Not sure whether you are responding to any of my statements. Just in case it is. I view what you said as designing new algorithms. Guess you view them as implementing existing algorithms.
Actually all programming jobs are trivial. Believe we have different definitions of programming.
Caveat: I don’t have formal training in CS 
The average salary for Machine Learning job at Google is 145K?
https://www.glassdoor.com/Salary/Google-Machine-Learning-Engineer-Salaries-E9079_D_KO7,32.htm
Looks like the average is better at Apple at 158K.
https://www.indeed.com/salaries/machine-learning-engineer-Salaries-at-Apple
So much for hard to find machine learning engineer.
Most stats / ml majors (or any quantitative field) can take on ML jobs. This isn’t too different from quantitative trading shops would hire any random math / physics phds from harvard / mit / etc - they too make $500k easily.
Most fresh grads study materials with a huge assumption: the data you train & build model on is static. In real world, input data changes all the time, and the model you trained yesterday now becomes completely useless today. Sometimes the brittleness of the data comes not in time dimension, but follows a random process. So that adds another modeling layer on top of the first problem you were trying to solve. So techniques you employ is different in every deployment.
“New algorithms” @hanera refers to typically assume that there is a well defined problem - e.g. object recognition in images. Once a problem has been posed (by a well known researcher, or from slew of related applications), the ML community solves those problem thru a series of publications, and they become available in open source, so anyone can run those libraries.
But there is a huge long tail of problems that are seemingly unrelated, and therefore still needs lots of engineers with experience building similar systems. This reminds me - a long time ago, @manch asked if web search is solved, every other companies can just turn on the programs and “bam you go”.
That won’t work, because searching for snpachat videos vs maps vs web documents are all different problems.
I guess you are right because searching on Facebook still sucks. In 2019. So searching must be pretty damn hard.
Use Anaplan, abstract these data. j/k - feel like talking craps 
@notabene seize the opportunity to tell what he knows 
So plenty jobs
Some of what you said echoes what I wrote earlier. When it comes to doing creative and non-repititive work, AI/ML will not work. It is just like valuing a stock based on past data. Past data may only be used for predicting the future if trend is not broken. AI and ML are simply giving a structure to past data.
Some of the people I have spoken to tell me most of their time in ML is spent on cleaning the dirty or incomplete data. Building recipes and finding weights for their models is the least of their work.
Data wrangling? Highly paid data wranglers.
Looks like companies pay 123k in Austin. They pay 168K in Santa Clara. 45k (35%) difference.
https://www.indeed.com/salaries/machine-learning-engineer-Salaries,-Austin-TX
According to my scuttlebutt is 15% difference 
Just as the question I asked other day that if the college education is such a wonderful, whey do a lot of students cannot pay the cost of education back?
The same question. If Bay area is such a wonderful place to earn, why do most people cannot afford to buy their own home. And why do we keep hearing of demand for minimum wages , social services, and affordable homes. Why do rent controls and just eviction clauses resonate with many Bay Area people. Apparently, for many people Bay area is not as a good source of income as it is for some others.
I have no problem in accepting that some engineers at Google make more than $500 K per year, but there are a lot of Bay area resident who cannot make more than 50K per year. That ratio might be 1 to 10000 or even worse.
When we discuss the prosperity in Bay Area, do we become victim of the two evils that cloud rational thinking: Confirmation Bias and Motivated reasoning.
I found this survey of top paying job in South Bay. Take a look.
Rank Job Description Average Salary i Entry Level
Salary i # of Empl i
1 Computer and Information Systems Managers $188,190 $121,840 11,950
2 Nurse Anesthetists $187,700 $157,180 0
3 Marketing Managers $182,490 $111,940 5,630
4 Compensation and Benefits Managers $163,130 $97,120 160
5 Financial Managers $157,690 $90,710 6,580
6 Human Resources Managers $157,090 $87,700 1,840
7 Training and Development Managers $156,650 $90,830 440
8 Nurse Midwives $153,520 $87,640 70
9 Sales Managers $149,070 $54,570 6,860
10 Purchasing Managers $142,440 $85,440 940
https://www.usawage.com/high-pay/jobs-san_jose-sunnyvale-santa_clara-ca.php
Pay is high enough to qualify, but there are no savings to buy the home. This is very common issue when buying a home.
recently, I had 20+ offers when I sold my home,less than $1M sale price, hardly three are really having good amount of savings 20% or above. I decided a family who had appx 40% cash position.
More than 10+ friends who are looking to buy primary condo, town home are good in terms of pay, but their savings are poor enough to compete in bidding-war. They are unable to compete in SFH by poor savings.
Real estate investors are having exceptional savings.
Two years before when I sold another home, around 1.2M price, I had five offers 3 primary buyer, 2 investors. Those investors had appx full cash to pay, but gave proof of full cash and got additional loans from bank.
BA in above article refers to SF MSA exclude SB 
They ranked San Jose as the 13th most competitive market in the nation with SF at 12th.
Who fixed the price of Bay Area home?
The sellers who doesn’t need to sell.
We predict the overall market will be challenged by changing migration patterns as some buyers will search for more affordable housing markets, particularly by first time buyers, who are the hardest hit, moving out of state. In fact, according to the most recent Housing Market Study, nearly a third of those sellers who planned on repurchasing said that they will buy their next home in another state outside of California- the highest level since 2005. Older generations were more likely to buy outside of California.
Despite all the recent data indicating a possible change, @manch would extrapolate from past 50 years of data that BARE would continue to rocket in the future.
People who can’t afford houses here will not move the housing market one way or another, by definition. They may move markets in the middle of nowhere, like Austin.
People who don’t study history are doomed to repeat past mistakes. 