Interviews/Application Development/Quantitative Developer

Hire Quantitative Developers
who actually ship low-latency code that the desk trusts

With EasyEnv we build a fit test for the role. Use it to cut the noise at the top of the funnel, or to go deep on the candidates who matter.

You can even evaluate how this person works with AI. So you hire the engineer who can ship, not the one who looks good in a slide.

C++ · Rust · PythonKDB+ · ArrowBacktest pipelinesLow-latencyLive or take-home

Request a Demo Create a Free Account

Real Linux VM

Not a sandbox

systemd, kernel, full toolchain

Browser-based

Zero install for candidates

One link and they are in

Auto-graded

Pass / fail checks per challenge

Plus the full session replay

A great quant developer makes the desk faster without scaring risk. The good ones reach for arena allocators, design backtests that are honest about look-ahead, and write code that survives an audit. EasyEnv hands the candidate a real market data feed and a backtest with subtle look-ahead bias.

Inside a live interview

What you see when they work.

Real market data, real backtest, every keystroke recorded.

billing-api

AMS

Billing-api

src

billing.py

auth.py

models.py

tests

test_billing.pyM

test_auth.py

pyproject.toml

README.md

Pybilling.py

from decimal import Decimal

def calculate_total(items: list[dict]) -> Decimal:

# Sum line totals using Decimal for currency precision

total = Decimal("0")

for item in items:

total += Decimal(str(item["price"])) * item["qty"]

return total

candidate@quant-workspace:~

alice@dev:~/billing-api$

● mainPython 3.12

Claude · ready

Claude

You

Refactor calculate_total to use Decimal for currency math, and add a test.

Claude

Ask Claude...

Mike

Sam

How it works

Three steps. Zero infra to maintain.

Pick the fit test

Use a ready-made challenge or ask us to build one for your stack. Live, take-home, or both.

Send one link

The candidate clicks. A full Linux workspace boots in their browser. No installs, no VPN, no zoom-share.

Review at 2x

Replay the session, read the auto-graded checks, and decide. Most reviews take under five minutes.

The problem

Why hiring a Quantitative Developer is hard.

Quant depth runs from "wrote a backtest" to "wrote the matching engine".

Look-ahead bias is the most common ship-blocker.

Allocation discipline matters more than CV bullets.

KDB+ / q is a niche signal.

The fit test

What we measure for a Quantitative Developer.

Every area runs inside a real production-like workspace. The candidate works in a browser, the session records itself.

Look-ahead bias

A backtest using future data. They explain.

Latency profile

A hot loop with allocation. They profile and cut.

Market data pipelines

A feed handler dropping packets. They diagnose.

Numerical stability

A returns calc that underflows. They use Kahan or log-space.

KDB+ / q fluency

A query that should be one line. They write it.

AI collaboration

Allow AI for code and grade whether they ran the backtest after the LLM-suggested change.

The stack we put in front of them.

Real tools, pre-installed, accessible from the workspace terminal.

C++ 20RustPythonNumPyPolarsArrowKDB+ / qpandasasyncioC++ allocatorsEigenBLASBoostCatch2wandb

Sample challenges

What a Quantitative Developer interview looks like.

Pick from our library or ask us to build a challenge for your stack. Each one is a real workspace, not a snippet.

Challenge 01

The backtest that cheats

Scenario

A backtest using current-bar close to size positions for the same bar. The candidate has the loop.

What you learn

Whether they restructure to use prior data only and re-validate.

In the workspace

$ ls challenges/

workspace, runbook.md, README.md

candidate has:

· a real Linux box in the browser
· kubectl, docker, terraform, jq, yq
· cluster, repo and cloud creds pre-wired
· auto-checks running in the background

interviewer sees:

· every keystroke + every command
· terminal + screen recording
· auto-graded pass/fail per check

AI in the loop

Score the engineer, and how they work with AI.

AI writes backtest code that uses the very bar it is predicting. EasyEnv records prompts so you see whether the candidate ran walk-forward validation after the LLM-suggested change.

Allow or block AI per question, not per interview

Full prompt and response history replayed in the review

Score "verifies AI output" as a separate signal

Ready to hire a Quantitative Developer who ships?

Set up your first interview in under an hour. We will help you build a fit test for this role on your stack.

Request a Demo Create a Free Account

Role FAQ

Hiring a Quantitative Developer, answered.

Yes. The workspace can be seeded with a public dataset; enterprise plans federate to your tick store.

Yes. KDB+ and q are pre-installed with a seeded sym/quote/trade table.

No. The workspace runs in their browser. Terminal, editor, ports and dashboards are all served from the EasyEnv workspace, so candidate setup is zero.

Each challenge has automated checks (did the service come back, did the test pass, did the right log message appear) plus a recording your team can replay. Most reviews take under five minutes.

Yes. AI access is a per-interview setting. Block it for fundamentals, then allow it later in the loop to assess AI collaboration separately.

Interviews/Application Development/Quantitative Developer

Hire Quantitative Developers
who actually ship low-latency code that the desk trusts

With EasyEnv we build a fit test for the role. Use it to cut the noise at the top of the funnel, or to go deep on the candidates who matter.

You can even evaluate how this person works with AI. So you hire the engineer who can ship, not the one who looks good in a slide.

C++ · Rust · PythonKDB+ · ArrowBacktest pipelinesLow-latencyLive or take-home

Request a Demo Create a Free Account

Real Linux VM

Not a sandbox

systemd, kernel, full toolchain

Browser-based

Zero install for candidates

One link and they are in

Auto-graded

Pass / fail checks per challenge

Plus the full session replay

Inside a live interview

What you see when they work.

