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Complete Genomics Software Engineer Interview Guide

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

The 60-second brief

As a Software Engineer at Complete Genomics, you will build the mission-critical software that directly powers next-generation DNA sequencing and advanced sample preparation platforms. This role sits at a highly sophisticated intersection of software engineering, hardware control, and life sciences. The systems you design and maintain are responsible for sub-millisecond real-time instrumentation control, high-throughput data acquisition, and complex robotic orchestration. Your work will have a direct impact on the speed, accuracy, and accessibility of genomic sequencing worldwide. At Complete Genomics, software is not an isolated utility; it is the vital link that translates physical biological processes into digital data.

This guide is scoped to a Software Engineer candidate at Complete Genomics.

Focus areas

  • Real-time systems
  • Hardware control software
  • C# /.NET

The Complete Genomics interview process

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

Initial Screening Call

First call conducted by a talent acquisition specialist or service department manager to assess candidate fit.

What to demonstrate

  • First call conducted by a talent acquisition specialist or service department manager to assess candidate fit
  • Depth in Real-time systems

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 Phone Interview

Discussion with a Department VP or hiring manager about past experiences and technical aspects of systems built.

What to demonstrate

  • Discussion with a Department VP or hiring manager about past experiences and technical aspects of systems built
  • Depth in Real-time systems

How to prepare

  • Answer aloud and timed: In your past experience, how did you balance the need for rapid feature development with maintaining high code quality and automated test coverage?
  • Answer aloud and timed: Explain a scenario where you had to refactor a legacy codebase. What design patterns did you implement to improve its maintainability?

Onsite Interview

Comprehensive interviews at the San Jose, CA or Palo Alto offices with 6 to 7 team members, focusing on past work and collaboration.

What to demonstrate

  • Comprehensive interviews at the San Jose, CA or Palo Alto offices with 6 to 7 team members
  • Focusing on past work and collaboration

How to prepare

  • Answer aloud and timed: How do you manage multithreading and prevent race conditions when writing software that controls real-time hardware instruments?
  • Answer aloud and timed: Imagine a scenario where an instrument sensor is sending data faster than your application can process it. How would you design a buffer or queue system to handle this?

Where candidates lose points

Master Your Resume Details

Every interviewer you meet will likely have read your resume closely. Be prepared to explain the "why" behind every technology choice, architectural decision, and project challenge listed on your application.

Emphasize Hardware-Software Interaction

Whenever you discuss your software engineering experience, highlight how your code interacted with physical systems, managed memory, or handled real-time constraints.

Prepare for Cross-Disciplinary Questions

Practice explaining complex technical concepts using analogies that a non-software stakeholder (like a biologist or laboratory technician) can easily understand.

Verify Logistics Early

Ensure you confirm all interview details, platform links (such as MS Teams), and travel reimbursement policies in writing with your coordinator well in advance of your sessions.

Questions & practice

How do you manage multithreading and prevent race conditions when writing software that controls real-time har

Medium · Instrument Control & Systems Engineering

How do you manage multithreading and prevent race conditions when writing software that controls real-time hardware instruments?

Approach

  1. Say what the runtime actually does before reasoning about the code.
  2. Name what is shared across threads and what owns each piece of state.
  3. Identify the window where an invariant is briefly untrue.
  4. Distinguish a value from a reference to it, and say which one you handed out.

Follow-up

  • What happens if two callers reach this at the same time?
  • Where could this allocate more than you expect?

Track a rolling failure rate per destination for circuit decisions

Easy · sliding window · ring buffer · circuit breaker

The egress service delivers about 1,500 webhooks per second across roughly 40,000 destinations, each call bounded by a 10 second timeout. Maintain, per destination, the failure rate over the trailing 60 seconds so a caller can ask before dispatch whether the circuit should open. Attempts arrive as (destination_id, finished_at_ms, outcome). Requirement: amortised O(1) per attempt, with total memory bounded by the destination count rather than by traffic. Give the structure, its exact memory, and the rule that stops a destination with three attempts from opening a circuit.

Approach

  1. Name the exact-deque version and then reject it as the default. Holding timestamps and advancing a tail pointer past anything older than now minus 60 seconds is a correct two-pointer window at amortised O(1) per attempt, but its memory tracks in-window traffic, so one destination in a retry storm holds hundreds of thousands of entries while thousands of quiet destinations hold none.
  2. Use a ring of 60 one-second buckets per destination, each bucket a pair of counters for attempts and failures. On an attempt, advance the ring by the elapsed whole seconds, zeroing at most min(elapsed, 60) buckets, then increment the head. That is amortised O(1) with a fixed footprint per destination.
  3. State the footprint: 60 buckets times two 4-byte counters is 480 bytes of payload per destination, so 40,000 destinations is roughly 20 to 25 MB with per-entry overhead, bounded by the catalogue rather than by the rate. The cost is granularity, since the oldest bucket ages out in whole seconds, which is far tighter than the decision needs.
  4. Require a minimum sample before the circuit may open. A destination with three attempts and three failures reads as 100 percent and is not evidence; a floor of roughly 20 attempts in the window makes the ratio meaningful, and below that floor use a run of consecutive failures as the trigger instead.

