As a Software Engineer at Radical Ai, you will build the foundational systems that are fundamentally disrupting how physical materials are discovered and manufactured. The traditional materials R&D process is notoriously slow and capital-intensive, often taking up to 10 years and $100 million to bring a single new discovery to market. Radical Ai is replacing this outdated approach by leveraging artificial intelligence, machine learning, and advanced robotic automation to invent, simulate, and physically synthesize novel materials in a matter of weeks. Depending on your specific focus—whether it is platform, systems, or generalist software engineering—your work will bridge the digital and physical worlds. You might design the core platform services and deployment primitives that enable ML teams to ship quickly, orchestrate the complex distributed workflows that power autonomous labs, or develop low-level drivers that interface directly with physical laboratory instruments and robotic workcells. This is a highly collaborative environment where your software directly controls physical systems in the real world, making system reliability, correctness, and performance absolutely paramount.
Initial Screening Call
reportedA call with a recruiter or hiring manager to align on your background and the role's expectations.
What to demonstrate
- A call with a recruiter or hiring manager to align on your background and the role's expectations
- Depth in Backend engineering (production 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 Deep Dive
reportedIn-depth discussions with technical leadership or the CEO about past architectural decisions and systems thinking.
What to demonstrate
- In-depth discussions with technical leadership or the CEO about past architectural decisions and systems thinking
- Depth in Backend engineering (production systems)
How to prepare
- Answer aloud and timed: What are the trade-offs between using gRPC and REST APIs, particularly in a hybrid cloud and on-premise networking environment?
- Answer aloud and timed: How do you implement idempotency and safe retry mechanisms in distributed task queues?
Practical Technical Assessment
reportedSolve a realistic problem to showcase your coding, design, and debugging skills.
What to demonstrate
- Solve a realistic problem to showcase your coding, design, and debugging skills
- Depth in Backend engineering (production systems)
How to prepare
- Answer aloud and timed: Describe a scenario where you had to debug a performance bottleneck or memory leak in a production backend service.
- Answer aloud and timed: How would you design a robust driver interface for a laboratory instrument that communicates over Modbus or TCP/IP?
PracHub editorial advice for the preparation topics above.
Highlight Real-World Constraints
When designing systems, explicitly mention how you would handle network drops, hardware latency, and partial system failures. This demonstrates that you have "production instincts" and understand that software in an autonomous lab controls physical hardware.
Radical Ai values clean, reusable code abstractions
During your coding sessions, prioritize writing clean, readable code over hyper-optimized, complex one-liners.
Showcase Cross-Disciplinary Empathy
Be prepared to explain how you communicate technical limitations to non-technical team members. Your ability to collaborate with materials scientists and roboticists is just as important as your coding ability.
Choose a category, try a prompt, then open its approach, worked solution or follow-up when you need it.
How do you manage concurrency and handle cancellation semantics in Python or Go when dealing with long-running
How do you manage concurrency and handle cancellation semantics in Python or Go when dealing with long-running tasks?
Approach
- Say what the runtime actually does before reasoning about the code.
- Name what is shared across threads and what owns each piece of state.
- Identify the window where an invariant is briefly untrue.
- 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?
Identify the heaviest tenants in a five-minute window under memory pressure
The edge service handles about 3,000 requests per second across roughly 50,000 tenants, peaking near 9,000. Expose the 50 heaviest tenants by request count over the trailing five minutes so limits can be tightened before one tenant's backfill starves the fleet. You may not retain five minutes of raw records. Give the exact solution and its memory, then the bounded-memory approximation with its error stated as a formula, and say which you would ship and at what tenant cardinality that choice changes.
Approach
- Do the exact version first, because it is affordable at this cardinality: a ring of 300 one-second counters per tenant, advanced lazily, is 1,200 bytes of counters per tenant and roughly 60 to 90 MB for 50,000 tenants with overhead. Carry a running total and subtract the bucket you overwrite so a window read is O(1) rather than 300 adds.
- Extract the top 50 with a size-k min-heap over the tenant sums: O(d log k) for d tenants, against O(d log d) to sort them all. Maintaining the heap continuously instead of on query requires a tenant-to-heap-index map, because incrementing a count already inside the heap means sifting from a known position, and without that map you rebuild the heap on every request.
