cswork / libraryOpen practice workspace

Interview guides · Last Energy

Last Energy Software Engineer Interview Guide

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

The 60-second brief

As a Software Engineer at Last Energy, you will build the digital infrastructure that powers the future of private nuclear energy. Last Energy is pioneering micro-nuclear reactor technology, which means our software engineering team is tasked with solving incredibly complex, high-stakes problems. You will work on software systems that bridge the gap between heavy industrial hardware and modern, cloud-based monitoring, simulation, and control platforms. This role is critical to the business because our reactors require ultra-reliable, real-time data pipeline execution, robust safety-system simulations, and highly efficient internal tooling. The systems you design and maintain will directly impact our ability to deploy, monitor, and scale nuclear power plants globally.

This guide is scoped to a Software Engineer candidate at Last Energy.

Focus areas

  • Data Structures & Algorithms (DSA)
  • Problem Solving
  • Coding Interview Preparation

The Last Energy interview process

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

Recruiter Screen

Initial screening by a recruiter to assess candidate fit for the role.

What to demonstrate

  • Initial screening by a recruiter to assess candidate 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 Conversation

Discussion with the hiring manager to evaluate background and team alignment.

What to demonstrate

  • Discussion with the hiring manager to evaluate background and team alignment
  • Depth in Data Structures & Algorithms (DSA)

How to prepare

  • Answer aloud and timed: Write an algorithm to find the shortest path in a network of power distribution nodes, handling dynamic weight changes.
  • Answer aloud and timed: Explain how you would optimize a recursive function to prevent stack overflow in a memory-constrained environment.

Technical Screen

Focused assessment on data structures and algorithms to test coding fundamentals.

What to demonstrate

  • Focused assessment on data structures and algorithms to test coding fundamentals
  • Depth in Data Structures & Algorithms (DSA)

How to prepare

  • Answer aloud and timed: Design a telemetry ingestion pipeline capable of processing millions of data points per second from distributed physical assets.
  • Answer aloud and timed: How would you architect a secure, remote monitoring system for industrial hardware that must operate reliably over low-bandwidth connections?

Executive Screen

Fast-paced interview, often with the CEO, to gauge overall fit and communication.

What to demonstrate

  • Fast-paced interview, often with the CEO, to gauge overall fit and communication
  • Depth in Data Structures & Algorithms (DSA)

How to prepare

  • Answer aloud and timed: Design a distributed configuration management system that ensures all micro-reactor nodes maintain consistent state.
  • Answer aloud and timed: Walk us through your approach to building a real-time alerting system that minimizes false positives while ensuring critical safety alerts are never missed.

Practical Design Round

Intensive session where candidates solve a complex problem and present solutions.

What to demonstrate

  • Intensive session where candidates solve a complex problem and present solutions
  • Depth in Data Structures & Algorithms (DSA)

How to prepare

  • Answer aloud and timed: Tell me about a time you had to make a critical technical decision with incomplete data. What was the outcome?
  • Answer aloud and timed: How do you handle feedback or pushback from stakeholders who may not have a deep technical background?

Where candidates lose points

Be prepared for directness

The culture at Last Energy is highly transparent and direct. During your interviews, expect interviewers to challenge your assumptions, ask follow-up questions rapidly, and push you to defend your engineering decisions. Treat these moments as collaborative brainstorming sessions rather than personal critiques.

The executive interview is exceptionally fast-paced

Avoid long-winded introductions or overly detailed background explanations. Get straight to the point of your answers within the first two sentences.

Practice live-recorded design

Since the final technical round requires you to solve an architecture problem with your screen recorded, practice this format beforehand. Speak your thoughts aloud as you design, document your assumptions clearly on a virtual whiteboard, and structure your final presentation to be highly polished.

Questions & practice

Implement a function to detect cycles in a directed graph, and discuss its time and space complexity.

Medium · Data Structures & Algorithms

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?

Write an algorithm to find the shortest path in a network of power distribution nodes, handling dynamic weight

Medium · Data Structures & Algorithms

Write an algorithm to find the shortest path in a network of power distribution nodes, handling dynamic weight changes.

