This has been exactly my experience in the workplace as an autistic ML engineer. I’m still doing machine learning, building recommender systems, attending conferences, contributing to open source projects, publishing my work.

I’m just not getting paid, because I don’t fit in. ImageImageImageImage
When I mention that I’m not currently employed, people sometimes reach out to see if I’d be interested in a position with their company—which I appreciate but often don’t know how to respond to.
I might be, but the truth is that I have absolutely been through the wringer with work these past five years or so, and I have some lingering workplace-related trauma that makes me very hesitant to pursue the next thing.
I would love to be working again! I really like having problems that matter to work on and people to tackle them with.

What typically happens—assuming I can find a niche for myself at all—is that I end up in a position where I’m highly valued but pushed beyond my limitations.
And they aren’t things that are going to change with learning or practice or exposure.

Large groups, loud spaces, chaotic projects, indirect communication, and unrealistic optimism are never going to be my forte.
I work best with a small group of people who work together iteratively on small, shared projects in service of big goals for extended stretches.

I have experience creating those circumstances but it’s not always possible to do from a position of informal authority (at best.)
Each person has their own project? Projects have milestones every 6 months? Team members change every month? Team is bigger than 8 or so? Work tracking is very informal? Every project has to be approved by the big boss?

Count me out, because I will not be functional there.
I have the skills to be successful doing this kind of work, I can see the big picture and the small details and connect them to each other, I value and care about my coworkers, and I bring a lot of insight into ways of working you might not expect from an engineer.
But I will never feel comfortable at team social outings, and I don’t rely on social connections to form working relationships. (I do the reverse.)

Like many aspects of personality, people don’t know what to make of that, and have a tendency to read all kinds of things into it.
So, there you have it:

I’m one of the skilled but underemployed autistic people this article is discussing.

Would love to find a place where I do fit in and can do the kind of work I know I’m capable of (having done it before.)

It ain’t easy though.
P.S. I didn’t learn a lot of the stuff I know from work experience. I’ve been working on machine learning and recommender systems in my own time and between jobs for many years.

(Some of the upsides of being autistic are that I have deep interests and I’m _very_ persistent.)

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More from @karlhigley

9 Oct
“Why Are Deep Learning Models Not Consistently Winning Recommender Systems Competitions Yet?“

dl.acm.org/doi/abs/10.114…

My take is that we haven’t had the right model architectures. Here’s why I think that...
Going way back to the Netflix prize, multiplicative interactions have been a key component of successful modeling strategies. Matrix factorization did well on the Netflix data and became a classic approach to making recommendations.
Many further iterations on the key concept of factorizing matrices into low-rank approximations with vector embeddings per user/item/attribute have also been successful.
Read 17 tweets
9 Aug
It’s actually not that difficult to understand why people whose foundational worldview is “I feel safe and comfortable when I’m part of a homogeneous in-group that defines the terms of public life” would feel like they’re under siege by the (reasonable) demands from out-groups.
When people say that they feel like their freedoms are being taken away when freedoms are granted to others, this is what they’re referring to: the freedom to live in a social bubble where they don’t have to grapple with differences they find unsettling or threatening.
Here’s the thing: we all feel more comfortable in homogeneous groups that dictate the terms of interaction. Some of us grow beyond it to become capable of more engaging outside our bubbles, and some of us don’t. This mindset is in all of us to some extent and is never going away.
Read 4 tweets
27 Jul
This story and picture of paint-covered magazines really doesn't add up, so I did some digging and found some interesting stuff. 1/many
First of all, the paint goes entirely unremarked on and makes no sense. However, reporting from various sources indicates that federal agents claim that they are being hit with balloons full of paint. 🤔
Oh really, you don't say? Well, here's a Reuters photo from yesterday of a DHS police officer covered in red paint.

Source (slide 2): mobile.reuters.com/news/picture/t…
Read 54 tweets
3 Mar
I’m not a PM, but I have worked on the home screen for a major streaming service and wrestled with some of these trade-offs as algorithmic recommendations and ranking were introduced. I learned a few things along the way...🧵
Before the home screen is algorithmically ranked, it likely represents the product of many years of messy stakeholder interaction and negotiation, during which they all vied to have their thing at the top.
When new home screen elements are being tested, everyone wants to know how their new thing performs in the top slot. Performance at the top is in no way representative of the overall value of having the content somewhere on the page.
Read 23 tweets
6 Sep 19
Building recommenders without machine learning:
Where the privacy risks of recommender systems live:
The gap between published research and production:
Read 14 tweets
12 Jun 19
I used to think that making explainable recommendations required an interpretable model. I was wrong, and in order to understand why, you need to understand a few things about the structure of industrial recommender systems. 🧵
The structure I used to picture involved using interaction data and a model to generate vectors for users and items (matrix factorization, word embeddings, etc), and then making recs by finding items similar to each user vector with approximate nearest neighbor search.
The picture above is the sort of thing you'll often see in introductory texts about recommender systems, and while it's sufficient to generate a list of recs, it doesn't provide an easy way to explain why any particular item was selected.
Read 14 tweets

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