- State the approximation precisely rather than gesturing at sketches. Misra-Gries with m counters retains every item whose true count exceeds N/(m+1), and each retained count underestimates the truth by at most N/(m+1). With m = 1,000 and N = 900,000 requests in the window the error is roughly 900 requests, which is fine for spotting a tenant sending 50,000 and useless for ranking two tenants 200 apart.
- Say what breaks when the window slides: Misra-Gries and Space-Saving are insert-only and cannot be decremented as records age out. The workable construction is one summary per sub-window, say ten seconds, with 30 summaries merged at query time, and the merged error is the sum of the per-summary errors, so the bound degrades linearly in the number of sub-windows.
Follow-up
- The heaviest tenant is heavy because of one export job rather than user traffic. Should the limiter treat those as the same tenant?
- Two tenants sit tied at the boundary of the top 50. Does your answer flap, and does the flapping matter?
Archive a resource graph without breaking live references or recursing
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?
Write the update path that detects a concurrent edit
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
- 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.
- 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.
- 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.
- 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?
Keep soft-deleted accounts from blocking re-registration
app_user holds user_id, tenant_id, email CITEXT, password_hash (NULL for SSO principals), email_verified_at, auth_version, status ('invited','active','suspended','deactivated'), created_at, updated_at, deleted_at. Two live accounts for one address inside a tenant must be impossible, but an address freed by a soft delete must be reusable, and the same tenant may delete and re-register it repeatedly. Write the uniqueness DDL for PostgreSQL 16, then the equivalent for MySQL 8 where partial indexes do not exist, and say what each permits once three deleted rows already hold that address.
Approach
- Start from what is actually unique: not (tenant_id, email), but (tenant_id, email) among live rows. PostgreSQL says that directly — CREATE UNIQUE INDEX app_user_live_email ON app_user (tenant_id, email) WHERE deleted_at IS NULL. A full constraint over the same two columns burns the address permanently the first time someone deletes an account.
- Keep case-insensitivity in the type or the index, never in the application: CITEXT as given, or UNIQUE (tenant_id, lower(email)) as an expression index where the extension is unavailable. A case-sensitive unique column is exactly how two accounts for one human appear.
- For MySQL 8 the predicate has to move inside the key: add a discriminator column that is a constant 0 while the row is live and is set to user_id on delete, with UNIQUE (tenant_id, email, deleted_marker). Live rows share the constant and still collide; deleted rows differ from each other and stop colliding.
- State the NULL variant and its dependency: leaving the marker NULL for deleted rows also works, because a unique index treats NULLs as distinct — true in MySQL, and true in PostgreSQL only under the default NULLS DISTINCT, which PostgreSQL 15 lets you reverse. Check the polarity against the three existing deleted rows: constant-on-live is what preserves the collision you want, and reversing it silently admits duplicate live accounts.
Follow-up
- A deleted account re-registers with the same address the next day. Do the old resource rows follow the new user_id, and how does the API keep the two principals apart?
- How do you honour an erasure request while resource_revision.actor_user_id still references this table?
Explain how you would design a service-oriented architecture to handle high-throughput, asynchronous data inge
Explain how you would design a service-oriented architecture to handle high-throughput, asynchronous data ingestion from physical sensors.
Approach
- Fix the scope first: who calls this, how often, and what they do when it fails.
- Name the read and write paths separately; they rarely have the same bottleneck.
- Choose a partition key and say what query it makes expensive.
- 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 are the trade-offs between using gRPC and REST APIs, particularly in a hybrid cloud and on-premise networ
What are the trade-offs between using gRPC and REST APIs, particularly in a hybrid cloud and on-premise networking environment?
Approach
- Say who the caller is and what they do when the call fails halfway.
- Define the identity of a request so a retry cannot double-apply it.
- Separate accepted, pending, failed and confirmed; they are different facts.
- Design the error taxonomy before the success shape; callers branch on it.
Follow-up
- What happens if the caller retries after a timeout?
- How does a client discover it is on an old version of this contract?
How do you implement idempotency and safe retry mechanisms in distributed task queues?
How do you implement idempotency and safe retry mechanisms in distributed task queues?
Approach
- Fix the scope first: who calls this, how often, and what they do when it fails.
- Name the read and write paths separately; they rarely have the same bottleneck.
- Choose a partition key and say what query it makes expensive.