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 how you would optimize a recursive function to prevent stack overflow in a memory-constrained environm

Medium · Data Structures & Algorithms

Explain how you would optimize a recursive function to prevent stack overflow in a memory-constrained environment.

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?

Write the update path that detects a concurrent edit

Medium · optimistic concurrency · transactional outbox · audit history

resource carries version INT NOT NULL DEFAULT 1. resource_revision holds revision_id, resource_id, version, actor_user_id, change_kind, patch JSONB, request_id, created_at with UNIQUE (resource_id, version). outbox_event holds aggregate_type, aggregate_id, aggregate_version, event_type, payload, status. A PUT carries the version the client read. Write the exact statements for the single transaction that applies the edit, records the revision and enqueues 'resource.updated', and give the handler's branch on zero affected rows. Then say what PostgreSQL 16 does under READ COMMITTED when two of these updates hit one row at once.

Approach

  1. One transaction, three writes, no network call inside it: UPDATE resource SET title = $3, version = version + 1, updated_at = now() WHERE resource_id = $1 AND tenant_id = $4 AND version = $2; then INSERT the resource_revision row at version $2 + 1; then INSERT the outbox_event row at the same aggregate_version. The event goes to a table rather than a broker because no transaction spans both.
  2. Branch on the affected-row count before doing anything else. Zero has three causes — stale version, wrong tenant, row gone — so re-read once and map to 409 carrying the current version, or 404 for an id outside the caller's tenant, which also stops the endpoint confirming that another tenant's id exists.
  3. State the engine behaviour instead of assuming it. Under READ COMMITTED the second UPDATE blocks on the row lock, and when the first commits PostgreSQL re-evaluates the WHERE clause against the newly committed row, so the version predicate now fails and the statement reports zero rows. Under REPEATABLE READ the identical collision raises SQLSTATE 40001 instead, so the handler must fold both shapes into one conflict response.
  4. Keep UNIQUE (resource_id, version) even though the predicate already serialises writers. It is what makes a lost update unwritable if any other path ever reaches the revision table, and it converts a logic bug into 23505 rather than into a silently missing history row.

Follow-up

  • A client sends the version it read ten minutes ago and the resource has moved three versions. What is in your 409 so it can resolve the conflict without a full re-fetch?
  • Two editors, two disjoint fields, no overlap. Does your answer still refuse the second write, and should it?

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 an efficient in-memory data store for real-time sensor data with high write throughput?

Medium · Data Structures & Algorithms

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?

Design a telemetry ingestion pipeline capable of processing millions of data points per second from distribute

Medium · System Design & Case Presentation

Design a telemetry ingestion pipeline capable of processing millions of data points per second from distributed physical assets.

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?

How would you architect a secure, remote monitoring system for industrial hardware that must operate reliably

Medium · System Design & Case Presentation

How would you architect a secure, remote monitoring system for industrial hardware that must operate reliably over low-bandwidth connections?

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?

Design a distributed configuration management system that ensures all micro-reactor nodes maintain consistent

Medium · System Design & Case Presentation

Design a distributed configuration management system that ensures all micro-reactor nodes maintain consistent state.

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?

Walk us through your approach to building a real-time alerting system that minimizes false positives while ens

Medium · System Design & Case Presentation

Walk us through your approach to building a real-time alerting system that minimizes false positives while ensuring critical safety alerts are never missed.

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?

p99 jumped on one listing filter while p50 stayed flat

Medium · query planning · composite index · percentiles

After a release that added an owner_user_id filter to the resource listing, p99 rose from 90 ms to 1.9 s while p50 stayed at 40 ms. Traffic and row counts are unchanged. resource carries the index (tenant_id, status, updated_at DESC, resource_id DESC). The new query filters tenant_id and owner_user_id, orders by updated_at DESC, resource_id DESC, and takes 20 rows. On PostgreSQL, explain the shape of the regression, prove it from a query plan, and give the index you would add.

