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> However, after a few years, I realized that my technical skill growth was stalling, so I looked for an an internal transfer to another department, ideally in

> However, after a few years, I realized that my technical skill growth was stalling, so I looked for an an internal transfer to another department, ideally in a data analysis/software engineering role. > Having received no responses internally, I realized I would have to expand my search to outside of Apple. I literally just went through this exact same process, coming from Apple, and looking for Machine Learning/ Data scientist roles. Only people hiring internally wanted graduate degree credentials in the field. I hope you find what you are looking for! Some time off to work on side projects to skill up and get a portfolio was my plan A before I accepted an offer. It worries me as a shareholder though.. These are just anecdotes and I don't know if they actually represent a trend, but in my case I wish Apple was better about internal transfers. Edit: you are living the dream and I am definitely going to follow this blog to vicariously learn. Congrats on having the balls to just do it.

jjtheblunt 10y ago
Identical story for me : left Apple after >6 yrs in hardware, parallelizing optimizing compiler history, excellent reviews, impossible to find transfers internally to leverage grad experience, and internal rules were forcing stagnation, so I left, and don't regret it, though believe in Apple long term too.
- mattnewton 10y ago
  I really want Apple to succeed. My experience was they genuinely had their hearts in the right place and I want to be able to buy high-quality, privacy-conscious hardware and software, something of a rarity these days.
  - mercurysmessage 10y ago
    I'm not so certain that Apple is as privacy conscious as they are touting themselves to be. Apple still collects data from it's users.
    - mattnewton 10y ago
      Crash reports and usage telemetry is different from information you can use to sell ads.
      - mercurysmessage 10y ago
        You mean like Telemetry in Windows 10? https://docs.microsoft.com/en-us/windows/configuration/basic-level-windows-diagnostic-events-and-fields https://docs.microsoft.com/en-us/windows/configuration/basic... I didn't even know that Apple collected information until I saw an article detailing how to secure mac OS. Which, I think is worse.
        mattnewton 10y ago
        That perception means they risk to lose real market share if they get caught doing something shady. Which means they don't do shady things in my experience. I don't want to turn this into a tired apple-vs-windows thread, so if you can't see why Apple's lack of ads on their desktop is indicative of a fundamentally different view of their users, and a selling point for me, you are free to do so. I see a difference worth paying for (and you are free to call me a sucker I guess).
minimaxir 10y ago
OP here: For emphasis, I have absolutely nothing against Apple, and I wish I could have stayed. Although similar circumstances with internal transfers is certainly an unexpected coincidence :P
eksemplar 10y ago
The thing about machine learning is that, unlike with robotics RPA, new graduates in the field are so much better than anyone else. I'm in the public sector in a Scandinavian country mind you, so it's not like AI experts are lining up. We tried retraining, didn't work. Then we tried retraining some more, this time with outside consultants, didn't really work out either. So we hired a new graduate who had specialized in tensorflow during his degree, who (obviously?) blew our small progress away. Now this is anecdotal, but being the public sector we share experience openly with each other, and I'm hearing the exact same story from every other muniplicity in our network.
- mewwts 10y ago
  Hello from Oslo Would be interesting to hear about what you do machine learning for in the public sector. Could you share a bit?
  - eksemplar 10y ago
    It variates, and it's in the earl stages. One example we've done is sort through 500.000 personal cases, containing multiple documents, some documents containing up to 50 scanned pages to find cases missing a specific law required form. Once machine learning has OKed a case we used a software robot to archive it automatically. Because it was an experiment and also a job that had to be done. We did it simultaneously with as a competition of man vs machine. The man power was a team of expienced case workers working on it 37 hours a week. The machine was two new teams, one in RPA and one in machine learning. Both proceses ended up taking roughly 3 months, but the "tech" team spent a lot of the time learning how to use the technology and quite a bit training the algorithm. Once the tech team and algorithms were ready, the actual processing time took 5 hours in the Azure cloud. Now we have a process for finding specific documents, however, and I'm told it'll take 1-2 weeks to retrain it, meaning it'll be 1-2 weeks + 5 hours vs 3 months next time.
    - kk_cz 10y ago
      > One example we've done is sort through 500.000 personal cases, containing multiple documents, some documents containing up to 50 scanned pages to find cases missing a specific law required form. Forgive my ignorance, but how is this not a case for OCR + indexing? I assume that the form would have some specific texts that can be found once it's processed with OCR, what is there to machine learn?
1234r9h924 10y ago
Anecdotal counterpoint (from a throwaway account for obvious reasons)... This very much varies depending on the team you're on. I had no issues transferring from QA to engineering, but it's very much about who you know. From what I see my experience was the exception, and most people looking for internal transfers fared much like the OP. If you don't have a prior relationship with anyone on the teams you want to move to you're in for a rough ride. This is easier said than done because Apple is far more silo'd than other tech companies.
- threeseed 10y ago
  > This is easier said than done because Apple is far more silo'd than other tech companies. Apple was ridiculously siloed at least when I was there. There was such a culture of fear around leaks that teams shut themselves away from everyone else unless there was a reason to work together. This made the idea of internal transfers a remote possibility. The only way to get to know people was WWDC and meeting colleagues at bar.
- CoolGuySteve 10y ago
  At least under Bertrand, Apple prioritized hiring QA people with Comp Sci degrees. So it meant a lot of QA people wanted to move into engineering and the process would take years. My advice to anyone interviewing for a QA position at Apple is that if you're not really excited about QA or think you can move out later, don't bother with them. They say a lot of lies to get you in the door concerning career progression.
microtherion 10y ago
I transferred into my current job a few years ago, and this year, I interviewed two transfer candidates, one of them (not you, I'm pretty sure) a QA engineer who wanted to move into an engineering role, so I might have some thoughts on this situation from the other side: What advantages does the candidate expect from being an internal transfer, and what can we fairly offer them? To me, an internal transfer has a sizable advantage in that they are a known quantity in many aspects: You can ask them in much more detail about previous work at the company, you can talk to their co-workers, you have a clearer sense of the projects you were involved in, and in some cases you may already have personally worked with them. Where we DON'T cut the candidates any slack is the technical side of the job, and QA is usually different work from engineering. So the candidates still stand or fall based on the technical interview. The controversial aspect of Machine Learning jobs in particular, and the situation you described, is credentialism. In many areas of software engineering, credentials are indeed overrated, but Machine Learning IS a very technical field, requiring rather advanced math to truly understand it (rather than just blindly plug together pieces from an ML toolkit), and right now the field is undergoing revolutionary changes, so this is one area where a recent graduate degree really DOES convey a concrete advantage. It should not be an INSURMOUNTABLE advantage, but you'd have to be a very impressive non-academic candidate. What ML papers have you recently read? How would you plan to apply them in the job you're aspiring to? What contributions have you made to the field?
- mattnewton 10y ago
  I was trying to transfer as an engineer to another engineering position with the possibility of growth. These were not pure research positions, but positions with a fair amount of scaffolding making internal tools or productionizing a system alongside a team of existing experts, and so I thought my good performance being an engineer was going to let me move to a place where I could do more or less what I had already been doing, while learning and having room for growth. I had no illusions about being competitive for the actual machine learning _research_, having only taken online classes and done a side project. But there is a lot of schlep in managing data and then getting a model into production I knew I could do because I had done very very similar things successfully. I'm not saying the people they picked over me are worse though, I'm just sad they couldn't have picked us both :D
ryanSrich 10y ago
That makes sense though right? You'd want Apple to select the best candidate for the job. Ideally that someone has a PhD in CS with a focus on ML. I don't see the issue here.
- mattnewton 10y ago
  See my comment above, wish I could edit to clarify that I didn't think I was competitive for the _research_, just all the software engineering that comes with it.
- deleted 10y ago
  [deleted]

