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Meta•
5 years experience
Daniel Kim
ML Engineer → ML Platform Engineer
LandedMeta•ML Platform Engineer•$285,000 + $150,000 equity
74 → 93
Resume Score
3 → 5
Interviews
75 minutes
Time to Improve
Background
Industry
AI/ML Infrastructure
Experience
5 years
Location
Menlo Park, CA
The Challenge
Before Resume Scorer
Applications20
Interviews3
Interview Rate15%
Timeline4 months
Main Pain Points
- Resume showed model building, not ML infrastructure
- Missing MLOps and platform engineering skills
- No internal tooling and developer platform examples
- Positioned as ML engineer, not ML platform engineer
The Solution
1
Platform Engineering
Before
Built and deployed ML models for production
After
Architected ML platform: Kubeflow, MLflow, custom tooling serving 500+ data scientists
Impact
Showed platform engineering at enterprise scale
2
MLOps
Before
Model training and evaluation
After
Built end-to-end MLOps: automated training, monitoring, retraining pipelines, reduced model deployment time from weeks to hours
Impact
Demonstrated MLOps engineering and automation
3
Infrastructure
Before
Trained models using Python and scikit-learn
After
Built GPU clusters on Kubernetes, implemented feature stores, managed 10PB training data infrastructure
Impact
Showed ML infrastructure and data engineering depth
The Results
After Resume Scorer
Applications12
Interviews5
Interview Rate41.7%
Timeframe3 weeks
Interview Rate Increase
+178%
Increased from 15% to 41.7%
Landed Job
Meta•ML Platform Engineer•$285,000 + $150,000 equity
"I looked like an ML engineer who builds models. Resume Scorer helped me show I build ML platforms that serve other engineers—that's what got me hired at Meta."
— Daniel Kim, ML Platform Engineer at Meta
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