Follow-up

  • The fleet is 30 instances and each sees roughly a thirtieth of a destination's traffic. Where does the rate actually live, and what does a per-instance answer get wrong?
  • A destination answers in 9.5 seconds and succeeds. It is not failing but it is consuming your per-destination concurrency. What signal should open the circuit here?

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

  1. 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.
  2. 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.
  3. 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.
  4. 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?

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?

Hold a per-tenant active cap against concurrent creates

Hard · write skew · isolation levels · constraints

A tenant on the standard plan may hold at most 50 resources with status='active'. The create handler runs SELECT count(*) FROM resource WHERE tenant_id = $1 AND status = 'active', compares to 50, then inserts. Two creates arrive 3 ms apart on different instances and the tenant lands at 51. Name the anomaly, say whether PostgreSQL 16 READ COMMITTED or REPEATABLE READ prevents it and why, then give an implementation that holds the cap at READ COMMITTED with the exact statements. Finally, say what changes when the cap is 'at most one running export per tenant' on job_run.

Approach

  1. Name it: write skew. The two transactions read an overlapping set and write disjoint rows, so there is no row-level conflict for the engine to detect and each commit is individually legal.
  2. Rule out the levels precisely. READ COMMITTED takes a fresh snapshot per statement and takes no lock on the counted rows, so both see 49. PostgreSQL's REPEATABLE READ is snapshot isolation: it removes non-repeatable reads and phantoms within the snapshot but still admits write skew, because the anomaly is not a re-read of a changed row, it is a read of a set that a concurrent transaction invalidates. Only SERIALIZABLE closes it, by tracking the read dependency and aborting one transaction with SQLSTATE 40001 — a guarantee that exists only if the application re-runs the whole transaction from the read.
  3. Convert the set predicate into a single-row conflict: keep tenant.active_resource_count and run UPDATE tenant SET active_resource_count = active_resource_count + 1 WHERE tenant_id = $1 AND active_resource_count < 50 in the same transaction as the INSERT. Zero affected rows is the cap, returned as 409. The row lock serialises the decision at any isolation level, and contention is bounded to one tenant's row — which is also the fair-scheduling unit, unlike a global counter that would convoy every tenant behind one row.
  4. State the cost you just took on: a counter is a second source of truth that can drift, so every path that changes status must adjust it inside the same transaction, and a periodic reconciliation has to exist, with resource_revision as the authority for what the count should have been.

Follow-up

  • A resource moves from archived back to active. Which statements change, and what breaks if the counter update and the status change land in different transactions?
  • The cap becomes plan-dependent and a plan can change mid-month. Where does the number 50 live, and who reads it?

Walk me through the architecture of the most complex software system you have built. What were the primary bot

Medium · Resume & Experience Deep-Dive

Walk me through the architecture of the most complex software system you have built. What were the primary bottlenecks, and how did you resolve them?

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?

In your past experience, how did you balance the need for rapid feature development with maintaining high code

Medium · Resume & Experience Deep-Dive

In your past experience, how did you balance the need for rapid feature development with maintaining high code quality and automated test coverage?

Approach

  1. Clarify what is being asked and what a complete answer contains.
  2. State your assumptions explicitly before working the problem.
  3. Say what you would check first and why it is the highest-information step.
  4. Work from the requirement backwards to the design.

Follow-up

  • What assumption would you test first?
  • How would you know your answer was wrong?

Explain a scenario where you had to refactor a legacy codebase. What design patterns did you implement to impr

Medium · Resume & Experience Deep-Dive

Explain a scenario where you had to refactor a legacy codebase. What design patterns did you implement to improve its maintainability?

Approach

  1. Clarify what is being asked and what a complete answer contains.
  2. State your assumptions explicitly before working the problem.
  3. Say what you would check first and why it is the highest-information step.
  4. Work from the requirement backwards to the design.

Follow-up

  • What assumption would you test first?
  • How would you know your answer was wrong?

Imagine a scenario where an instrument sensor is sending data faster than your application can process it. How

Medium · Instrument Control & Systems Engineering

Imagine a scenario where an instrument sensor is sending data faster than your application can process it. How would you design a buffer or queue system to handle this?

