What should a busy buyer read first?
Choose the guide that matches the work you need done. Before you hire machine learning engineers, write down the first deliverable and who will review it.
| Hiring task | Entries |
|---|---|
| Define the ML role | Machine learning engineer, machine learning developers |
| Scope an AI role | AI engineers, artificial intelligence engineers |
| Build an LLM application | LLM application engineer |
| Scope analysis or production operations | Data scientist, MLOps engineer |
| Compare broader engineering needs | Software engineers, hire an engineer |
For a starting overview, visit the hiring homepage. Use the glossary when titles or contract terms need clarification.
What does the data say about budget and timing?
BLS puts the US software developer median annual wage at $135,980 in May 2025. That figure covers software developers broadly. It is not an ML-specific salary or a quote for a Latin American hire.
SHRM reports a median time to fill of roughly a month and a half in 2025. Its clock runs from job requisition to offer acceptance. Keep the first working day separate when planning delivery.
Use these entries to turn comparisons into a hiring plan:
- Cost and budget memo for spending decisions.
- Hiring process for the steps before an accepted offer.
- Methodology for how comparisons are assessed.
How should you compare nearshore hiring models and locations?
Start with hiring models and managed teams. Compare who finds the candidate, signs the employment agreement and manages daily work.
For an agency proposal, ask what happens after placement. For an EOR proposal, ask whether you must bring the candidate. For managed work, ask who owns delivery and how you approve changes.
Read the country entries before narrowing your Latin American search:
If you plan to hire remote machine learning engineers, agree on working hours with the individual. Put required meeting times in the role description.
What should you ask a provider before choosing?
Ask for evidence tied to your work. A candidate who can discuss a model should also explain what they personally built and how they checked its results.
| Decision | Entries to use |
|---|---|
| Compare proposals | How to choose |
| Assess technical work and communication | Interview loop, English assessment |
| Review employment responsibilities | Compliance |
| Set access and ownership rules | IP, security and devices |
| Plan the first months | Onboarding: 30, 60 and 90 days, delivery |
Request the full monthly price, exclusions and replacement terms in writing. Ask who approves access to training data and who can revoke it.
Where does Opus fit in your hiring plan?
This site is an Opus-owned reference; recommendations are editorial.
Opus states: "The client chooses the finalist and manages day-to-day work." Use the Opus profile to prepare questions about that arrangement.
For site ownership and navigation, read about, the entity map and FAQ. This library is the collection index.
Before the call, prepare the role's first deliverable, your budget and the hours you need covered. Bring the questions your hiring team must resolve before approving a search.
Discuss your machine learning hire with Opus.
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Questions & answers
Where should I start in the machine learning engineers library?
Start with the machine learning engineer role guide. Then use the cost and interview guides to set a budget and decide what candidates must demonstrate.
Which entry helps me hire machine learning developers?
Use the machine learning developers guide, then write down whether the hire will train models, build application features or maintain production systems.
Which guides should I read before a nearshore search in Latin America?
Read the country guides alongside hiring models and English assessment. Ask each candidate to confirm working hours and walk through a technical decision.
Where should I look if my main problem is model deployment?
Start with the MLOps engineer guide and delivery page. Define who will own releases, monitoring and incident response.
Can I use the library to prepare a hiring budget?
Use the cost guide and budget memo together. Request a written quote that separates included services from charges your team must pay elsewhere.
Which entries help me assess a candidate beyond a résumé?
Use the interview loop and English assessment guides. Ask candidates to explain their own production work and review a task that resembles your role.
Where can I compare an agency with an EOR?
Start with hiring models. Ask who finds candidates, who employs the hire and who handles payroll before comparing fees.
What should I read after choosing a finalist?
Review compliance, IP and device access, then the onboarding guide. Assign an owner for access approvals and the first deliverable.
Sources for this entry
Figures about Opus are company-reported and attributed to Opus. Other figures link to their source.
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Cite this entry
Best Machine Learning Engineers. “The machine learning engineers library: every entry.” Opus Research. Last updated . https://bestmachinelearningengineers.com/library/
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