Machine Learning Engineer Expert

Software Engineering
Machine Learning Engineer Expert
Ethos
•
Remote
Education
Not stated
Type
Hourly
Pay Rate
$80/hr
Listed
40d ago
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How Ethos hiring works
Your application is a 10-minute call.
Ethos's AI interviewer asks about your career, then matches you to projects like Machine Learning Engineer. Keep your CV open and cover these:
4+ years as a software engineer, data engineer
▍
4+ years as a software engineer, data engineer
Direct experience evaluating model outputs or system readiness and documenting technical tradeoffs
▍
Direct experience evaluating model outputs or system readiness and documenting technical tradeoffs
Expert-level document, spreadsheet, and slide craftsmanship, with excellent written communication
▍
Expert-level document, spreadsheet, and slide craftsmanship, with excellent written communication
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About this role
From the Ethos listing
About This Opportunity
We're working with a leading foundational AI lab to find experienced machine learning engineers who can help train their latest language model on professional document, spreadsheet, and slide deck tasks.
We're looking for machine learning engineers with 4+ years building software, AI/ML systems, or data infrastructure in a professional engineering or data role to create, evaluate, and refine AI-generated documents, spreadsheets, and slide decks across core workflows: technical design documents and architecture decision records, data pipeline and ETL documentation, model evaluation and AI system readiness reports, API specification and integration guides, data quality and validation test plans, and developer onboarding guides and system architecture diagrams.

Compensation

$80/hour
Commitment:
Flexible, 5-20 hours per week (or more if desired)
Location:
Fully remote, work on your own schedule
Start date:
ASAP

Qualifications

4+ years as a software engineer, data engineer, or AI/ML practitioner writing technical design documents, data pipeline documentation, or API specifications
Direct experience evaluating model outputs or system readiness and documenting technical tradeoffs for engineering or cross-functional stakeholders
Expert-level document, spreadsheet, and slide craftsmanship, with excellent written communication and attention to detail
About Ethos
Ethos is a new expert network built by a McKinsey/SoftBank/DeepMind team and backed by world-leading investors like General Catalyst. We connect experts with investors and consultancies for paid expert calls, speaking engagements, and advisory opportunities.
How long hiring takes
Across the platforms we work with, people typically hear back about a hire within

about

30 days
of being referred.
Some are quicker, some take longer.
Within 2 weeks
~25%
Within 6 weeks
~60%
Within 3 months
~80%
Machine Learning Engineer Interview Prep
Practice questions for Machine Learning Engineer interviews in general. They are not taken from this listing, but they cover what interviewers in this field tend to ask.
How do you choose between a simpler interpretable model and a more complex model that performs marginally better?
The decision depends on how the model's output is used downstream. If a human needs to act on and justify individual predictions, like in lending or healthcare, interpretability often outweighs a small accuracy gain. If the model feeds an automated system where explainability isn't a hard requirement, I'll take the complexity if the performance gain is meaningful and validated, not just noise.
Explain how you would set up a proper train, validation, and test split for a time series problem.
Random splitting leaks future information into training for time series, so I split chronologically instead, training on the earliest period, validating on the next, and testing on the most recent. Any cross-validation also needs to respect time order, using something like rolling-origin validation, rather than standard k-fold, which would otherwise let the model see the future during validation.
What statistical test would you use to determine if an A/B test result is significant, and what assumptions does it rely on?
For a conversion-rate comparison, a two-proportion z-test or chi-squared test is standard, and it assumes independent observations and a large enough sample size for the normal approximation to hold. If the sample is small or the metric isn't binary, I'd switch to a more appropriate test, since applying the wrong test's assumptions is a common source of false confidence in A/B results.
How do you approach hyperparameter tuning efficiently when the model is expensive to train?
I use a coarse random search first to identify the promising region of the hyperparameter space, since grid search wastes compute on combinations unlikely to matter. From there, Bayesian optimization narrows in on the best configuration with far fewer expensive training runs than an exhaustive search would require, which matters a lot when each run is costly.
See all 10 Machine Learning Engineer interview questions →
Why this role
$80/hr for Machine Learning Engineer Expert work puts your Software Engineering expertise on the same footing as an AI lab's own research staff, without the overhead of running a consulting practice around it.
Skills and categories
Explore other opportunities in related specializations:
Software Engineering
No Degree Required
Software Engineering & Coding
Machine Learning
Engineering & Robotics
Coding
Expert
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Common questions
How and when does Ethos pay experts?
Within 30 days of invoice submission after a project closes. That net-30 schedule is standard for enterprise expert networks, but it's slower than the weekly payouts some AI training platforms offer, so plan your cash flow around it.
Is Ethos regular AI training work like rating chatbot responses?
No. Ethos is an expert network, not a task queue. AI labs, investment funds, and Fortune 500s pay to access your specific domain knowledge (deal analysis, clinical reasoning, legal logic, financial modeling) through three formats: multi-week consulting engagements, 30-60 minute advisory calls, and targeted research surveys.
What does task-based AI training work look like?
Practical, hands-on data work: recording short videos, categorizing images, rating text responses, or analyzing data. Tasks are designed to be short and distinct, typically 5 to 60 minutes each.
What does asynchronous AI training work mean in practice?
No set hours, no check-ins, no meetings. You log in when you want, pick up an available task, complete it, and submit; nobody is waiting on you in real time. That's different from remote employment, where you're expected online during business hours. The tradeoff: you're competing with others for available tasks, so an empty queue means there's simply nothing to do until more work is released.
What does Software Engineering work look like for a Machine Learning Engineer Expert?
Tasks here are scoped to Software Engineering, not generic labeling. As a Machine Learning Engineer Expert, expect to draw on real domain judgment (evaluating outputs, correcting errors, or providing expert reasoning specific to Software Engineering) rather than following a one-size-fits-all rubric. If you don't have hands-on Software Engineering background, this is likely not the right listing to start with.
What specific skills does this listing call for?
Coding and Expert are named directly in the listing. If you don't have hands-on experience with these, expect the screening process to test for them directly rather than accepting adjacent experience as a substitute.
How much does this specific role pay?
This listing is posted at $80/hr, an hourly rate. Pay can change between when we last checked the listing and when you apply, so confirm the current number on the platform's own application page before committing time.
What happens when I click Apply on this listing?
You'll be taken to Ethos's external site to complete your application there. This listing links through a referral, but the process is identical to applying directly; the link just routes you correctly. Create an account on their site and follow their onboarding steps.
Who built Ethos and is it legitimate?
Yes, it's legitimate and well-funded: Ethos raised $3.5M from General Catalyst and was co-founded by Daniel Mankowitz (Google DeepMind research scientist and lead author of AlphaDev) and James Lo (McKinsey, SoftBank Vision Fund). Over 17,000 experts have signed up, and it's actively working with 25+ PE firms, hedge funds, and consultancies. The referral link on this page is an affiliate link; it doesn't affect your pay or opportunities.