- 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 design a robust driver interface for a laboratory instrument that communicates over Modbus or TC
How would you design a robust driver interface for a laboratory instrument that communicates over Modbus or TCP/IP?
Approach
- Fix the scope first: who calls this, how often, and what they do when it fails.
- Name the read and write paths separately; they rarely have the same bottleneck.
- Choose a partition key and say what query it makes expensive.
- 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 ensure zero-data-loss and low latency when streaming telemetry data from robotic
What strategies do you use to ensure zero-data-loss and low latency when streaming telemetry data from robotic actuators to an observability dashboard?
Approach
- Say who the caller is and what they do when the call fails halfway.
- Define the identity of a request so a retry cannot double-apply it.
- Separate accepted, pending, failed and confirmed; they are different facts.
- Design the error taxonomy before the success shape; callers branch on it.
Follow-up
- What happens if the caller retries after a timeout?
- How does a client discover it is on an old version of this contract?
Describe how you would implement a hardware cycle-testing script that safely handles unexpected physical failu
Describe how you would implement a hardware cycle-testing script that safely handles unexpected physical failures or emergency stops.
Approach
- Say who the caller is and what they do when the call fails halfway.
- Define the identity of a request so a retry cannot double-apply it.
- Separate accepted, pending, failed and confirmed; they are different facts.
- Design the error taxonomy before the success shape; callers branch on it.
Follow-up
- What happens if the caller retries after a timeout?
- How does a client discover it is on an old version of this contract?
How do you approach writing unit and integration tests when the target physical hardware is not available or i
How do you approach writing unit and integration tests when the target physical hardware is not available or is undergoing maintenance?
Approach
- Say who the caller is and what they do when the call fails halfway.
- Define the identity of a request so a retry cannot double-apply it.
- Separate accepted, pending, failed and confirmed; they are different facts.
- Design the error taxonomy before the success shape; callers branch on it.
Follow-up
- What happens if the caller retries after a timeout?
- How does a client discover it is on an old version of this contract?
Describe a scenario where you had to debug a performance bottleneck or memory leak in a production backend ser
Describe a scenario where you had to debug a performance bottleneck or memory leak in a production backend service.
Approach
- Establish what changed and when, before forming any theory.
- Pick a bisection that eliminates candidates whichever way it turns out.
- Check the instrumentation before believing the symptom.
- 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?
Built from the rounds and topics Radical Ai candidates report.
Prepare, practise & reflect
One practical outcome each day. Spend longer where you need it.
0 / 7 done01Map the Radical Ai loop
- Write out the reported sequence: Initial Screening Call, Technical Deep Dive, Practical Technical Assessment.
- 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.
02Work Backend engineering (production systems)
- Spend the session on Backend engineering (production systems), which Radical Ai candidates report being tested on.
- Write one worked example in Backend engineering (production systems) and time yourself on it.
Deliverable: One timed worked example in Backend engineering (production systems).
03Work Observability (logs, metrics, traces)
- Spend the session on Observability (logs, metrics, traces), which Radical Ai candidates report being tested on.
- Write one worked example in Observability (logs, metrics, traces) and time yourself on it.
Deliverable: One timed worked example in Observability (logs, metrics, traces).
04Work Reliability engineering
- Spend the session on Reliability engineering, which Radical Ai candidates report being tested on.
- Write one worked example in Reliability engineering and time yourself on it.
Deliverable: One timed worked example in Reliability engineering.
05Answer out loud: Technical & Core Engineering
- Answer aloud, timed: How do you manage concurrency and handle cancellation semantics in Python or Go when dealing with long-running tasks?
- Answer aloud, timed: Explain how you would design a service-oriented architecture to handle high-throughput, asynchronous data ingestion from physical sensors.
Deliverable: Spoken answers to 2 reported Technical & Core Engineering question(s), under time.
06Answer out loud: Systems & Robotics Integration
- Answer aloud, timed: How would you design a robust driver interface for a laboratory instrument that communicates over Modbus or TCP/IP?
- Answer aloud, timed: What strategies do you use to ensure zero-data-loss and low latency when streaming telemetry data from robotic actuators to an observability dashboard?
Deliverable: Spoken answers to 2 reported Systems & Robotics Integration question(s), under time.
07Answer out loud: Behavioral & Leadership
- Answer aloud, timed: Can you describe a significant technical challenge you faced on a past project and walk me through how you handled it?
