. This role is not just about writing code; it is about building robust, scalable systems that handle high-stakes data with precision and reliability. You will contribute to a product ecosystem that serves as the backbone for healthcare administration. Whether you are working on our core platform, integrating with external medical databases, or enhancing our user-facing interfaces, your solutions will solve real-world problems for healthcare professionals.

This guide is scoped to a Software Engineer candidate at MD-Staff.

Focus areas

  • Data Structures & Algorithms (DSA)
  • System Design
  • Live Coding

The MD-Staff interview process

MD-Staff candidates report 3 rounds over 3-5 weeks. The stages below are what candidates describe, not a published process.

Recruiter Screen

Initial contact with a recruiter to discuss the candidate's background and fit for the role.

What to demonstrate

  • Initial contact with a recruiter to discuss the candidate's background and fit for the role
  • Depth in Data Structures & Algorithms (DSA)

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.

Technical Assessments

Includes take-home challenges, live coding, and system design sessions to evaluate technical skills.

What to demonstrate

  • Includes take-home challenges, live coding, and system design sessions to evaluate technical skills
  • Depth in Data Structures & Algorithms (DSA)

How to prepare

  • Answer aloud and timed: How do you handle edge cases such as empty inputs, null pointers, or integer overflows during a live coding challenge?
  • Answer aloud and timed: Explain the mechanism behind a Hashmap and how you would optimize its performance for large datasets.

Behavioral Interviews

Interviews with leadership to assess cultural alignment and soft skills.

What to demonstrate

  • Interviews with leadership to assess cultural alignment and soft skills
  • Depth in Data Structures & Algorithms (DSA)

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 interviews above and write down what you would ask to confirm before it.

Where candidates lose points

Speak your mind

During coding rounds, explain your logic as you go. We are interested in your problem-solving process, not just the final output.

Embrace the STAR method

When answering behavioral questions, keep your responses structured and focused on your specific actions and the resulting impact.

Prepare for culture fit

Familiarize yourself with our company values. We look for team players who are eager to learn and willing to support their colleagues.

Ask insightful questions

Use the time at the end of your interviews to ask about the team’s current technical challenges or the company’s roadmap. This demonstrates engagement and strategic thinking.

Questions & practice

Implement a function to find the longest evenly spaced increasing subsequence in an array.

Medium · Technical & Coding

Approach

  1. Restate the input: its shape, its size, and what is guaranteed about it.
  2. Name the brute-force solution and its complexity before improving on it.
  3. Choose the data structure from the access pattern, not from familiarity.
  4. 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?

Describe how you would approach a string manipulation problem involving palindromes or complex sorting.

Medium · Technical & Coding

Approach

  1. Restate the input: its shape, its size, and what is guaranteed about it.
  2. Name the brute-force solution and its complexity before improving on it.
  3. Choose the data structure from the access pattern, not from familiarity.
  4. 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?

Explain the mechanism behind a Hashmap and how you would optimize its performance for large datasets.

Medium · Technical & Coding

Approach

  1. Restate the input: its shape, its size, and what is guaranteed about it.
  2. Name the brute-force solution and its complexity before improving on it.
  3. Choose the data structure from the access pattern, not from familiarity.
  4. 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?

Given a set of requirements, how do you translate them into clean, maintainable, and modular code?

Medium · Technical & Coding

Approach

  1. Restate the input: its shape, its size, and what is guaranteed about it.
  2. Name the brute-force solution and its complexity before improving on it.
  3. Choose the data structure from the access pattern, not from familiarity.
  4. 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?

How do you approach database schema design for a platform that requires high data integrity?

Medium · System Design & Architecture

Approach

  1. Name the grain you start from and join outward from it.
  2. Check whether any join is one-to-many before aggregating, or the sums inflate.
  3. Say which index the query would use, and what makes it unusable.
  4. 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?

Stop tag and share joins from fanning out a page

Medium · join fan-out · semi-joins · many-to-many

resource_tag is (resource_id, tag_id) with PK (resource_id, tag_id); resource_share is (resource_id, shared_with_user_id, permission). The tagged-and-shared listing inner-joins resource to both, filters tenant_id, tag_id = ANY($2) and shared_with_user_id = $3, orders by updated_at DESC and takes 50. Pages come back with fewer than 50 distinct resources and the total in the header is far too high. Explain the row multiplication, rewrite both the page query and the count query so each is correct, and name the index each one needs. PostgreSQL 16.

