Complete English Guide for ML Engineers

Why English Matters for ML Engineers

Machine learning engineering sits at one of the most internationally collaborative frontiers of software engineering. The primary research literature — arXiv preprints, NeurIPS and ICML conference papers, transformer architecture papers, scaling law research — is in English. The leading open-source frameworks (PyTorch, TensorFlow, Hugging Face Transformers, LangChain, vLLM) are documented in English. The community discussions on X/Twitter, Discord, Slack, and GitHub Issues that shape the field happen in English. For an ML engineer, English proficiency is not optional — it is a prerequisite for staying current.

Beyond keeping up with research, ML engineers operate in teams where precise communication is especially high-stakes. A misunderstood metric in a model review meeting could lead to a flawed model reaching production. Imprecise language in an A/B test design document could invalidate an experiment. Poor communication between research scientists and ML engineers — two groups with overlapping but different vocabularies and priorities — is a well-known source of friction at AI companies.

The ML engineer role is also unusually broad. Depending on the organisation, an ML engineer may be expected to train models (deep learning vocabulary), serve them (MLOps and infrastructure vocabulary), monitor them in production (observability vocabulary), communicate results to product managers (business vocabulary), discuss model architecture with researchers (academic vocabulary), and write about model behaviour for regulators or compliance teams (formal, precise English). Each context requires a different register and vocabulary set.

This guide covers the vocabulary and communication patterns for each of these contexts. It focuses specifically on the English used by practising ML engineers — not the English of academic ML papers alone, but the blend of precise technical language and clear business communication that defines effective ML engineering practice.

Section 1: Model Review Meeting Vocabulary

A model review meeting (also called a model evaluation meeting or model sign-off) is the gate through which a trained model must pass before it can be deployed to production. Being able to present your model's performance clearly, respond to questions about its limitations, and discuss deployment readiness requires a specific vocabulary.

Presenting Model Performance

Model performance is described using a hierarchy of metrics, and being precise about which metric you are optimising and why is the first test of clear communication in a model review: "The model achieves 94.2% accuracy on the held-out test set, with a precision of 0.91 and recall of 0.87 on the positive class. We chose to optimise for recall rather than precision because in this use case — fraud detection — the cost of a false negative (a fraudulent transaction we miss) significantly exceeds the cost of a false positive (flagging a legitimate transaction for review)." This precision-recall trade-off discussion, where you explicitly name the business cost of each type of error, is exactly what model review stakeholders want to hear.

When discussing multiple model candidates: "We trained three model variants: a logistic regression baseline, a gradient-boosted trees model, and a fine-tuned BERT-based classifier. The gradient-boosted model outperforms the baseline by 8.3 percentage points on F1 but underperforms BERT by 2.1 percentage points. Given the latency requirement of 50 milliseconds for this endpoint, and BERT's 200ms inference time on our current hardware, we're recommending the gradient-boosted model for production deployment." This kind of structured comparison — presenting options, their metrics, and the reasoning behind the recommendation — is the gold standard for model review communication.

Discussing Model Limitations

Proactively naming model limitations before reviewers ask about them demonstrates engineering maturity: "I want to flag two limitations before we discuss deployment. First, the training data covers transactions from the past 18 months. If fraud patterns shift significantly, we should expect model performance to degrade — we'll need to set up monitoring for distribution shift and retrain quarterly. Second, the model has lower recall on transactions from mobile devices — 0.79 versus 0.91 for desktop. We believe this is due to underrepresentation of mobile transactions in the training data. We're labelling additional mobile examples for the next training run."

Section 2: Experiment Discussion Vocabulary

ML experiments require precise language to communicate what was tested, how, and what was learned — whether the experiment succeeded or failed. A well-communicated failed experiment is often more valuable than a poorly communicated success, because it helps the team avoid repeating the same dead end.

Designing and Describing Experiments

When proposing an experiment: "I'm proposing an ablation study to understand the contribution of each feature group to the model's performance. We'll train four model variants: one with all features, one without the behavioural features, one without the demographic features, and one with only the transactional features. By comparing performance across all four variants, we can identify which feature groups are driving the model's predictive power and which can be removed to reduce complexity." Key vocabulary: ablation study (systematically removing components to understand their contribution), control (the baseline condition), treatment (the experimental condition), variant (one version in a multi-version experiment), hypothesis (the testable prediction), confounding variable (a factor that could explain the result independently of the treatment).

