Looks like pretty much all high tech company's slowing the hiring. That usually indicates that in few months it would not be surprised to see stating the blood bath in tech companies.
I'm looking for a job right now and honestly the market is red hot. Total comp is higher than I've ever seen and I'm even getting the opportunity to pick my interview style (eg: opting out of leetcode style interviews). I'm really enjoying the change.
I'm going to be looking soon, any tips that've been working for you so far? What sites were you looking on? Were you requesting interview type up front or just applying to ones that mention it, etc.?
- hired.com is good for startups; startups are often matching salary and benefits with top engineering firms and don't do geopay.
- (as usual) keyword optimize your resume and sync it with LinkedIn. A lot of managers, corporate recruiters, and third party recruiters are using LinkedIn search to find candidates. I use LinkedIn as a medium-quality queue.
There is a strange rash of companies putting their total comp (and base pay) under NDA. I generally let recruiters know that I think it's important to be up front about compensation from the beginning and starting the conversation from a place that makes sense with my experience.
Yes, I let recruiters know that if I have to do a leetcode interview then they go to the back of my queue (I can do them decently well; I'm just exhausted with them and don't see value). If they're project based or where they want to look at my GitHub then they go to the front. One thing that helps them meet in the middle is that I explain that I'm developing a short list of five companies and that I will knock all of those interviews out in one week and promise not to hedge offers against each other.
I'm a US citizen, honestly, my first thought was that it sounds shady and illegal. I'm not a lawyer nor do I have the time or energy to fight them on it, I just tossed it in the trash because there's a long queue ahead of them anyway.
Colorado was the first state to require salary ranges in job listings. More states have since legislated the same, and California might have a draft bill out, I believe.
Totally understand if not the right time but was keen to see if you'd be interested in discussing a fully remote Site Reliability Engineer position with one of the leading global cryptocurrency exchanges, who are driving the mainstream adoption of blockchain and crypto technologies and have grown from a small scale up to over 1500 people in the last four years?
They attract some awesome people globally and pride themselves on a no-ego team who are brilliant technically, but work together to achieve great things in a complex environment.
I'm under NDA on salaries so can't mention exact figures, but can say total comp can run into the hundreds of thousands on base, bonus and equity. The role is fully remote from anywhere in the US.
The SRE team tend to be 'dev heavy' and are using Rust and Golang currently however are open to candidates with experience in Python or Java too. You'd be introduced to Rust if needed (experience not required) and will have wider responsibility for ensuring reliability processes and the build and upkeep of container environments supporting massive volume live cryptocurrency trading platforms.
It would be great to tell you more, would this be of interest?
>"Yes, I let recruiters know that if I have to do a leetcode interview then they go to the back of my queue (I can do them decently well; I'm just exhausted with them and don't see value). If they're project based or where they want to look at my GitHub then they go to the front."
I'm curious what your views on interviews that require "take home projects." Companies have no skin in the game when they require you to use your nights and weekend hours to complete their project. As a result it's not uncommon to submit these and then be ghosted. I don't think I would agree to one now. The other issue is how many of these 4-5 hours can a person do in a month?
That is a perspective, and a valid one at that if you have kids or other obligations.
I dropped out of college, so a lot of the math this industry uses I had to learn on my own. I bought a lot of books and sweat over studying them, I had a leet code subscription too. The problem with this interview scheme is that:
1. It's timeboxed and our work is rarely, if ever, timeboxed to 30 minutes or less.
2. I've rarely, in my day to day work, implemented these algorithms by hand. I just need to know which to use and when to use it. When I do need to implement them I generally have a reference and I have a good reason for implementing it (like in Go with a lack of generics).
3. It's purely for the employers benefit. I gain no insight into the kind of work the team does, how it gets done, etc...
4. I have never in the history of my career had a more senior engineer watch me code in real time and criticize me.
Personally, I think companies need to offer a set of interviews. The typical leetcode for people who have gotten used to that, take home exercises for people that don't want to deal with leetcode, or even submitting example code that's relevant to the position. I'm also of the opinion that the time sunk into interviewing is the same between "problem solving exercises" and take homes. You spend months preparing on leetcode for one, but you invest a week at a time into take homes. I have been able to share take homes between companies, so I think these can be reasonably spreadload as well.
Any of these can be abused, imo, but none are more abused than leetcode or "problem solving exercises".
imho it's the blowback of covid + the few years before.
Companies hired based on temporary trends that could mostly be explained by covid. They scaled their team accordingly, now that people start to go out again they get less users/sales/engagement they have to scale back down.
I think it's the opposite thinking at Microsoft: they see here an opportunity to change their reputation as "the forgotten FAANG", not even in the acronym because it doesn't pay as well as the SV companies.
The stock is good sure, but I always figured that FAANG was more about their reputation as employers than it was about their reputation as an investment.
Just hired a man, put the idea inside his head
Pulled the trigger, now his career's dead.
MAAMA! Funding had just begun,
but now my crypto has gone and blown it all away.
We use self deployable configuration to allow Data Scientist to control the model's destiny.
Models are written in Python (mix of pytorch/NLP/tensorF).
The Models are serving about 35 predictions/second on avg.
The API server written in the Python. API server container feeds or write the requests in the distributed queue cluster.
The models picks up the samples from the queue in batching.
It allows to experiment the models (different flavor) based on the routing being set during the deployment time and which in turns being set in the cache.
We use AWS managed cache, queuing and container orchestration platform.
Next:
1)Current pipeline for the training and production is two separate pipeline which we want to combined, possibly use MLFlow, Airflow or KubeFlow.
Deployment to the production is done through Jenkins.
2)Active retraining and auto deployment to production.
3)Tie the version of model in production to model being trained. There is no way for us to tie back the version.