Approach

  1. Start from the shape. A flat p50 with a moved p99 means a subset of requests changed cost, not all of them, so the first job is naming the subset. Bucket the endpoint's latency by the tenant's row count; the natural hypothesis is that large tenants are a small share of requests and all of the tail.
  2. Get the plan for the new query on a large tenant with EXPLAIN (ANALYZE, BUFFERS). Expect an index scan over the tenant's range, a filter discarding most of it, then a Sort feeding the Limit, possibly reporting Sort Method: external merge Disk. Read actual rows on the scan node, not estimated.
  3. Explain why the existing index cannot serve it. A composite B-tree is seekable only as a left prefix, and with no equality predicate on status the scan cannot treat updated_at as an ordering, because rows in the tenant's range are ordered by status first. Everything matching must be read and sorted before LIMIT 20 can apply, so a tenant with 400,000 rows pays 400,000 rows to return 20.
  4. Add (tenant_id, owner_user_id, updated_at DESC, resource_id DESC). Equality on the first two columns leaves the index ordered by updated_at within that pair, so the plan becomes an index scan that stops after 20 rows with no Sort node. PostgreSQL can scan a B-tree backwards, so the DESC markers matter only if the two sort columns ever disagree in direction; keeping them explicit documents the order the keyset cursor depends on.

Follow-up

  • The endpoint paginates with OFFSET. What does page 500 cost with your index, and what does the keyset version cost?
  • How would you have caught this before release, given that a 10,000-row seed database produces the same plan shape at an unnoticeable cost?

Suggested practice checklist

Built from the rounds and topics Last Energy candidates report.

Day 1 — Map the Last Energy loop

  • Write out the reported sequence: Recruiter Screen, Technical Conversation, Technical Screen, Executive Screen, Practical Design Round.
  • 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 5 reported rounds, with the weakest marked.

Day 2 — Work Data Structures & Algorithms (DSA)

  • Spend the session on Data Structures & Algorithms (DSA), which Last Energy 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 Problem Solving

  • Spend the session on Problem Solving, which Last Energy candidates report being tested on.
  • Write one worked example in Problem Solving and time yourself on it.

Deliverable: One timed worked example in Problem Solving.

Day 4 — Work Coding Interview Preparation

  • Spend the session on Coding Interview Preparation, which Last Energy candidates report being tested on.
  • Write one worked example in Coding Interview Preparation and time yourself on it.

Deliverable: One timed worked example in Coding Interview Preparation.

Day 5 — Answer out loud: Data Structures & Algorithms

  • Answer aloud, timed: Implement a function to detect cycles in a directed graph, and discuss its time and space complexity.
  • Answer aloud, timed: How would you design an efficient in-memory data store for real-time sensor data with high write throughput?

Deliverable: Spoken answers to 2 reported Data Structures & Algorithms question(s), under time.

Day 6 — Answer out loud: System Design & Case Presentation

  • Answer aloud, timed: Design a telemetry ingestion pipeline capable of processing millions of data points per second from distributed physical assets.
  • Answer aloud, timed: How would you architect a secure, remote monitoring system for industrial hardware that must operate reliably over low-bandwidth connections?

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

Day 7 — Answer out loud: Behavioral & Executive Alignment

  • Answer aloud, timed: Tell me about a time you had to make a critical technical decision with incomplete data. What was the outcome?
  • Answer aloud, timed: How do you handle feedback or pushback from stakeholders who may not have a deep technical background?

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

Behavioural preparation

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

Tell me about a time you had to make a critical technical decision with incomplete data. What was the outcome?

Medium · Behavioral & Executive Alignment

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 feedback or pushback from stakeholders who may not have a deep technical background?

Medium · Behavioral & Executive Alignment

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 situation where you had to quickly pivot your engineering approach due to a change in physical or h

Medium · Behavioral & Executive Alignment

Describe a situation where you had to quickly pivot your engineering approach due to a change in physical or hardware constraints.

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 manage your workflow and maintain code quality when working under tight, high-pressure deadlines?

Medium · Behavioral & Executive Alignment

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?