When sharing experiment results in a weekly sync: "The experiment showed a 3.2% improvement in click-through rate over the control. However, I want to caveat this result — the experiment ran for only 5 days, which is shorter than the 14-day minimum we typically require for seasonal variance to average out. The confidence interval is wide at 95% CI [+0.8%, +5.6%], so while the direction is promising, I'd recommend running for another 9 days before making a deployment decision."

Writing Experiment Documents

ML experiment documents (also called experiment write-ups, experiment notes, or model cards informally) follow a structured format: Background, Hypothesis, Method, Results, Analysis, Conclusion, and Next Steps. When discussing the Analysis section: "The model's performance improvement is most pronounced for users with fewer than 10 historical interactions — a 6.1% CTR improvement for cold-start users versus 1.2% for established users. This suggests the new features are especially valuable for bootstrapping recommendations for new users, which aligns with the hypothesis." Clear analysis language identifies patterns, offers explanations, and relates findings back to the original hypothesis.

Section 3: Paper Reading Vocabulary

Reading and discussing ML research papers is a daily activity for many ML engineers. Whether you are presenting a paper at a team reading group, discussing a technique with a colleague, or applying a published method to your own problem, you need the vocabulary to engage with the academic literature and translate it into engineering practice.

Understanding Paper Structure

An ML paper typically contains: Abstract, Introduction (problem, motivation, contributions), Related Work (how this work differs from existing approaches), Methodology (the proposed approach, often including a diagram of the architecture), Experiments (benchmark datasets, baseline comparisons, ablation studies), Results, Discussion (limitations, future work), and Conclusion. When presenting a paper: "The paper proposes a new attention mechanism they call 'Sparse Attention', which reduces the quadratic complexity of standard self-attention to O(n log n) by attending only to a structured sparse subset of positions. Their main contribution is demonstrating that this sparse pattern can be learned rather than fixed, which they call 'dynamic sparsity.'"

Common paper-reading vocabulary: propose (introduce a new method), demonstrate (show experimentally), outperform (achieve better results than), baseline (the comparison method), state-of-the-art (SOTA) (the current best-performing method), ablation (removing a component to test its contribution), replication (reproducing the results of a paper), preprint (a paper shared before peer review, typically on arXiv), concurrent work (another paper addressing the same problem published around the same time).

Discussing Applicability to Your Own Work

When deciding whether to apply a paper's technique: "This paper's approach is promising, but there are three reasons I think it won't transfer directly to our setting. First, they train on 10 billion tokens — we have 50 million. The technique may not be effective at our scale. Second, their evaluation is on English text only, but our production model needs to handle Ukrainian and Polish. Third, the implementation requires a custom CUDA kernel, which adds significant engineering effort. I think we should run a small-scale proof of concept before committing to a full implementation."

Section 4: Production ML Vocabulary (Serving, Monitoring, Drift)

Getting a model into production and keeping it performing well there requires a distinct vocabulary — one that blends ML terminology with software engineering and operations vocabulary. This is the domain of MLOps.

Model Serving Vocabulary

Serving a model means making it available to receive inference requests in production: "We're serving the recommendation model via a Python FastAPI service, deployed on Kubernetes. The service loads the model from the model registry on startup and caches it in memory. Each inference request passes the user features through the feature store, constructs the feature vector, calls the model's predict() method, and returns the top 10 recommendations." Key serving vocabulary: inference (running a model to produce a prediction), latency (time to produce a prediction), throughput (predictions per second), batch inference (processing many inputs together), online inference (real-time, low-latency prediction), model registry (a versioned store of trained models), feature store (a system that serves pre-computed features for model inference).

Discussing serving performance: "The p99 inference latency is currently 180 milliseconds, which exceeds our 100ms SLA. We've profiled the serving path and identified the bottleneck at the feature retrieval step — the feature store lookup takes 120ms on average. We're investigating two optimisations: pre-computing and caching the features at request time on the client side, and upgrading the feature store infrastructure to reduce latency to under 20ms."

