The specific signals an experienced ml engineer hiring panel grades on during the eight-second scan.

• Summary names the model + the system

  'Owns the inference request router for the 70B LLM on 4M req/day' beats 'ML engineer.' Model + system is what panels scan for.

• Latency-per-token + throughput-per-GPU

  LLM-era metrics. ms/token, tokens/sec/GPU. Generic 'low latency' parses as undated.

• Eval methodology named

  Specific eval framework (lm-evaluation-harness, custom rubric-based, LLM-as-judge). Generic 'evaluated model performance' is filler.

• Production serving stack

  vLLM, Triton, TGI, SGLang, Modal, Ray Serve. Named tools parse as ML-engineering depth.

• Drift / monitoring work

  Data drift, model drift, prompt-injection rate, hallucination rate. Production-ML is graded on monitoring discipline.

• Open-source contribution (if any)

  vLLM, Transformers, PyTorch, JAX, LangChain, LlamaIndex. ML engineering is open-source-native.

1. 1

   Open with the model and the system

   A senior LLM-engineering summary names the model and the system: 'LLM engineer at a Series C AI company; owns the inference request router for the Llama-3.1-70B-Instruct fleet on 4M req/day.' Classical-ML: 'ML engineer at a marketplace; owns the recommendation model + the feature store (Feast) for 38M users.' Entry-level: 'Recent CS grad with shipped Kaggle medal; built a fine-tuned Llama-3.1-8B model on internal support data, public eval writeup at 4,800 reads.'

2. 2

   Quantify with model-era units

   LLM era: ms/token (P50 + P99), tokens/sec/GPU, cost-per-1k-tokens, eval-vs-base improvement (pp). Classical ML: accuracy / precision / recall / AUC, business-metric delta, training time, model size, feature count.

   The numbers that pull a resume forward differ by era; surface the units that match the JD you're targeting.

3. 3

   Name the serving + training stack

   Serving: vLLM, Triton, SGLang, TGI, Modal, Ray Serve, Bento, BudgetServer. Training: PyTorch (2.x with torch.compile), JAX, Transformers, DeepSpeed, FSDP, Accelerate, Axolotl, Unsloth. Evals: lm-evaluation-harness, custom rubric, LLM-as-judge with calibration.

   Named tools parse better than 'ML serving experience.'

4. 4

   Surface eval + monitoring discipline

   Eval discipline is the load-bearing senior-LLM signal. The pattern that works: • Build one rigorous eval that catches regressions. • Run it on every model release. • Gate model deploys on the eval.

   Monitoring: data drift, model drift, prompt-injection rate, hallucination rate (with named methodology), output toxicity, refusal rate.

   ML resumes without monitoring or eval bullets read as research-only.

5. 5

   Close with OSS / paper / shipped model

   High-signal closing items: • Merged PRs to vLLM, Transformers, PyTorch, JAX (named PR, technical description). • NeurIPS / ICML / ICLR paper (named conference, title, role). • Open-sourced fine-tuned model on Hugging Face with downloads + named evals. • NeurIPS / ICML workshop talk.

   These items pull senior ML resumes forward.

Each follows the [verb] [object] [number] structure hiring managers grade against. Copy them as a starting point, swap in your own numbers, and read the annotation to understand why each one works.

• Latency

  Cut p99 ms/token on the Llama-3.1-70B inference path from 84ms to 28ms by migrating from HF Transformers serving to vLLM with continuous batching + paged-attention; speculative decoding with a Llama-3.1-8B draft model accounted for the last 12ms.

  Why it works: Names the model, before/after, three interventions, and the draft model. The depth signals senior LLM engineering.

• Evals

  Built the team's eval harness in lm-evaluation-harness + custom rubric tasks (n=480 prompts, 4-judge ensemble with calibration); held post-fine-tune model to ≥ base + 6pp on the internal benchmark across 4 quarterly releases.

  Why it works: Names the eval framework, harness size, judge-ensemble methodology, and the regression-gate outcome over a sustained window.

• Fine-tuning

  Fine-tuned Llama-3.1-8B on a 38k-row internal support-ticket dataset (LoRA r=16, alpha=32, 3 epochs); resulting model beat GPT-4o-mini on the internal eval by 14pp at 1/12th the per-token cost.

  Why it works: Base model, dataset size, LoRA config, eval outcome, cost comparison. The combo is the senior LLM-fine-tuning signature.

