u/CoachSea4160

Hey everyone,

I recently got an invite for a HireVue (on-demand video interview) for a Graduate Verification Engineer role at Arm, and I’m trying to prepare.

Has anyone here gone through this specific HireVue process with Arm? If yes, could you share what kind of questions were asked, especially:

• Behavioral vs technical split
• Any coding or verification-related questions
• Difficulty level
• Time given per question

Also, any tips for doing well in HireVue interviews in general would really help.

Thanks in advance!

Hey everyone,

I recently got an invite for a HireVue (on-demand video interview) for a Graduate Verification Engineer role at Arm, and I’m trying to prepare.

Has anyone here gone through this specific HireVue process with Arm? If yes, could you share what kind of questions were asked, especially:

• Behavioral vs technical split
• Any coding or verification-related questions
• Difficulty level
• Time given per question

Also, any tips for doing well in HireVue interviews in general would really help.

Thanks in advance!

Hey everyone, I just got confirmation that the Round 2 interview for NVIDIA’s New Grad 2026 AI Performance & Efficiency Engineer role is actually a coding round.
If anyone has interviewed for this role, or similar NVIDIA systems/performance roles, could you please share what kind of coding questions to expect and what I should focus on preparing?
From the JD, I’m guessing topics could include:
Distributed systems

GPU/cluster performance

Python scripting

Algorithms/data structures

Parallel computing

Debugging/performance optimization

Would really appreciate any guidance on:
Difficulty level

LeetCode style vs practical systems coding

ML/distributed systems concepts asked

Any topics that came up frequently

Thanks everyone!

Hey everyone, I just got confirmation that the Round 2 interview for NVIDIA’s New Grad 2026 AI Performance & Efficiency Engineer role is actually a coding round.

If anyone has interviewed for this role, or similar NVIDIA systems/performance roles, could you please share what kind of coding questions to expect and what I should focus on preparing?

From the JD, I’m guessing topics could include:

• Distributed systems
• GPU/cluster performance
• Python scripting
• Algorithms/data structures
• Parallel computing
• Debugging/performance optimization

Would really appreciate any guidance on:

• Difficulty level
• LeetCode style vs practical systems coding
• ML/distributed systems concepts asked
• Any topics that came up frequently

Thanks everyone!

Hey everyone,

I just received an online assessment invite for the JIT, Staff Software Engineer, Master's Candidates role at Palo Alto Networks, USA.

Has anyone taken this OA recently? I would really appreciate any insights on what to expect, such as the format, number of questions, topics covered, difficulty level, or any general preparation tips.

Not looking for exact questions, just trying to understand what the assessment usually includes so I can prepare better.

Thanks in advance!

Hey everyone,

I recently got moved forward in the process for the AI Performance & Efficiency Engineer (New Grad 2026) role at NVIDIA (Santa Clara), and my recruiter mentioned that the second round will be a 45 min discussion on AI Efficiency and large scale systems.

I have not gotten much detail beyond that, so I wanted to ask:

• Has anyone here interviewed for this specific role?
• If not this exact role, what does a typical second round at NVIDIA look like for similar positions?
• How technical is this round, is it more systems focused, ML concepts, or performance optimization?
• Is it more discussion based or should I expect coding or deep technical grilling?
• Anything you wish you knew going into this round?

From what I have been told, it sounds like a discussion-heavy interview, but I am not sure how deep it goes into topics like distributed training, GPU performance, or infra-level debugging.

Would really appreciate any insights or tips!

Job Description (for context):

We are seeking an AI/ML Performance and Efficiency Engineer, GPU Clusters at NVIDIA to join our AI Efficiency efforts. This role focuses on improving efficiency across the stack for large scale ML workloads.

Key responsibilities include:

• Improving ML model efficiency for researchers
• Building tools to detect and fix performance bottlenecks
• Working on large scale GPU clusters across domains like LLMs, robotics, AV, video
• Monitoring and optimizing fleet wide utilization
• Collaborating across infra, hardware, and ML teams

Core requirements:

• Strong systems + ML understanding
• Experience with large scale infrastructure
• Performance debugging (Nsight Systems / Compute)
• Distributed training (NCCL)
• Programming in Python, Go, Bash
• Familiarity with cloud and parallel computing

Nice to have:

• CUDA, NVIDIA GPUs, MLPerf
• InfiniBand, RDMA
• Distributed storage (Lustre, GPFS)
• PyTorch / TensorFlow

Thanks in advance!

Hey everyone,

I recently got moved forward in the process for the AI Performance & Efficiency Engineer (New Grad 2026) role at NVIDIA (Santa Clara), and my recruiter mentioned that the second round will be a 45 min discussion on AI Efficiency and large scale systems.

I have not gotten much detail beyond that, so I wanted to ask:

• Has anyone here interviewed for this specific role?
• If not this exact role, what does a typical second round at NVIDIA look like for similar positions?
• How technical is this round, is it more systems focused, ML concepts, or performance optimization?
• Is it more discussion based or should I expect coding or deep technical grilling?
• Anything you wish you knew going into this round?

From what I have been told, it sounds like a discussion-heavy interview, but I am not sure how deep it goes into topics like distributed training, GPU performance, or infra-level debugging.

Would really appreciate any insights or tips!

Job Description (for context):

We are seeking an AI/ML Performance and Efficiency Engineer, GPU Clusters at NVIDIA to join our AI Efficiency efforts. This role focuses on improving efficiency across the stack for large scale ML workloads.

