Interviews/AI & Machine Learning/PyTorch Engineer

Hire PyTorch Engineers
who actually ship PyTorch code that uses the GPU

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 2CUDA · ROCmtorch.compileDDP · FSDPLive 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 PyTorch engineer reads nvidia-smi alongside the loss curve. The good ones reach for torch.compile when it earns its keep, write data pipelines that do not starve the GPU, and treat mixed precision as table stakes. EasyEnv hands the candidate a real PyTorch training run with a GPU at 30% utilisation.

Inside a live interview

What you see when they work.

Real PyTorch training, real GPU, 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@pytorch-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 PyTorch Engineer is hard.

PyTorch depth ranges from "ran a tutorial" to "wrote a custom CUDA kernel".

GPU utilisation is universally claimed and rarely measured.

Distributed training is where things go silently wrong.

Mixed precision and gradient accumulation taste varies.

The fit test

What we measure for a PyTorch Engineer.

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

GPU utilisation

A training loop with a 30% GPU. They find the bottleneck.

Data pipeline

A DataLoader starving the GPU. They tune num_workers and pin_memory.

Distributed training

A DDP loss diverging. They explain ranks and sync.

Mixed precision

A loss exploding with AMP. They wire GradScaler correctly.

torch.compile and FSDP

A model that benefits from compile. They prove the gain.

AI collaboration

Allow AI for code scaffolding and grade whether they ran the profiler after.

The stack we put in front of them.

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

PyTorch 2CUDAtorch.compileDDPFSDPDeepSpeedAcceleratePyTorch ProfilerTritonTorchScriptONNXwandbTensorBoardLightningPyTorch DataLoader

Sample challenges

What a PyTorch 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 training at 30% GPU

Scenario

A training loop where nvidia-smi shows 30% utilisation. The profiler is open.

What you learn

Whether they fix DataLoader or move work to GPU.

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 PyTorch that imports cleanly and starves the GPU. EasyEnv records prompts so you see whether the candidate looked at GPU utilisation 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 PyTorch 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 PyTorch Engineer, answered.

Yes. Enterprise workspaces include multi-GPU nodes.

Yes. The workspace ships with PyTorch 2.x and a representative model.

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/AI & Machine Learning/PyTorch Engineer

Hire PyTorch Engineers
who actually ship PyTorch code that uses the GPU

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 2CUDA · ROCmtorch.compileDDP · FSDPLive 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 PyTorch training, real GPU, 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@pytorch-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 PyTorch Engineer is hard.

PyTorch depth ranges from "ran a tutorial" to "wrote a custom CUDA kernel".

GPU utilisation is universally claimed and rarely measured.

Distributed training is where things go silently wrong.

Mixed precision and gradient accumulation taste varies.

The fit test

What we measure for a PyTorch Engineer.

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

GPU utilisation

A training loop with a 30% GPU. They find the bottleneck.

Data pipeline

A DataLoader starving the GPU. They tune num_workers and pin_memory.

Distributed training

A DDP loss diverging. They explain ranks and sync.

Mixed precision

A loss exploding with AMP. They wire GradScaler correctly.

torch.compile and FSDP

A model that benefits from compile. They prove the gain.

AI collaboration

Allow AI for code scaffolding and grade whether they ran the profiler after.

The stack we put in front of them.

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

PyTorch 2CUDAtorch.compileDDPFSDPDeepSpeedAcceleratePyTorch ProfilerTritonTorchScriptONNXwandbTensorBoardLightningPyTorch DataLoader

Sample challenges

What a PyTorch 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 training at 30% GPU

Scenario

A training loop where nvidia-smi shows 30% utilisation. The profiler is open.

What you learn

Whether they fix DataLoader or move work to GPU.

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 PyTorch that imports cleanly and starves the GPU. EasyEnv records prompts so you see whether the candidate looked at GPU utilisation 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 PyTorch 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 PyTorch Engineer, answered.

Yes. Enterprise workspaces include multi-GPU nodes.

Yes. The workspace ships with PyTorch 2.x and a representative model.

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 PyTorch Engineer | Real-GPU Interviews | EasyEnv | EasyEnv

Hire PyTorch Engineerswho actually ship PyTorch code that uses the GPU

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 PyTorch Engineer is hard.

What we measure for a PyTorch Engineer.

GPU utilisation

Data pipeline

Distributed training

Mixed precision

torch.compile and FSDP

AI collaboration

The stack we put in front of them.

What a PyTorch Engineer interview looks like.

The training at 30% GPU

Score the engineer, and how they work with AI.

Ready to hire a PyTorch Engineer who ships?

Hiring a PyTorch Engineer, answered.

Hire PyTorch Engineerswho actually ship PyTorch code that uses the GPU

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 PyTorch Engineer is hard.

What we measure for a PyTorch Engineer.

GPU utilisation

Data pipeline

Distributed training

Mixed precision

torch.compile and FSDP

AI collaboration

The stack we put in front of them.

What a PyTorch Engineer interview looks like.

The training at 30% GPU

Score the engineer, and how they work with AI.

Ready to hire a PyTorch Engineer who ships?

Hiring a PyTorch Engineer, answered.

Hire PyTorch Engineers
who actually ship PyTorch code that uses the GPU

Hire PyTorch Engineers
who actually ship PyTorch code that uses the GPU