What does an NLP Engineer do?
NLP Engineers design, train, evaluate, and ship language-based models and systems. You might fine-tune transformers, build retrieval-augmented generation (RAG) pipelines, deploy text classification or NER services, and monitor performance and safety in production.
- Day-to-day: exploring datasets, cleaning and labeling text, training/fine-tuning models, running evaluations, optimizing latency/cost, and collaborating with product and infra.
- Typical deliverables: trained model checkpoints, inference services/APIs, evaluation reports and dashboards, data pipelines, safety guardrails, and documentation/playbooks.
Example week in the role
- Mon: Review error analysis and plan next experiments for recall on rare intents.
- Tue: Fine-tune a token classification head for NER; run ablations on learning rate and sequence length.
- Wed: Add BM25 + dense retrieval to RAG; curate high-quality context chunks.
- Thu: Deploy new inference container with dynamic batching; create monitoring alerts for p95 and toxicity rate.
- Fri: Postmortem on a spike in false positives; update labeling guidelines and safety filters.
Who this is for
- You enjoy working with messy language data and iterating on experiments.
- You are comfortable with Python and want to build systems that run in production.
- You like measuring quality with clear metrics and improving results methodically.
Prerequisites
- Python basics (functions, classes, virtual environments) and comfort with Jupyter/Colab.
- Linear algebra and probability at a practical level (vectors, dot products, distributions).
- Git and basic terminal skills; ability to read docs and troubleshoot.
Mini task: check your baseline
- Load a small IMDB dataset, clean the text (lowercase, strip punctuation), and train a logistic regression with TF-IDF. Report accuracy, precision, recall, and F1.
Hiring expectations by level
- Junior: Implements models from templates, runs evaluations, improves labeling and data quality, writes clear experiment logs. Needs guidance on system design and trade-offs.
- Mid-level: Owns features end-to-end, selects appropriate models (classical vs transformer vs RAG), sets evaluation strategy, collaborates on deployment and monitoring.
- Senior: Leads problem framing, defines metrics and safety standards, optimizes cost/latency at scale, mentors others, and drives roadmap across teams.
Salary ranges
Varies by country/company; treat as rough ranges.
- Junior: ~$80k–$130k
- Mid-level: ~$120k–$180k
- Senior/Staff: ~$170k–$280k+
Where you can work
- Industries: SaaS, healthcare, fintech, e-commerce, legal tech, customer support, education, security, and research labs.
- Teams: ML Platform, Search/Retrieval, Applied Research, Product ML, Safety/Trust, Data Engineering.
Skill map for NLP Engineer
- NLP Foundations: tokens, embeddings, language modeling, classic vs neural approaches.
- Text Data Collection and Labeling: sourcing, annotation guidelines, inter-annotator agreement.
- Text Preprocessing and Normalization: tokenization strategies, handling Unicode, lemmatization.
- Feature Engineering for Classical NLP: n-grams, TF-IDF, hashing tricks, linear models.
- Transformer Models and Fine Tuning: encoder/decoder families, adapters, LoRA, hyperparams.
- Embeddings and Retrieval: vector stores, ANN indexes, hybrid search, chunking.
- LLM Applications and RAG: prompt design, context windows, grounding, citation.
- NLP Evaluation and Error Analysis: precision/recall/F1, BLEU/ROUGE, qualitative slices.
- Training and Optimization: regularization, curriculum, mixed precision, batching.
- Model Serving for NLP: GPU/CPU trade-offs, token streaming, batching, caching.
- MLOps for NLP Systems: CI/CD for models, data/versioning, drift monitoring, A/B tests.
- Safety and Compliance for NLP: PII handling, toxicity, jailbreak mitigation, auditability.
Learning path
Step 1: NLP Foundations, Text Data Collection and Labeling, Text Preprocessing and Normalization
Step 2: Feature Engineering for Classical NLP → build a strong baseline
Step 3: Transformer Models and Fine Tuning + NLP Evaluation and Error Analysis
Step 4: Embeddings and Retrieval + LLM Applications and RAG
Step 5: Training and Optimization + Model Serving for NLP
Step 6: MLOps for NLP Systems + Safety and Compliance for NLP
Mini task: plan your first month
- Week 1: Foundations + Preprocessing; ship a TF-IDF baseline.