Monitoring and Drift Detection

Production models degrade over time as the real world changes. Detecting and responding to this is a critical MLOps responsibility: "We monitor the model along three dimensions. First, we monitor operational metrics: latency, error rate, and throughput. Second, we monitor input data distribution: we run statistical tests comparing the current week's feature distributions to the training set baseline. If the feature distribution shifts significantly — which would indicate data drift — we alert the team. Third, we monitor model output distribution: we track the proportion of high-confidence predictions. A sudden drop in high-confidence predictions often precedes a decline in model accuracy." Key monitoring vocabulary: data drift (the input distribution changes over time), concept drift (the relationship between inputs and labels changes), model degradation (model performance declines in production), retraining trigger (the condition that initiates a new training run), shadow deployment (running a new model in parallel with the production model to compare outputs without affecting users).

Section 5: Research vs Engineering Communication

One of the most culturally specific communication challenges in ML roles is navigating the research–engineering interface. Research scientists and ML engineers have overlapping but different goals, timelines, and vocabulary, and misalignment between them is a common source of friction at AI companies.

Translating Research Goals into Engineering Requirements

Research scientists often describe goals in terms of model performance metrics and research novelty: "We want to explore whether a multi-task learning objective improves generalisation on low-resource languages." An ML engineer needs to translate this into concrete engineering requirements: "To support this experiment, I need: a training pipeline that supports multiple task objectives and datasets; infrastructure to run a hyperparameter sweep across at least 20 configurations; a storage and versioning system for checkpoint saving (the multi-task models will be 3x larger, so we need to ensure we have enough storage); and an evaluation harness that runs the held-out benchmarks automatically after each training run."

Common patterns in research-engineering communication: "What is the minimum viable version of this we can test quickly?" / "If this works at small scale, what would it cost to run at full scale?" / "What is the latency of this architecture at inference time — is it compatible with our production SLA?" / "How long will the training run take, and what compute resources do you need?" These engineering questions are not a challenge to the research direction — they are the practical bridge between an idea and a production system.

Setting Expectations Across Teams

Research timelines are inherently uncertain, but engineering timelines need to be planned. Communicating this difference clearly is important: "I want to flag a risk on the timeline. The research team has committed to a working model by the end of Q2, but the model architecture is still being finalised. Until we know the architecture, I can't fully design the serving infrastructure. I can make progress on the generic parts of the serving stack, but the final 2 weeks of work are blocked on the research output. If the research timeline slips by 2 weeks, the production launch date will also slip by 2 weeks."

Section 6: LLM and AI Vocabulary

The rapid growth of large language models (LLMs) and generative AI has introduced a large amount of new vocabulary that ML engineers need to be able to use precisely. Much of this vocabulary is new enough that usage is still being standardised — being aware of how terms are used in different communities is important.

Foundation Model and LLM Vocabulary

Key terms: foundation model (a large model pre-trained on broad data and adaptable to many tasks), large language model (LLM) (a foundation model specialised for text), pre-training (initial training on large-scale data, typically self-supervised), fine-tuning (continuing to train a pre-trained model on task-specific data), instruction tuning (fine-tuning on instruction-following datasets to make a model more helpful), RLHF (Reinforcement Learning from Human Feedback) (a technique for aligning model behaviour with human preferences), context window (the maximum number of tokens a model can process at once), token (the unit of text that an LLM processes — roughly 3/4 of a word on average for English text).

Usage in practice: "The model has a 128,000-token context window, which means we can fit a full code repository in a single prompt without truncation." / "We're fine-tuning Llama 3 on our customer support ticket dataset using LoRA (Low-Rank Adaptation) — it's much more parameter-efficient than full fine-tuning and achieves comparable performance at a fraction of the compute cost." / "The instruction-tuned version of the model follows instructions much more reliably than the base model — the base model generates continuations, while the instruction-tuned model responds to requests."

Prompt Engineering and RAG Vocabulary

Prompt engineering is the practice of designing inputs to get the best output from an LLM. Key vocabulary: prompt (the input given to an LLM), system prompt (instructions that define the model's behaviour and persona), few-shot prompting (including examples in the prompt to demonstrate the desired output format), chain-of-thought (CoT) (prompting the model to reason step by step), hallucination (when a model generates plausible-sounding but incorrect information), grounding (anchoring model outputs in factual external sources to reduce hallucination).