Real market data, real backtest, every keystroke recorded.

billing-api

AMS

Billing-api

src

billing.py

auth.py

models.py

tests

test_billing.pyM

test_auth.py

pyproject.toml

README.md

Pybilling.py

from decimal import Decimal

def calculate_total(items: list[dict]) -> Decimal:

# Sum line totals using Decimal for currency precision

total = Decimal("0")

for item in items:

total += Decimal(str(item["price"])) * item["qty"]

return total

candidate@quant-workspace:~

alice@dev:~/billing-api$

● mainPython 3.12

Claude · ready

Claude

You

Refactor calculate_total to use Decimal for currency math, and add a test.

Claude

Ask Claude...

Mike

Sam

How it works

Three steps. Zero infra to maintain.

Pick the fit test

Use a ready-made challenge or ask us to build one for your stack. Live, take-home, or both.

Send one link

The candidate clicks. A full Linux workspace boots in their browser. No installs, no VPN, no zoom-share.

Review at 2x

Replay the session, read the auto-graded checks, and decide. Most reviews take under five minutes.

The problem

Why hiring a Quantitative Developer is hard.

Quant depth runs from "wrote a backtest" to "wrote the matching engine".

Look-ahead bias is the most common ship-blocker.

Allocation discipline matters more than CV bullets.

KDB+ / q is a niche signal.

The fit test

What we measure for a Quantitative Developer.

Every area runs inside a real production-like workspace. The candidate works in a browser, the session records itself.

Look-ahead bias

A backtest using future data. They explain.

Latency profile

A hot loop with allocation. They profile and cut.

Market data pipelines

A feed handler dropping packets. They diagnose.

Numerical stability

A returns calc that underflows. They use Kahan or log-space.

KDB+ / q fluency

A query that should be one line. They write it.

AI collaboration

Allow AI for code and grade whether they ran the backtest after the LLM-suggested change.

The stack we put in front of them.

Real tools, pre-installed, accessible from the workspace terminal.

C++ 20RustPythonNumPyPolarsArrowKDB+ / qpandasasyncioC++ allocatorsEigenBLASBoostCatch2wandb

Sample challenges

What a Quantitative Developer interview looks like.

Pick from our library or ask us to build a challenge for your stack. Each one is a real workspace, not a snippet.

Challenge 01

The backtest that cheats

Scenario

A backtest using current-bar close to size positions for the same bar. The candidate has the loop.

What you learn

Whether they restructure to use prior data only and re-validate.

In the workspace

$ ls challenges/

workspace, runbook.md, README.md

candidate has:

· a real Linux box in the browser
· kubectl, docker, terraform, jq, yq
· cluster, repo and cloud creds pre-wired
· auto-checks running in the background

interviewer sees:

· every keystroke + every command
· terminal + screen recording
· auto-graded pass/fail per check

AI in the loop

Score the engineer, and how they work with AI.

AI writes backtest code that uses the very bar it is predicting. EasyEnv records prompts so you see whether the candidate ran walk-forward validation after the LLM-suggested change.

Allow or block AI per question, not per interview

Full prompt and response history replayed in the review

Score "verifies AI output" as a separate signal

Ready to hire a Quantitative Developer who ships?

Set up your first interview in under an hour. We will help you build a fit test for this role on your stack.

Request a Demo Create a Free Account

Role FAQ

Hiring a Quantitative Developer, answered.

Yes. The workspace can be seeded with a public dataset; enterprise plans federate to your tick store.

Yes. KDB+ and q are pre-installed with a seeded sym/quote/trade table.

No. The workspace runs in their browser. Terminal, editor, ports and dashboards are all served from the EasyEnv workspace, so candidate setup is zero.

Each challenge has automated checks (did the service come back, did the test pass, did the right log message appear) plus a recording your team can replay. Most reviews take under five minutes.

Yes. AI access is a per-interview setting. Block it for fundamentals, then allow it later in the loop to assess AI collaboration separately.

How to Hire a Quantitative Developer | C++ · KDB+ Interviews | EasyEnv | EasyEnv

Hire Quantitative Developerswho actually ship low-latency code that the desk trusts

What you see when they work.

Three steps. Zero infra to maintain.

Pick the fit test

Send one link

Review at 2x

Why hiring a Quantitative Developer is hard.

What we measure for a Quantitative Developer.

Look-ahead bias

Latency profile

Market data pipelines

Numerical stability

KDB+ / q fluency

AI collaboration

The stack we put in front of them.

What a Quantitative Developer interview looks like.

The backtest that cheats

Score the engineer, and how they work with AI.

Ready to hire a Quantitative Developer who ships?

Hiring a Quantitative Developer, answered.

Hire Quantitative Developerswho actually ship low-latency code that the desk trusts

What you see when they work.

Three steps. Zero infra to maintain.

Pick the fit test

Send one link

Review at 2x

Why hiring a Quantitative Developer is hard.

What we measure for a Quantitative Developer.

Look-ahead bias

Latency profile

Market data pipelines

Numerical stability

KDB+ / q fluency

AI collaboration

The stack we put in front of them.

What a Quantitative Developer interview looks like.

The backtest that cheats

Score the engineer, and how they work with AI.

Ready to hire a Quantitative Developer who ships?

Hiring a Quantitative Developer, answered.

Hire Quantitative Developers
who actually ship low-latency code that the desk trusts

Hire Quantitative Developers
who actually ship low-latency code that the desk trusts