- Week 2: Label 200–500 examples; improve baseline with better labeling.
- Week 3: Fine-tune a small transformer; compare to baseline with F1.
- Week 4: Add retrieval for a simple Q&A; log latency and cost per request.
Practical portfolio projects
1) Support ticket intent classifier (baseline → transformer)
Outcome: A service that tags incoming tickets with intents.
- Data: 1k–5k labeled tickets; clear label guidelines.
- Baseline: TF-IDF + logistic regression; report F1 per class.
- Upgrade: Fine-tune a small transformer; compare to baseline.
- Deliverables: API endpoint, model card, confusion matrix, error buckets.
2) Document Q&A with RAG
Outcome: Users ask questions about documents and get grounded answers with citations.
- Retrieval: Hybrid (BM25 + dense) with chunking and metadata.
- Generation: Small chat model; include source snippets in the answer.
- Evaluation: Groundedness score, answer helpfulness, p95 latency.
- Deliverables: Demo, eval set, logs for failures.
3) Named Entity Recognition for contracts
Outcome: Extract parties, dates, and amounts from legal text.
- Annotation: 500–1,000 sentences; measure inter-annotator agreement.
- Model: Token classification head (e.g., BERT-style); handle long sequences.
- Evaluation: Entity-level precision/recall/F1, per-entity confusion.
- Deliverables: Labeling guide, training code, error analysis report.
4) Toxicity and PII safety filter
Outcome: Moderation pipeline for user-generated text.
- Rules + model hybrid approach; redact PII before storage.
- Metrics: false positive/negative rates on curated test sets.
- Deliverables: Policy doc, tests, safe defaults, audit logs.
5) Real-time summarization with streaming output
Outcome: Summarize long transcripts into short notes.
- Chunk + retrieve relevant segments; incremental generation.
- Metrics: ROUGE, summary length control, latency budget.
- Deliverables: Service with streaming responses, monitoring dashboard.
Interview preparation checklist
- Foundations: explain tokenization, embeddings, precision/recall/F1; compute F1 from given numbers.
- Modeling: when to use classical ML vs transformers vs RAG; pick loss functions for classification/sequence labeling.
- Systems: design an inference service with dynamic batching, caching, and timeouts; reason about p95 latency.
- Data: write annotation guidelines; handle label noise; measure agreement (Cohen's kappa).
- Evaluation: set up slice-based evaluation; track drift; design A/B tests and rollout strategy.
- Safety: PII handling, prompt injection defenses, red teaming, and policy-driven filters.
- Behavioral: STAR stories on failures, trade-offs, and cross-team collaboration.
Mini task: whiteboard exercise
Design a system to answer product FAQs from docs. Include ingestion, chunking, indexing, retrieval, generation, evaluation, and safety.
Common mistakes (and how to avoid them)
- Chasing SOTA without a baseline: always start with a simple TF-IDF or small transformer baseline and clear metrics.
- Ignoring data quality: invest in labeling guidelines and audits; small high-quality datasets often beat huge noisy ones.
- Overfitting demos: test with realistic queries and adversarial prompts, not just happy-path examples.
- Skipping monitoring: track quality, latency, cost, and safety metrics before rollout.
- One-size-fits-all prompts: evaluate prompts per domain; log failures and iterate with error buckets.
Next steps
- Pick one portfolio project and set a two-week goal with clear metrics.
- Then deepen skills in the order shown in the Learning path.
- Pick a skill to start in the Skills section below.
FAQ
Q: Do I need a powerful GPU?
A: No to start. Many tasks run on CPU or small GPUs; focus on baselines and evaluation first.
Q: How math-heavy is the role?
A: Practical linear algebra and probability help, but strong engineering and evaluation habits matter most.