RAG (Retrieval-Augmented Generation) vocabulary: "We use a RAG architecture to ground the model's responses in our internal documentation. When a user asks a question, we first retrieve the top 5 most relevant document chunks from the vector database using cosine similarity, then pass them to the LLM as context along with the user's question. This significantly reduces hallucination compared to relying on the model's parametric knowledge alone." Key RAG terms: vector database (a database that stores and searches vector embeddings), embedding (a numerical representation of text that captures semantic meaning), retrieval (finding relevant documents), chunk (a segment of a document), cosine similarity (a measure of the angle between two vectors, used to find semantically similar content).

Section 7: Interview Vocabulary for ML Engineers

ML engineering interviews test a wide range of knowledge — from ML fundamentals to systems design to coding ability. The vocabulary you use to describe your experience and answer technical questions signals your level of expertise.

Discussing Training and Evaluation

Being precise about the training-evaluation workflow demonstrates seniority: "We split the dataset into training, validation, and test sets in a 70/15/15 ratio. I use the validation set during training to monitor for overfitting and to guide hyperparameter search. The test set is held out entirely until the final model evaluation — it is never used to make training decisions. This is a hard rule we enforce on the team, because if you tune on the test set, your reported performance is no longer an unbiased estimate of production performance."

Key evaluation vocabulary: overfitting (a model learns the training data too specifically and generalises poorly), underfitting (a model is too simple to capture the patterns in the data), bias-variance trade-off (the tension between model flexibility and generalisation), cross-validation (a technique for robust performance estimation using multiple data splits), learning curve (a plot of model performance vs. training data size, used to diagnose overfitting and data needs).

Discussing Production ML and MLOps

Senior ML engineer interviews almost always include a system design component for a production ML system. Key vocabulary: model registry, feature store, serving infrastructure, A/B testing, shadow mode deployment, online vs batch inference, retraining pipeline, model versioning. Example answer structure: "For this recommendation system, I'd design it as follows. Offline: we train the model on a weekly schedule using a Spark job that generates training data from the event logs. The model is versioned and stored in MLflow's model registry. Online: at inference time, the recommendation service calls the feature store for real-time user features, runs the model, and returns the top-k items. We'd use shadow deployments to evaluate new model versions before promoting them to production."

Most Useful Vocabulary & Phrases for ML Engineers

Recommended Learning Path for ML Engineers

Stage 1: Foundation — Core ML Vocabulary

1. 1

   Machine Learning Vocabulary

   Build the core vocabulary of machine learning: training, evaluation, inference, model types, feature engineering, and the standard English of ML engineering discussions.

2. 2

   AI/ML Collocations

   Practise the verb-noun collocations of ML engineering: collect data, label examples, train the model, evaluate performance, fine-tune, serve predictions, monitor drift, retrain.

3. 3

   AI Engineering Vocabulary

   The vocabulary of production AI systems: model registries, feature stores, serving infrastructure, LLMs, RAG, prompt engineering, and evaluation frameworks.

Stage 2: Intermediate — Experiments and Production

1. 4

   Deployment Collocations

   Deploy, release, roll out, shadow-deploy, canary — the vocabulary of shipping ML models to production safely and incrementally.

2. 5

   Observability Collocations

   Monitor, instrument, trace, alert, silence — the observability vocabulary applied to ML models in production: drift detection, performance monitoring, alerting.

3. 6

   Technical Writing exercises

   Write clear experiment documents, model cards, and technical design documents — the writing skills that distinguish senior ML engineers.

Stage 3: Advanced — Communication and Interviews

1. 7

   Presentations Language

   Present model review results, experiment findings, and production ML system designs to technical and non-technical audiences using clear, structured English.

2. 8

   Stakeholder Management Language

   Communicate model limitations, deployment risks, and retraining needs to product managers, executives, and non-technical stakeholders.

3. 9

   Technical Interview Language

   Articulate ML system design decisions, explain the bias-variance trade-off, discuss fine-tuning and RAG architectures — the vocabulary of senior ML engineer interviews.