- Answer aloud, timed: Tell me about a time you had to collaborate with non-software stakeholders, such as scientists or mechanical engineers, to deliver a complex feature.
Deliverable: Spoken answers to 2 reported Behavioral & Leadership question(s), under time.
Expand any day for tasks and deliverables. Your progress is saved on this device.
Behavioural rounds judge the decision you made and what it cost.
Can you describe a significant technical challenge you faced on a past project and walk me through how you han
Can you describe a significant technical challenge you faced on a past project and walk me through how you handled it?
Approach
- Pick a story where you made the decision, not one where you watched it.
- State the situation in two sentences and spend the rest on the reasoning.
- Give the blast radius: what could have broken, and what you measured.
- 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 time you had to collaborate with non-software stakeholders, such as scientists or mechanical e
Tell me about a time you had to collaborate with non-software stakeholders, such as scientists or mechanical engineers, to deliver a complex feature.
Approach
- Pick a story where you made the decision, not one where you watched it.
- State the situation in two sentences and spend the rest on the reasoning.
- Give the blast radius: what could have broken, and what you measured.
- 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 balance the need for shipping features quickly with the necessity of building long-term, reliable s
How do you balance the need for shipping features quickly with the necessity of building long-term, reliable software abstractions?
Approach
- Pick a story where you made the decision, not one where you watched it.
- State the situation in two sentences and spend the rest on the reasoning.
- Give the blast radius: what could have broken, and what you measured.
- 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 a production system failed under your watch. What was your immediate response, and
Describe a situation where a production system failed under your watch. What was your immediate response, and how did you prevent it from happening again?
Approach
- Pick a story where you made the decision, not one where you watched it.
- State the situation in two sentences and spend the rest on the reasoning.
- Give the blast radius: what could have broken, and what you measured.
- 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?
- 01
Can you describe a significant technical challenge you faced on a past project and walk me through how you handled it?
- 02
Tell me about a time you had to collaborate with non-software stakeholders, such as scientists or mechanical engineers, to deliver a complex feature.
- 03
How do you balance the need for shipping features quickly with the necessity of building long-term, reliable software abstractions?
- 04
Describe a situation where a production system failed under your watch. What was your immediate response, and how did you prevent it from happening again?
What is the typical interview difficulty at Radical Ai?
Candidates generally describe the technical interviews as highly practical and "easy to moderate" in terms of algorithmic complexity, but rigorous when it comes to systems thinking, reliability, and real-world edge cases. The focus is on how you build real systems rather than solving abstract leetcode puzzles.
Radical Ai Software Engineer candidate reports ↗What is the work arrangement and location policy?
These roles are primarily based in the New York City lab. Because the software interacts directly with physical robotic hardware and lab instruments, teams work in-person most of the time to facilitate rapid iteration, with hybrid arrangements evaluated on a case-by-case basis.
Radical Ai Software Engineer candidate reports ↗How can I best prepare for the conversation with the CEO or leadership?
Be ready to talk holistically about your past engineering experiences. Focus on the business and scientific impact of your work, why you chose specific frameworks, and how you managed project challenges. Show a genuine curiosity about generative materials science and automation.
Radical Ai Software Engineer candidate reports ↗What makes a candidate successful during the technical problem-solving stage?
Successful candidates do not just write code that works in the happy path. They proactively write unit tests, handle potential exceptions, explain concurrency trade-offs, and design their code with clear abstractions that are easy to maintain.
Radical Ai Software Engineer candidate reports ↗What topics does Radical Ai test in interviews?
Radical Ai interviews most often cover Backend engineering (production systems), Observability (logs, metrics, traces), Reliability engineering, Algorithms, and Programming languages (Python). The exact emphasis depends on the specific role you apply for.
Radical Ai Software Engineer candidate reports ↗Sources & methodology 3 sources ↗
No official company page is cited. Rounds and questions come from candidate reports and PracHub editorial material; each source shows the date it was read.
- 01Radical Ai Software Engineer candidate reports ↗
Company-reported rounds, questions and FAQ.
Candidate reports · Accessed 2026-09-22 - 02PracHub Software Engineer practice ↗
PracHub practice material, not company-reported.
PracHub page · Accessed 2026-09-22 - 03PracHub preparation framework ↗
PracHub preparation guidance.
PracHub page · Accessed 2026-09-22
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