Hire Machine Learning Engineers
Build fast with Machine Learning Engineers from LatAm. Proficient in model pipelines, feature engineering, and deployment with setup in 21 days.
Hire Remote Machine Learning Engineers
Sebastián excels in prompt engineering, blending creativity and precision seamlessly.
- NLP
- Python
- AI Ethics
- Data Labeling
- LLMs
Cristian is an AI expert creating smart, purposeful tools for user-centered systems.
- Machine Learning Models
- AI Optimization
- Data-Driven Decisions
- Cloud Integration
- Scalable Solutions
Martín is an AI specialist turning technical ideas into usable, impactful applications.
- AI Strategy
- Machine Learning
- Data Modeling
- Product Integration
- Problem Solving
Facundo is a dynamic AI researcher known for innovative solutions and insightful analysis.
- TensorFlow
- NLP
- Reinforcement Learning
- Computer Vision
- Deep Learning
Daniela is an AI practitioner delivering smart systems that solve tangible challenges.
- Machine Learning Models
- AI Experimentation
- Tool Integration
- Product Thinking
- System Optimization
"Over the course of 2024, we successfully hired 9 exceptional team members through Lupa, spanning mid-level to senior roles. The quality of talent has been outstanding, and we’ve been able to achieve payroll cost savings while bringing great professionals onto our team. We're very happy with the consultation and attention they've provided us."
“We needed to scale a new team quickly - with top talent. Lupa helped us build a great process, delivered great candidates quickly, and had impeccable service”
“With Lupa, we rebuilt our entire tech team in less than a month. We’re spending half as much on talent. Ten out of ten”
Machine Learning Engineers Skills
Model Development
Build supervised and unsupervised learning pipelines.
Feature Engineering
Create, clean, and select features for model input.
ML Ops Integration
Automate training, deployment, and monitoring steps.
Evaluation Metrics
Track model performance with precision and recall.
Data Pipeline Design
Develop scalable data flows for ML ingestion.
Cloud ML Deployment
Use AWS, GCP, or Azure for scalable model serving.
Machine Learning Engineers Soft Skills
Logical Thinking
Approach experiments with scientific structure.
Collaboration
Work with data, infra, and product teams on delivery.
Time Management
Balance research with production deliverables.
Communication
Convey trade-offs between speed, accuracy, and scope.
Accountability
Own model performance and ongoing improvements.
Adaptability
Shift approaches based on feedback or data shifts.
How to Hire Machine Learning Engineers with Lupa
Bring predictive systems to life with ML Engineers. Access talent through our Remote Recruiting Company, scale fast with Remote Staffing Solutions, or build long-term teams using our RPO services.
Together, we'll create a precise hiring plan, defining your ideal candidate profile, team needs, compensation and cultural fit.
Our tech-enabled search scans thousands of candidates across LatAm, both active and passive. We leverage advanced tools and regional expertise to build a comprehensive talent pool.
We carefully assess 30+ candidates with proven track records. Our rigorous evaluation ensures each professional brings relevant experience from industry-leading companies, aligned to your needs.
Receive a curated selection of 3-4 top candidates with comprehensive profiles. Each includes proven background, key achievements, and expectations—enabling informed hiring decisions.
Top candidates ready for your assessment. We handle interview logistics and feedback collection—ensuring smooth evaluation. Not fully convinced? We iterate until you find the perfect fit.
We manage contracting, onboarding, and payment to your team seamlessly. Our partnership extends beyond hiring—providing retention support and strategic guidance for the long-term growth of your LatAm team.
How to Write an Effective Job Post to Hire Machine Learning Engineers
Recommended Titles
- ML Engineer
- AI Model Developer
- Data Science Engineer
- ML Ops Engineer
- AI Systems Developer
- Predictive Modeling Engineer
Role Overview
- Tech Stack: Skilled in Scikit-learn, TensorFlow, Pandas, SQL, and Python.
- Project Scope: Build pipelines for data preprocessing, training, evaluation, and model deployment.
