What does a Machine Learning Engineer actually do?

KNOWN ALIASES / DISGUISES:

MARKET AVERAGE

$175,000

* Often inflated by the 'AI' buzzword, but frequently on par with or lower than senior Software Engineers, especially outside of FAANG or dedicated research roles.

"A premium paid for the *potential* of intelligence, which rarely translates into tangible business value beyond a few impressive demos and a hefty cloud bill."

[DIAGNOSIS]Expensive to maintain, projects often fail to meet ROI expectations, and many 'AI' problems are solvable with simpler, cheaper software engineering. Easily replaced by off-the-shelf APIs or a skilled Software Engineer.

"We are seeking an experienced Machine Learning Engineer with experience in NLP and computer vision to join our dynamic team and play a pivotal role in the development and success of our Automotive Benchmarking Platform. We are at the forefront of innovation, leveraging natural language processing (NLP) and computer vision techniques to drive breakthroughs the Automotive industry."

OTIOSE TRANSLATION

You will spend months attempting to apply off-the-shelf NLP and computer vision models to poorly labeled automotive data, ultimately producing a 'benchmark' that is statistically insignificant and ignored by the actual product team.

"Machine learning engineers must rely on their knowledge of data modeling and evaluation to identify correlations and patterns and predict any properties of previously unobserved instances. Understanding standard machine learning algorithms is essential. Applying standard algorithms effectively within an appropriate model and creating learning procedures and parameters for automation are things you may do while on the job."

OTIOSE TRANSLATION

You will meticulously tune pre-existing scikit-learn models on small, unrepresentative datasets, generating 'predictions' that are either obvious or completely wrong, all while meticulously documenting your 'rigorous' evaluation metrics.

"Machine learning engineers create systems often responsible for tracking product analytics and making impactful business decisions."

OTIOSE TRANSLATION

You will build complex, unmaintainable data pipelines that feed into dashboards already managed by business intelligence, providing 'insights' that confirm existing biases or are too opaque to be actionable.

[10:00 - 11:00]

Cloud Resource Allocation Scrutiny

Obsessively checking GPU utilization dashboards, confirming models are still 'training' or 'inferring' something, anything, to justify the AWS bill.

[13:00 - 14:00]

Hyperparameter Tuning & Feature Engineering Theatre

Randomly adjusting learning rates or adding trivial features, hoping for a 0.1% metric improvement to document in the next sprint review.

[15:00 - 16:00]

Model Interpretability Presentation Prep

Crafting elaborate slide decks with SHAP values and LIME explanations for stakeholders who only care if the 'AI' can generate a profit, not how it thinks.

"Most companies don’t have specific positions as ML engineers, they use software engineers."

— r/cscareerquestions

"Its easier to teach how to use ML to a domain expert, than to teach domain expertise to an ML engineer."

— r/Salary

"My last ML project was supposed to revolutionize customer support. After six months and a huge cloud bill, we found out a simple rule-based system did 90% of the job with 1% of the maintenance."

— teamblind.com