What machine learning engineers are paid, what to name in the advert, and who you are up against — from the adverts on Machine Learning Jobs, not a survey. Looking for the roles themselves? See Machine Learning Engineer jobs.
The range employers advertised for machine learning engineers on Machine Learning Jobs over the last 12 months, base salary only, across 99 priced adverts. Adverts that state a salary get more applications than those that hide it, and a figure at or above the midpoint is the single cheapest thing you can do for the response.
A fully remote role draws the whole country and can pay a little under the London figure; an anchored one competes locally and cannot.
Building from nothing is worth more to the right person than running what exists, and they will ask which it is.
A strong lead in the team is worth thousands on the offer; without one, expect to pay for that experience directly.
The skills machine learning engineers on Machine Learning Jobs are actually being asked for, by how often they appear in adverts for the role over the last 12 months. Candidates search by these words; an advert that uses them is the one they find.
Three live adverts for the role, as candidates see them. How rivals pitch it is worth a look before you write yours.
Owns deployment of ML models and engineering surrogates for industrial customers, building scalable training and inference pipelines and APIs across cloud and on-prem. Leads scoping and architecture, mentors engineers, and travels to customer sites 3-4 weeks per quarter. Hybrid role with 3 days in the Shoreditch office.
Python · PyTorch · Pandas +6
Owns deployment of ML models and engineering surrogates (deep learning on CAE/CFD/FEA data, time-series forecasting, anomaly detection, optimization) into customer production environments, building scalable training and inference pipelines and APIs on cloud and on-prem. Leads scoping and architecture, mentors engineers, and travels to customer sites roughly 3-4 weeks per quarter.
Python · PyTorch · Pandas +3
Lead deployment of ML models and engineering surrogates (deep learning on CAE/CFD/FEA data, time-series forecasting, anomaly detection, optimisation) into customer production environments. Own scoping and architecture for data/ML systems, mentor engineers, and travel 3-4 weeks per quarter to customer sites across North America, Europe, Asia and Oceania.
Python · PyTorch · Pandas +3
The job is keeping models working on your data, not building them once. What separates the roles, why your data maturity decides which one you can fill, and how to interview for the part that actually fails.
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