- Team Size: Work with data scientists and back-end engineers in teams of 5–8.
Role Requirements
- Years of Experience: 3+ years of ML model engineering and deployment.
- Core Skills: Model tuning, feature engineering, pipeline automation, and evaluation metrics.
- Must-Have Technologies: Scikit-learn, MLflow, TensorFlow, Airflow, Docker.
Role Benefits
- Salary Range: $95,000 – $150,000 depending on tooling and vertical experience.
- Remote Options: Fully remote with team alignment tools and async ops.
- Growth Opportunities: Be part of scalable machine learning systems from concept to production.
Do
- List core skills: model building, deployment, and monitoring
- Mention experience with end-to-end ML pipelines
- Include preferred tools like Scikit-learn, TensorFlow, MLflow
- Highlight performance tuning and model optimization
- Use practical, scalable ML terminology
Don't
- Don’t generalize ML without specifying production context
- Avoid skipping model evaluation and data pipeline tasks
- Don’t list tools without linking them to deliverables
- Refrain from using catch-all roles like “Data Scientist”
- Don’t omit collaboration with data and dev teams
Top Machine Learning Engineer Interview Questions
Smart questions to assess Machine Learning Engineer skills
How do you design a scalable ML pipeline?
Expect modular design, automation (CI/CD), and tools like MLflow, Airflow, or Kubeflow. Look for deployment considerations.
What’s your experience with feature stores?
Look for understanding of consistency, offline/online sync, and tools like Feast or custom solutions.
How do you ensure data quality in ML workflows?
Expect discussion on data validation, monitoring, drift detection, and logging systems.
What tools do you use for model versioning and tracking?
Strong answers include MLflow, DVC, Weights & Biases, and explain how tracking fits into reproducibility goals.
How do you work with cross-functional teams in ML projects?
They should demonstrate clear communication with data teams, product, and DevOps to align models with production use cases.
How do you debug data pipeline issues affecting model accuracy?
Look for validation checks, schema mismatches, feature drift tracking, and logging at preprocessing steps.
Describe a time you deployed a model that failed in production. What did you do?
Expect monitoring, rollback strategies, and incident postmortem with retraining cycles.
What’s your approach when feature importance doesn’t match expectations?
Expect feature engineering review, domain expert input, or explainability tooling like SHAP or LIME.
How do you ensure robustness when retraining models periodically?
Expect versioning, retraining policies, model validation suites, and regression checks.
How do you handle inconsistent performance across customer segments?
Expect segment-specific evaluation, fairness audits, and model adaptations per cohort.
Describe a time you collaborated across teams to ship a model to production.
Expect coordination with MLOps, product managers, and devs—plus ownership of handoff success.
Tell me about a time your deployment failed—what did you learn?
Look for ownership, alerting improvements, and resilience through iteration.
How do you handle tension between research and engineering constraints?
Expect pragmatism, backlog balancing, and milestone planning.
Describe how you respond when metrics stop improving.
Expect root cause analysis, feature engineering pivots, or label strategy reviews.
How do you manage technical debt in ML pipelines?
Look for scheduled cleanup, modular design, and team-wide knowledge sharing.
- Weak integration between model and infrastructure
- Ignores version control for datasets and models
- Lack of CI/CD understanding for ML pipelines
- Fails to monitor model drift or accuracy decay
- Resistant to using standardized tools or frameworks
Build elite teams in record time, full setup in 21 days or less.
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End-to-end remote talent solutions, from recruitment to payroll. Country-compliant throughout LatAm.
Recruiting
Our recruiting team delivers pre-vetted candidates within a week. Not the perfect match? We iterate until you're satisfied. You control hiring and contracts, while we provide guidance.
Staffing
Our recruiters deliver pre-vetted remote talent in a week. You select the perfect candidate, we manage onboarding, contracts, and ongoing payroll seamlessly.
RPO
Our RPO services deliver flexible talent solutions. From targeted support to full-cycle recruitment, we adapt and scale to meet your hiring goals while you focus on strategic growth.