Approach

  1. Do the arithmetic against the predicates that are actually there. An inner join emits one row per matching child row, and both joins are filtered: tag_id = ANY($2) admits only the requested tags, shared_with_user_id = $3 admits one user's share rows. So a resource holding three of the requested tags and shared with $3 once yields three rows, not one — the multiplier is its count of matching tags times its share rows for that single user, and that second factor is 1 unless the table admits duplicate (resource_id, shared_with_user_id) pairs. LIMIT 50 then limits rows rather than resources, and COUNT(*) counts pairs — the header is the product, not the population.
  2. Reject DISTINCT as the fix. It deduplicates after the product has been built, so the planner must materialise and sort the fanned-out set before the LIMIT can apply, and it leaves any SUM or AVG in the same select list wrong.
  3. Rewrite both filters as semi-joins, keeping resource as the only row source: AND EXISTS (SELECT 1 FROM resource_tag rt WHERE rt.resource_id = r.resource_id AND rt.tag_id = ANY($2)) and the same shape against resource_share. A semi-join stops at the first match per resource and preserves the driving index order, so ORDER BY updated_at DESC, resource_id DESC LIMIT 50 still stops after 50 rows.
  4. Count with the same predicates and no join at all: SELECT count(*) FROM resource r WHERE r.tenant_id = $1 AND r.status = 'active' AND EXISTS (...) AND EXISTS (...). Nothing multiplies a resource, so the number is the population.

Follow-up

  • The filter changes from 'any of these tags' to 'all of these tags'. Rewrite it and state what it costs relative to the ANY form.
  • A resource can be shared with the same user twice under different permissions. Does your count change, and should it?

How would you design a scalable system similar to a video streaming service or a high-traffic marketplace?

Medium · System Design & Architecture

Approach

  1. Fix the scope first: who calls this, how often, and what they do when it fails.
  2. Name the read and write paths separately; they rarely have the same bottleneck.
  3. Choose a partition key and say what query it makes expensive.
  4. State the consistency you need, and where you are willing to be stale.

Follow-up

  • What breaks first when traffic grows ten times?
  • How does this behave when that dependency is down for an hour?

Explain how you would implement fault tolerance and monitoring in a distributed cloud environment.

Medium · System Design & Architecture

Approach

  1. Fix the scope first: who calls this, how often, and what they do when it fails.
  2. Name the read and write paths separately; they rarely have the same bottleneck.
  3. Choose a partition key and say what query it makes expensive.
  4. State the consistency you need, and where you are willing to be stale.

Follow-up

  • What breaks first when traffic grows ten times?
  • How does this behave when that dependency is down for an hour?

What strategies do you use to manage system latency and throughput during peak usage?

Medium · System Design & Architecture

Approach

  1. Fix the scope first: who calls this, how often, and what they do when it fails.
  2. Name the read and write paths separately; they rarely have the same bottleneck.
  3. Choose a partition key and say what query it makes expensive.
  4. State the consistency you need, and where you are willing to be stale.

Follow-up

  • What breaks first when traffic grows ten times?
  • How does this behave when that dependency is down for an hour?

One customer endpoint stalls deliveries to every other destination

Medium · bulkhead · timeouts · saturation

The egress service delivers about 1.5k webhooks/second across 40,000 destinations, with a per-destination concurrency cap of 4 and a 10-second connect-plus-read timeout. Throughput falls to 300/second, queue depth climbs, and p99 delivery latency for unaffected destinations goes from 200 ms to minutes, while the error rate barely moves. One tenant holds 900 destination rows whose URLs share a hostname that now answers in 9.5 seconds. Explain the mechanism with the arithmetic, then give the containment in the order you would apply it.

Approach

  1. Look at saturation before errors. A flat error rate with collapsing throughput says nothing is failing, things are waiting, so the first signal to pull is in-flight request count or pool wait time rather than the error counter. This is the distinction that decides the whole investigation.
  2. Group in-flight work by resolved host, not by destination id. The cap is keyed per destination row, so 900 rows sharing one hostname buy 3,600 concurrent slots against a single host, each held for 9.5 seconds. The bulkhead was never a bulkhead for that host, and grouping by the wrong dimension is why the dashboard looked healthy.
  3. Do the arithmetic in both directions. Required concurrency is arrival rate times latency, so 1.5k/second at 200 ms needs about 300 in flight, which is entirely consumed by 3,600 slow slots; conversely whatever concurrency is left sustains rate equals concurrency divided by 9.5 seconds, which is the 300/second you are seeing. Matching both numbers is what promotes this from a plausible story to the mechanism.
  4. Explain why the circuit breaker never helped. It opens on consecutive failures, and a 9.5-second response inside a 10-second timeout is a success. Slow is not failing, so an error-rate breaker cannot see this; you need a slow-call ratio, a deadline propagated from the caller's remaining budget, or a concurrency limiter.

Follow-up

  • The host recovers to 80 ms. How long does the queue take to drain, and what does the drain do to the recovered host?
  • Where should the 10-second timeout number actually come from?