Key responsibilities include:

• Improving ML model efficiency for researchers
• Building tools to detect and fix performance bottlenecks
• Working on large scale GPU clusters across domains like LLMs, robotics, AV, video
• Monitoring and optimizing fleet wide utilization
• Collaborating across infra, hardware, and ML teams

Core requirements:

• Strong systems + ML understanding
• Experience with large scale infrastructure
• Performance debugging (Nsight Systems / Compute)
• Distributed training (NCCL)
• Programming in Python, Go, Bash
• Familiarity with cloud and parallel computing

Nice to have:

• CUDA, NVIDIA GPUs, MLPerf
• InfiniBand, RDMA
• Distributed storage (Lustre, GPFS)
• PyTorch / TensorFlow

Thanks in advance!

Hey everyone,

I recently got moved forward in the process for the AI Performance & Efficiency Engineer (New Grad 2026) role at NVIDIA (Santa Clara), and my recruiter mentioned that the second round will be a 45 min discussion on AI Efficiency and large scale systems.

I have not gotten much detail beyond that, so I wanted to ask:

• Has anyone here interviewed for this specific role?
• If not this exact role, what does a typical second round at NVIDIA look like for similar positions?
• How technical is this round, is it more systems focused, ML concepts, or performance optimization?
• Is it more discussion based or should I expect coding or deep technical grilling?
• Anything you wish you knew going into this round?

From what I have been told, it sounds like a discussion-heavy interview, but I am not sure how deep it goes into topics like distributed training, GPU performance, or infra-level debugging.

Would really appreciate any insights or tips!

Job Description (for context):

We are seeking an AI/ML Performance and Efficiency Engineer, GPU Clusters at NVIDIA to join our AI Efficiency efforts. This role focuses on improving efficiency across the stack for large scale ML workloads.

Key responsibilities include:

• Improving ML model efficiency for researchers
• Building tools to detect and fix performance bottlenecks
• Working on large scale GPU clusters across domains like LLMs, robotics, AV, video
• Monitoring and optimizing fleet wide utilization
• Collaborating across infra, hardware, and ML teams

Core requirements:

• Strong systems + ML understanding
• Experience with large scale infrastructure
• Performance debugging (Nsight Systems / Compute)
• Distributed training (NCCL)
• Programming in Python, Go, Bash
• Familiarity with cloud and parallel computing

Nice to have:

• CUDA, NVIDIA GPUs, MLPerf
• InfiniBand, RDMA
• Distributed storage (Lustre, GPFS)
• PyTorch / TensorFlow

Thanks in advance!

Hey all,

I received an OA link from Amazon (came from a no-reply email, just says Software Engineer, so I’m assuming it’s for SDE 2026, USA).

From the overview, it looks like:

• 2 coding questions (one traditional, one with AI assistant access in a repo environment)
• Work simulation + surveys

I’m especially curious about the AI-assisted coding round.

For those who’ve taken this recently:

• How does the AI-assisted environment actually work?
• What kind of prompts did you use that worked well?
• Did you treat it like pair programming or more like debugging help?
• Any limitations or things the AI struggled with?
• Is it more about prompting skill or still mostly coding ability?

Also general OA feedback would be great:

• Difficulty level compared to typical Amazon OAs
• Time pressure
• Any surprises

Would really appreciate any insights, especially around the AI part since that’s new.

Thanks!

Hey all,

I received an OA link from Amazon (came from a no-reply email, just says Software Engineer, so I’m assuming it’s for SDE 2026, USA).

From the overview, it looks like:

• 2 coding questions (one traditional, one with AI assistant access in a repo environment)
• Work simulation + surveys

I’m especially curious about the AI-assisted coding round.

For those who’ve taken this recently:

• How does the AI-assisted environment actually work?
• What kind of prompts did you use that worked well?
• Did you treat it like pair programming or more like debugging help?
• Any limitations or things the AI struggled with?
• Is it more about prompting skill or still mostly coding ability?

Also general OA feedback would be great:

• Difficulty level compared to typical Amazon OAs
• Time pressure
• Any surprises

Would really appreciate any insights, especially around the AI part since that’s new.

Thanks!

Hey all,

I received an OA link from Amazon (came from a no-reply email, just says Software Engineer, so I’m assuming it’s for SDE 2026, USA).

From the overview, it looks like:

• 2 coding questions (one traditional, one with AI assistant access in a repo environment)
• Work simulation + surveys

I’m especially curious about the AI-assisted coding round.

For those who’ve taken this recently:

• How does the AI-assisted environment actually work?
• What kind of prompts did you use that worked well?
• Did you treat it like pair programming or more like debugging help?
• Any limitations or things the AI struggled with?
• Is it more about prompting skill or still mostly coding ability?

Also general OA feedback would be great:

• Difficulty level compared to typical Amazon OAs
• Time pressure
• Any surprises

Would really appreciate any insights, especially around the AI part since that’s new.

Thanks!

Hey everyone,

I recently got invited to the OA for the Snowflake Software Engineer, SnowConvert AI position (location: USA).

It looks like a take-home assignment focused on migrating SQL Server stored procedures to Snowflake and working with schema + queries. There’s also mention of using any tech stack and optionally AI.

Has anyone here taken this OA before?

Would really appreciate if you could share:

• What the difficulty level was like
• How much time it realistically took
• What kind of stored procedures / complexity to expect
• How deep they expect validation/testing to go
• Whether using AI actually helped and how you used it

Any tips on what they’re evaluating most would also be super helpful.

Thanks in advance!