ML Infrastructure Engineer
ML Infrastructure Engineers build and maintain the systems that allow data scientists and ML engineers to train, evaluate, and serve models at scale. Their daily English covers writing infrastructure architecture documents, presenting GPU utilization reports, discussing training pipeline bottlenecks, and explaining serving infrastructure choices to ML and product teams. This path builds the vocabulary for discussing model training, serving, and observability infrastructure.
Topics covered
- GPU & compute infrastructure
- Training pipeline engineering
- Model serving
- Feature stores
- ML platform reliability
- Distributed training
Vocabulary spotlight
4 terms every ML Infrastructure Engineer should know in English:
The proportion of a GPU's compute capacity that is actively being used — a key efficiency metric for training infrastructure, as idle GPU time is expensive
"We improved GPU utilization from 45% to 78% by overlapping data loading with forward passes using prefetching."
A centralized repository for machine learning features — enables teams to share, discover, compute, and serve features consistently across training and inference
"By routing all feature computation through the feature store, we eliminated training/serving skew for the recommendation model."
A system that tracks model versions, metadata, evaluation metrics, and deployment history — the source of truth for which model version is in production
"The model registry enforces a sign-off workflow before any model can be tagged for production deployment."
A class of model degradation where the features used during training differ from those computed at inference time, causing the model to underperform in production
"We traced the performance drop to a training/serving skew — the normalization logic differed between the training pipeline and the serving API."
📚 Vocabulary Reference
Key terms organised by category for ML Infrastructure Engineers:
Training Infrastructure
Serving Infrastructure
MLOps Platform
Reliability
Recommended exercises
Real-world scenarios you'll practise
- Writing a GPU infrastructure capacity proposal: justifying a cluster expansion with utilization data, model training projections, and cost-per-experiment analysis
- Presenting a training/serving skew incident postmortem: explaining root cause, impact, and the monitoring improvements that prevent recurrence
- Designing a feature store architecture: explaining the trade-offs between online and offline stores, point-in-time correctness, and backfill strategies
- Writing a model serving infrastructure runbook: documenting scaling policies, rollback procedures, and health check configurations for an inference fleet
Recommended reading
Frequently Asked Questions
What English skills do ML Infrastructure Engineers most need to improve?+
ML Infrastructure Engineers most commonly need to improve: technical vocabulary (the correct English terms for domain concepts), collocation accuracy (using the right verb for each action), written communication (bug reports, PR descriptions, technical docs), and spoken communication for standups, code reviews, and stakeholder meetings.
How long does the ML Infrastructure Engineer learning path take?+
The ML Infrastructure Engineer learning path contains 20–40 hours of material studied comprehensively. Most learners focus on the highest-priority modules first and return to the rest over time. Spending 30 minutes per day for 4–6 weeks produces noticeable improvement in workplace English.
What vocabulary should a ML Infrastructure Engineer prioritise first?+
Start with the vocabulary that appears most in your daily work — terms you read in documentation, use in commit messages, and hear in meetings. The ML Infrastructure Engineer path begins with the most frequent vocabulary clusters before moving to advanced communication patterns.
Are there interview exercises for ML Infrastructure Engineer roles?+
Yes. The ML Infrastructure Engineer path includes role-specific interview question modules with model answers and key phrases — the actual questions interviewers ask and the vocabulary needed to answer them fluently. There is also a dedicated Interview Practice hub for general interview skills.
Does this path include pronunciation help?+
Yes. The path links to pronunciation exercises for the technical terms most commonly mispronounced in this domain. The Pronunciation hub includes drills for acronyms, silent letters, word stress, and minimal pairs — all in IT context.
What are the most common English mistakes ML Infrastructure Engineers make?+
The most common mistakes: incorrect collocations (using the wrong verb with a technical noun), false friends from L1, tense errors when narrating past incidents or walkthroughs, and using overly formal or overly casual register in written communication.
How do I improve my English for code reviews?+
Learn the standard code review collocations: approve a PR, request changes, leave a nit, address feedback, block a merge, resolve a conversation. Use hedging language for suggestions: "This might be cleaner as…", "Have you considered…?". The Collocations section includes a dedicated Code Review set.
Can I use this path alongside my daily work?+
Yes — the path is designed for working professionals. Each exercise set takes 10–15 minutes. The most effective approach is to study a vocabulary module before a meeting or task where you'll use that vocabulary, then practise immediately after. Context-linked practice produces much faster retention.
Is the content free?+
Yes, completely free. No registration required, no payment, no time limit. All vocabulary modules, exercises, glossary entries, and learning path guides are open access.
How do I track my progress through this path?+
Progress is tracked in your browser's local storage — completed exercise sets are marked with a checkmark when you return. No account is needed. You can bookmark specific modules and use the exercises overview to see which sets you've completed.