Suggested practice checklist

Built from the rounds and topics MD-Staff candidates report.

Day 1 — Map the MD-Staff loop

  • Write out the reported sequence: Recruiter Screen, Technical Assessments, Behavioral Interviews.
  • For each round, write one sentence on what it is judging, from the description above, and mark the one you are least ready for.

Deliverable: A one-page map of the 3 reported rounds, with the weakest marked.

Day 2 — Work Data Structures & Algorithms (DSA)

  • Spend the session on Data Structures & Algorithms (DSA), which MD-Staff candidates report being tested on.
  • Write one worked example in Data Structures & Algorithms (DSA) and time yourself on it.

Deliverable: One timed worked example in Data Structures & Algorithms (DSA).

Day 3 — Work System Design

  • Spend the session on System Design, which MD-Staff candidates report being tested on.
  • Write one worked example in System Design and time yourself on it.

Deliverable: One timed worked example in System Design.

Day 4 — Work Live Coding

  • Spend the session on Live Coding, which MD-Staff candidates report being tested on.
  • Write one worked example in Live Coding and time yourself on it.

Deliverable: One timed worked example in Live Coding.

Day 5 — Answer out loud: Technical & Coding

  • Answer aloud, timed: Implement a function to find the longest evenly spaced increasing subsequence in an array.
  • Answer aloud, timed: Describe how you would approach a string manipulation problem involving palindromes or complex sorting.

Deliverable: Spoken answers to 2 reported Technical & Coding question(s), under time.

Day 6 — Answer out loud: System Design & Architecture

  • Answer aloud, timed: How would you design a scalable system similar to a video streaming service or a high-traffic marketplace?
  • Answer aloud, timed: How do you approach database schema design for a platform that requires high data integrity?

Deliverable: Spoken answers to 2 reported System Design & Architecture question(s), under time.

Day 7 — Answer out loud: Behavioral & Leadership

  • Answer aloud, timed: Describe a time you had to resolve a conflict within your team. How did you handle the differing opinions?
  • Answer aloud, timed: Tell me about a project where you had to pivot your approach due to changing requirements.

Deliverable: Spoken answers to 2 reported Behavioral & Leadership question(s), under time.

Behavioural preparation

Behavioural rounds judge the decision you made and what it cost.

How do you handle edge cases such as empty inputs, null pointers, or integer overflows during a live coding ch

Medium · Technical & Coding

How do you handle edge cases such as empty inputs, null pointers, or integer overflows during a live coding challenge?

Approach

  1. Pick a story where you made the decision, not one where you watched it.
  2. State the situation in two sentences and spend the rest on the reasoning.
  3. Give the blast radius: what could have broken, and what you measured.
  4. Name the disagreement and how you resolved it with evidence.

Follow-up

  • What would you do differently if you ran that again?
  • How did you know your change caused the improvement?

Describe a time you had to resolve a conflict within your team. How did you handle the differing opinions?

Medium · Behavioral & Leadership

Approach

  1. Pick a story where you made the decision, not one where you watched it.
  2. State the situation in two sentences and spend the rest on the reasoning.
  3. Give the blast radius: what could have broken, and what you measured.
  4. Name the disagreement and how you resolved it with evidence.

Follow-up

  • What would you do differently if you ran that again?
  • How did you know your change caused the improvement?

Tell me about a project where you had to pivot your approach due to changing requirements.

Medium · Behavioral & Leadership

Approach

  1. Pick a story where you made the decision, not one where you watched it.
  2. State the situation in two sentences and spend the rest on the reasoning.
  3. Give the blast radius: what could have broken, and what you measured.
  4. Name the disagreement and how you resolved it with evidence.

Follow-up

  • What would you do differently if you ran that again?
  • How did you know your change caused the improvement?

How do you handle constructive criticism from a peer during a code review?

Medium · Behavioral & Leadership

Approach

  1. Pick a story where you made the decision, not one where you watched it.
  2. State the situation in two sentences and spend the rest on the reasoning.
  3. Give the blast radius: what could have broken, and what you measured.
  4. Name the disagreement and how you resolved it with evidence.

Follow-up

  • What would you do differently if you ran that again?
  • How did you know your change caused the improvement?

Can you share an example of how you took ownership of a critical issue and saw it through to resolution?

Medium · Behavioral & Leadership

Approach

  1. Pick a story where you made the decision, not one where you watched it.
  2. State the situation in two sentences and spend the rest on the reasoning.
  3. Give the blast radius: what could have broken, and what you measured.
  4. Name the disagreement and how you resolved it with evidence.

Follow-up

  • What would you do differently if you ran that again?
  • How did you know your change caused the improvement?