Interviews/Data & AI/Machine Learning Engineer

Hire ML Engineers
who actually turn data into production behaviour

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.

PyTorch · JAXHugging FaceRay · DaskNotebooks + CLILive 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 ML engineer thinks in distributions before they think in epochs. They read the data, the loss curve and the latency budget as one picture. EasyEnv hands the candidate a notebook, a dataset and a model that is underperforming on a known subgroup.

Inside a live interview

What you see when they work.

Real GPU, real dataset, 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@ml-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 Machine Learning Engineer is hard.

ML breadth is huge. Research-leaning, infra-leaning and product-leaning ML engineers are different jobs.

Models look fine in aggregate. Subgroup problems only show with the right slice.

Notebook fluency does not equal engineering fluency. The interview has to test both.

Reproducibility is rare in ML. The candidate has to want it.

The fit test

What we measure for a Machine Learning Engineer.

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

Read the data first

A dataset with class imbalance and a leaky feature. They notice before they train.

Debug a model

A training run with a noisy loss. They reach for the right diagnostics.

Pipeline design

They sketch a re-training loop with feedback, evaluation and rollback.

Subgroup fairness

A model that scores well on average and badly on one cohort. They surface it.

Serving fluency

They wrap the model in a serving target with batching and timeouts.

AI collaboration

Allow Claude or Copilot and grade whether they treat the generated code as draft, not gospel.

The stack we put in front of them.

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

PyTorchJAXTensorFlowHugging Facescikit-learnpandasNumPyRayDaskJupyterMLflowTritonKServeCUDADocker

Sample challenges

What a Machine Learning Engineer 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 score that lies

Scenario

A model with 0.94 AUC offline and 0.71 in production. The candidate gets training data, code and the deploy diff.

What you learn

Whether they suspect leakage before they reach for hyperparameters.

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 is great at suggesting model architectures, indifferent to your data. EasyEnv records prompts so you see whether the candidate cross-checked the LLM suggestion against the dataset distribution before they wrote the training script.

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 Machine Learning Engineer 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 Machine Learning Engineer, answered.

Yes. Workspaces can be provisioned with NVIDIA GPUs for the duration of the session.

Yes. Notebook-first workflows are supported alongside CLI and serving flows.

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/Data & AI/Machine Learning Engineer

Hire ML Engineers
who actually turn data into production behaviour

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.

PyTorch · JAXHugging FaceRay · DaskNotebooks + CLILive 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 GPU, real dataset, 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@ml-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 Machine Learning Engineer is hard.

ML breadth is huge. Research-leaning, infra-leaning and product-leaning ML engineers are different jobs.

Models look fine in aggregate. Subgroup problems only show with the right slice.

Notebook fluency does not equal engineering fluency. The interview has to test both.

Reproducibility is rare in ML. The candidate has to want it.

The fit test

What we measure for a Machine Learning Engineer.

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

Read the data first

A dataset with class imbalance and a leaky feature. They notice before they train.

Debug a model

A training run with a noisy loss. They reach for the right diagnostics.

Pipeline design

They sketch a re-training loop with feedback, evaluation and rollback.

Subgroup fairness

A model that scores well on average and badly on one cohort. They surface it.

Serving fluency

They wrap the model in a serving target with batching and timeouts.

AI collaboration

Allow Claude or Copilot and grade whether they treat the generated code as draft, not gospel.

The stack we put in front of them.

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

PyTorchJAXTensorFlowHugging Facescikit-learnpandasNumPyRayDaskJupyterMLflowTritonKServeCUDADocker

Sample challenges

What a Machine Learning Engineer 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 score that lies

Scenario

A model with 0.94 AUC offline and 0.71 in production. The candidate gets training data, code and the deploy diff.

What you learn

Whether they suspect leakage before they reach for hyperparameters.

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.

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 Machine Learning Engineer 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 Machine Learning Engineer, answered.

Yes. Workspaces can be provisioned with NVIDIA GPUs for the duration of the session.

Yes. Notebook-first workflows are supported alongside CLI and serving flows.

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 Machine Learning Engineer | EasyEnv | EasyEnv

Hire ML Engineerswho actually turn data into production behaviour

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 Machine Learning Engineer is hard.

What we measure for a Machine Learning Engineer.

Read the data first

Debug a model

Pipeline design

Subgroup fairness

Serving fluency

AI collaboration

The stack we put in front of them.

What a Machine Learning Engineer interview looks like.

The score that lies

Score the engineer, and how they work with AI.

Ready to hire a Machine Learning Engineer who ships?

Hiring a Machine Learning Engineer, answered.

Hire ML Engineerswho actually turn data into production behaviour

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 Machine Learning Engineer is hard.

What we measure for a Machine Learning Engineer.

Read the data first

Debug a model

Pipeline design

Subgroup fairness

Serving fluency

AI collaboration

The stack we put in front of them.

What a Machine Learning Engineer interview looks like.

The score that lies

Score the engineer, and how they work with AI.

Ready to hire a Machine Learning Engineer who ships?

Hiring a Machine Learning Engineer, answered.

Hire ML Engineers
who actually turn data into production behaviour

Hire ML Engineers
who actually turn data into production behaviour