Software Engineering
Machine Learning Engineer Expert
Remote
Education
Hourly
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
ai
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 skilled 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.
Flexible, 5-20 hours per week (or more if desired)
Fully remote, work on your own schedule

ASAP

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
Ethos is a new expert network built by a McKinsey/SoftBank/DeepMind team and backed by world-leading investors like General Catalyst. 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.
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. 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.
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 →
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.
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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?
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. What does asynchronous AI training work mean in practice?
That's different from remote employment, where you're expected online during business hours. 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.
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.
This listing is posted at $80/hr, an hourly rate. You'll be taken to Ethos's external site to complete your application there. Create an account on their site and follow their onboarding steps.

📌 Machine Learning Engineer (Fully Remote) (Canada)
🏢 Ethos
📍 Canada