jjtheblunt 10y ago

Identical story for me : left Apple after >6 yrs in hardware, parallelizing optimizing compiler history, excellent reviews, impossible to find transfers internally to leverage grad experience, and internal rules were forcing stagnation, so I left, and don't regret it, though believe in Apple long term too.

mattnewton 10y ago
I really want Apple to succeed. My experience was they genuinely had their hearts in the right place and I want to be able to buy high-quality, privacy-conscious hardware and software, something of a rarity these days.
- mercurysmessage 10y ago
  I'm not so certain that Apple is as privacy conscious as they are touting themselves to be. Apple still collects data from it's users.
  - mattnewton 10y ago
    Crash reports and usage telemetry is different from information you can use to sell ads.
    - mercurysmessage 10y ago
      You mean like Telemetry in Windows 10? https://docs.microsoft.com/en-us/windows/configuration/basic-level-windows-diagnostic-events-and-fields https://docs.microsoft.com/en-us/windows/configuration/basic... I didn't even know that Apple collected information until I saw an article detailing how to secure mac OS. Which, I think is worse.
      - mattnewton 10y ago
        That perception means they risk to lose real market share if they get caught doing something shady. Which means they don't do shady things in my experience. I don't want to turn this into a tired apple-vs-windows thread, so if you can't see why Apple's lack of ads on their desktop is indicative of a fundamentally different view of their users, and a selling point for me, you are free to do so. I see a difference worth paying for (and you are free to call me a sucker I guess).

minimaxir 10y ago

OP here: For emphasis, I have absolutely nothing against Apple, and I wish I could have stayed. Although similar circumstances with internal transfers is certainly an unexpected coincidence :P

eksemplar 10y ago

The thing about machine learning is that, unlike with robotics RPA, new graduates in the field are so much better than anyone else. I'm in the public sector in a Scandinavian country mind you, so it's not like AI experts are lining up. We tried retraining, didn't work. Then we tried retraining some more, this time with outside consultants, didn't really work out either. So we hired a new graduate who had specialized in tensorflow during his degree, who (obviously?) blew our small progress away. Now this is anecdotal, but being the public sector we share experience openly with each other, and I'm hearing the exact same story from every other muniplicity in our network.

