u/Beginning_Chain5583

I am three months into my first job as a junior DevOps engineer at a 100+ employee IoT startup, and I am planning to start applying for DevOps/MLOps roles at FAANG companies when I hit the six month mark. I know that is early, and I don't expect offers in the first round, but I would rather start building the habit of interviewing sooner than later. My plan is to aim for interviews during autumn that can help build the groundwork for proper offers during the spring round.

My background is non-traditional. I have a mixed degree, mostly CS but with a humanities component, was active in an ML club at university, and worked part time as a data engineer during my studies. I recently made it to the second round at BCG X for a non-junior AI engineer role, which gave me some confidence that I am not completely off base.

My current stack at work is mostly Kubernetes, Python and Go, and my day to day has also involved ML work that I can't go into detail about publicly. For prep outside of work I am doing Kaggle competitions to keep my ML fundamentals sharp, grinding leetcode in Go, and planning to self-study Kubeflow by reimplementing an image generation project I found. I have not used Kubeflow professionally yet.

My question is whether this prep mix is actually aligned with what FAANG MLOps interviews look for. I am worried I might be trying to cover too many bases by practicing in multiple ways at the same time. Any feedback on what to prioritize or what I am missing would be appreciated.

Hi reddit! I made this post on r/MLQuestions, but I am posting it here too for spread:)

This is a case I have been assigned at work and I'd love input from anyone who's tackled something similar.

I'm building a failure prediction model for ~33k chargers. The devices emit data at two very different rates depending on operational state: roughly 1 obs/hour when idle and 1 obs/20s when active with a different feature set in each mode. I want to try predicting failures within a 7 day horizon, but I am open for other suggestions.

The positive rate is around 1% at 30 days and 2% at 90 days with a max of 5% of devices ever failing. Strong per-device behavioral variance makes it hard to even define what "normal" looks like. Devices have different usage patterns and

I'm now thinking about whether the mode shift problem is better solved at the architecture level or the data level. One option I'm considering is two separate RNN encoders for each operational state feeding into a shared decoder. But I'm also open to windowing and sampling approaches. And beyond reweighting and loss skewing what has actually worked for you at sub-2% positive rates in time series?

How would you tackle an issue like this?

Hi reddit!

This is my first real professional ML project and I'd love input from anyone who's tackled something similar.

I'm building a failure prediction model for ~33k chargers. The devices emit data at two very different rates depending on operational state: roughly 1 obs/hour when idle and 1 obs/20s when active with a different feature set in each mode. I want to try predicting failures within a 7 day horizon, but I am open for other suggestions.

The positive rate is around 1% at 30 days and 2% at 90 days with a max of 5% of devices ever failing. Strong per-device behavioral variance makes it hard to even define what "normal" looks like. Devices have different usage patterns and

I'm now thinking about whether the mode shift problem is better solved at the architecture level or the data level. One option I'm considering is two separate RNN encoders for each operational state feeding into a shared decoder. But I'm also open to windowing and sampling approaches. And beyond reweighting and loss skewing what has actually worked for you at sub-2% positive rates in time series?

How would you tackle an issue like this?

Hi! My partner and I are considering relocating to Singapore long-term and would love some advice on what realistic expectations looks like.

I'm a 24M DevOps engineer with a focus on ML and AI integrations, soon 1 year of experience and a Masters in Databases. My partner is a 25F secondary school teacher with STEM specializations, also soon 1 yearsof experience and a master in science education. We both graduated NTNU this summer.

Our reasoning is pretty straightforward. I want to work in a stronger tech hub than what I feel Scandinavia currently has, and she wants to build a career as an international teacher. We considered the US but given the current political climate we'd rather plant roots somewhere that feels more stable and reliable long-term. Singapore ticks a lot of boxes for both of us.

We've done some reading on the Employment Pass and COMPASS system and understand that building a stronger profile before making the move matters, so we're thinking a few years out rather than immediately. That said, we're aware there's a lot we don't know yet. We're curious about what a realistic EP pathway looks like for profiles like ours, whether there's meaningful demand for foreign STEM teachers, and generally what people wish they had known before starting the process.

Any advice is appreciated!