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 the technical mechanics of a specific instrument or hardware interface you have worked with in the pas

Medium · Instrument Control & Systems Engineering

Explain the technical mechanics of a specific instrument or hardware interface you have worked with in the past. How did your software communicate with it?

Approach

  1. Clarify what is being asked and what a complete answer contains.
  2. State your assumptions explicitly before working the problem.
  3. Say what you would check first and why it is the highest-information step.
  4. Work from the requirement backwards to the design.

Follow-up

  • What assumption would you test first?
  • How would you know your answer was wrong?

What strategies do you use to ensure software reliability and fault tolerance in systems where a single softwa

Medium · Instrument Control & Systems Engineering

What strategies do you use to ensure software reliability and fault tolerance in systems where a single software crash could ruin an expensive biological sample?

Approach

  1. Clarify what is being asked and what a complete answer contains.
  2. State your assumptions explicitly before working the problem.
  3. Say what you would check first and why it is the highest-information step.
  4. Work from the requirement backwards to the design.

Follow-up

  • What assumption would you test first?
  • How would you know your answer was wrong?

Describe a challenging project where you had to debug a system-level issue. What tools and methodologies did y

Medium · Resume & Experience Deep-Dive

Describe a challenging project where you had to debug a system-level issue. What tools and methodologies did you use to isolate the root cause?

Approach

  1. Establish what changed and when, before forming any theory.
  2. Pick a bisection that eliminates candidates whichever way it turns out.
  3. Check the instrumentation before believing the symptom.
  4. Separate the trigger from the cause; the deploy is rarely the bug.

Follow-up

  • What would you look at first, and what would it rule out?
  • How would you tell a cause from a coincidence here?

Suggested practice checklist

Built from the rounds and topics Complete Genomics candidates report.

Day 1 — Map the Complete Genomics loop

  • Write out the reported sequence: Initial Screening Call, Technical Phone Interview, Onsite Interview.
  • 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 Real-time systems

  • Spend the session on Real-time systems, which Complete Genomics candidates report being tested on.
  • Write one worked example in Real-time systems and time yourself on it.

Deliverable: One timed worked example in Real-time systems.

Day 3 — Work Hardware control software

  • Spend the session on Hardware control software, which Complete Genomics candidates report being tested on.
  • Write one worked example in Hardware control software and time yourself on it.

Deliverable: One timed worked example in Hardware control software.

Day 4 — Work C# /.NET

  • Spend the session on C# /.NET, which Complete Genomics candidates report being tested on.
  • Write one worked example in C# /.NET and time yourself on it.

Deliverable: One timed worked example in C# /.NET.

Day 5 — Answer out loud: Resume & Experience Deep-Dive

  • Answer aloud, timed: Walk me through the architecture of the most complex software system you have built. What were the primary bottlenecks, and how did you resolve them?
  • Answer aloud, timed: Describe a challenging project where you had to debug a system-level issue. What tools and methodologies did you use to isolate the root cause?

Deliverable: Spoken answers to 2 reported Resume & Experience Deep-Dive question(s), under time.

Day 6 — Answer out loud: Instrument Control & Systems Engineering

  • Answer aloud, timed: How do you manage multithreading and prevent race conditions when writing software that controls real-time hardware instruments?
  • Answer aloud, timed: Imagine a scenario where an instrument sensor is sending data faster than your application can process it. How would you design a buffer or queue system to handle this?

Deliverable: Spoken answers to 2 reported Instrument Control & Systems Engineering question(s), under time.

Day 7 — Answer out loud: Behavioral & Team Collaboration

  • Answer aloud, timed: Have you ever worked with scientists or hardware engineers who had different priorities or technical vocabularies? How did you bridge the communication gap to align on project requirements?
  • Answer aloud, timed: How do you handle a situation where a project requirement changes mid-development due to a shift in hardware specifications or scientific findings?

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

Behavioural preparation

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

Have you ever worked with scientists or hardware engineers who had different priorities or technical vocabular

Medium · Behavioral & Team Collaboration

Have you ever worked with scientists or hardware engineers who had different priorities or technical vocabularies? How did you bridge the communication gap to align on project requirements?

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 a situation where a project requirement changes mid-development due to a shift in hardware s

Medium · Behavioral & Team Collaboration

How do you handle a situation where a project requirement changes mid-development due to a shift in hardware specifications or scientific findings?

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 when you had to defend a technical design decision to your team or department heads. What was

Medium · Behavioral & Team Collaboration

Describe a time when you had to defend a technical design decision to your team or department heads. What was the outcome?

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?

If you do not have a background in biology, how do you plan to adapt to working on software that directly inte

Medium · Behavioral & Team Collaboration

If you do not have a background in biology, how do you plan to adapt to working on software that directly interfaces with DNA sequencing chemistry?

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?