mewwts 10y ago
Hello from Oslo Would be interesting to hear about what you do machine learning for in the public sector. Could you share a bit?
- eksemplar 10y ago
  It variates, and it's in the earl stages. One example we've done is sort through 500.000 personal cases, containing multiple documents, some documents containing up to 50 scanned pages to find cases missing a specific law required form. Once machine learning has OKed a case we used a software robot to archive it automatically. Because it was an experiment and also a job that had to be done. We did it simultaneously with as a competition of man vs machine. The man power was a team of expienced case workers working on it 37 hours a week. The machine was two new teams, one in RPA and one in machine learning. Both proceses ended up taking roughly 3 months, but the "tech" team spent a lot of the time learning how to use the technology and quite a bit training the algorithm. Once the tech team and algorithms were ready, the actual processing time took 5 hours in the Azure cloud. Now we have a process for finding specific documents, however, and I'm told it'll take 1-2 weeks to retrain it, meaning it'll be 1-2 weeks + 5 hours vs 3 months next time.
  - kk_cz 10y ago
    > One example we've done is sort through 500.000 personal cases, containing multiple documents, some documents containing up to 50 scanned pages to find cases missing a specific law required form. Forgive my ignorance, but how is this not a case for OCR + indexing? I assume that the form would have some specific texts that can be found once it's processed with OCR, what is there to machine learn?

1234r9h924 10y ago

Anecdotal counterpoint (from a throwaway account for obvious reasons)... This very much varies depending on the team you're on. I had no issues transferring from QA to engineering, but it's very much about who you know. From what I see my experience was the exception, and most people looking for internal transfers fared much like the OP. If you don't have a prior relationship with anyone on the teams you want to move to you're in for a rough ride. This is easier said than done because Apple is far more silo'd than other tech companies.

threeseed 10y ago
> This is easier said than done because Apple is far more silo'd than other tech companies. Apple was ridiculously siloed at least when I was there. There was such a culture of fear around leaks that teams shut themselves away from everyone else unless there was a reason to work together. This made the idea of internal transfers a remote possibility. The only way to get to know people was WWDC and meeting colleagues at bar.
CoolGuySteve 10y ago
At least under Bertrand, Apple prioritized hiring QA people with Comp Sci degrees. So it meant a lot of QA people wanted to move into engineering and the process would take years. My advice to anyone interviewing for a QA position at Apple is that if you're not really excited about QA or think you can move out later, don't bother with them. They say a lot of lies to get you in the door concerning career progression.

microtherion 10y ago

I transferred into my current job a few years ago, and this year, I interviewed two transfer candidates, one of them (not you, I'm pretty sure) a QA engineer who wanted to move into an engineering role, so I might have some thoughts on this situation from the other side: What advantages does the candidate expect from being an internal transfer, and what can we fairly offer them? To me, an internal transfer has a sizable advantage in that they are a known quantity in many aspects: You can ask them in much more detail about previous work at the company, you can talk to their co-workers, you have a clearer sense of the projects you were involved in, and in some cases you may already have personally worked with them. Where we DON'T cut the candidates any slack is the technical side of the job, and QA is usually different work from engineering. So the candidates still stand or fall based on the technical interview. The controversial aspect of Machine Learning jobs in particular, and the situation you described, is credentialism. In many areas of software engineering, credentials are indeed overrated, but Machine Learning IS a very technical field, requiring rather advanced math to truly understand it (rather than just blindly plug together pieces from an ML toolkit), and right now the field is undergoing revolutionary changes, so this is one area where a recent graduate degree really DOES convey a concrete advantage. It should not be an INSURMOUNTABLE advantage, but you'd have to be a very impressive non-academic candidate. What ML papers have you recently read? How would you plan to apply them in the job you're aspiring to? What contributions have you made to the field?

mattnewton 10y ago
I was trying to transfer as an engineer to another engineering position with the possibility of growth. These were not pure research positions, but positions with a fair amount of scaffolding making internal tools or productionizing a system alongside a team of existing experts, and so I thought my good performance being an engineer was going to let me move to a place where I could do more or less what I had already been doing, while learning and having room for growth. I had no illusions about being competitive for the actual machine learning _research_, having only taken online classes and done a side project. But there is a lot of schlep in managing data and then getting a model into production I knew I could do because I had done very very similar things successfully. I'm not saying the people they picked over me are worse though, I'm just sad they couldn't have picked us both :D

ryanSrich 10y ago

That makes sense though right? You'd want Apple to select the best candidate for the job. Ideally that someone has a PhD in CS with a focus on ML. I don't see the issue here.

mattnewton 10y ago
See my comment above, wish I could edit to clarify that I didn't think I was competitive for the _research_, just all the software engineering that comes with it.
deleted 10y ago
[deleted]