• Cost

  Reduced cost-per-1k-tokens on the inference fleet from $0.42 to $0.18 through three interventions: H100 → H200 migration (1.4× throughput), continuous batching tuning, and prefix caching for long-system-prompt workloads.

  Why it works: Cost-per-token improvement with absolute numbers, hardware migration named, and three specific interventions. LLM cost work is heavily-weighted in 2026 hiring.

• RAG

  Built the team's RAG pipeline (pgvector + sentence-transformers + custom hybrid retriever); retrieval Recall@5 went from 62% to 87% via a BM25 + dense ensemble + LLM rerank, and end-to-end RAG accuracy on the internal eval rose 18pp.

  Why it works: Names the stack, the metric (Recall@5), the retrieval architecture (hybrid + rerank), and the end-to-end outcome. RAG depth at this level is senior signal.

• Safety

  Shipped a real-time prompt-injection detection layer (Llama-3.1-8B guard model + heuristic prefilter); detected 98.4% of injection attempts in the redteam corpus with 0.4% false-positive rate on benign traffic.

  Why it works: Names the architecture (guard + prefilter), the detection rate, and the FP rate. Safety work with sustained metrics is senior signal.

• Monitoring

  Built the model-monitoring system (data drift + output entropy + refusal-rate dashboards in Grafana); caught a 14% accuracy regression within 4 hours of a base-model upstream release.

  Why it works: Names the three monitoring signals, the dashboard tool, and a specific catch outcome. Monitoring discipline is senior signal.

• Tooling

  Migrated the eval pipeline from a notebook-based ad-hoc loop to a CI-gated harness (lm-eval-harness in GitHub Actions); every model release now runs against 480 prompts in 38 minutes; deploy is gated on regression < 2pp.

  Why it works: Names the before/after architecture, the runtime, and the deploy gate. Eval-in-CI is the modern LLM-engineering bread-and-butter.

• Open Source

  Two merged PRs to vllm-project/vllm — one extended the continuous-batching scheduler for prefix-caching across long-prefix workloads; one closed a memory leak in the paged-attention kernel under high tensor-parallelism.

  Why it works: Named project (vLLM), PR count, and two technically meaty descriptions. vLLM is current-vintage LLM serving infra.

• Shipped model

  Open-sourced llama-3.1-8b-quill-support — a LoRA-fine-tuned support-ticket model; 4,800 downloads on Hugging Face; named in two model-card writeups from the AI-eng community.

  Why it works: Named model, download count, external citations. Open-sourced fine-tuned models are gold-standard LLM-engineering credentials.

• Mentorship

  Mentored 2 junior ML engineers transitioning from classical ML into LLM-engineering focus; both shipped sole-owner production LLM systems within 6 months.

  Why it works: Names the transition, timeframe, and deliverable per mentee. Cross-era mentorship is a senior signal.

• Publication

  NeurIPS 2024 workshop paper — 'Speculative decoding with mixture-of-experts draft models' (coauthor, presenter). 380 conference attendees; 2,400 paper views post-publication.

  Why it works: Named conference, paper title, role, and post-publication metrics. Workshop papers carry weight in ML hiring.

Same intent, two phrasings. Read why the right column lands on the keep-pile and the wrong column doesn't.

Patterns our writers see most often when reviewing ml engineer resumes — each one disqualifies candidates faster than weak experience does.

• Mistake

  Conflating classical ML and LLM engineering.

  Fix

  Specify the era. Classical ML and LLM engineering are different roles in 2026.

• Mistake

  Generic 'low latency' claims without ms/token.

  Fix

  LLM era: ms/token (P50, P99), tokens/sec/GPU. Classical ML: training time, inference latency in absolute ms.

• Mistake

  Eval claims without naming the methodology.

  Fix

  Name the eval framework (lm-evaluation-harness, custom rubric, LLM-as-judge with calibration). Generic 'evaluated model' is filler.

• Mistake

  Listing every ML framework without naming what you shipped.

  Fix

  Name the framework only if you've shipped production with it. PyTorch + Transformers + vLLM + a couple of adjacent tools is more credible than ten framework names.

• Mistake

  Notebook-only work without production deployment.

  Fix

  Production ML and notebook ML are different. If you've only shipped notebooks, lead with the experimentation signal — but be transparent.

• Mistake

  Reporting FID metrics without ms/token (in LLM context).

  Fix

  LLM era metrics are ms/token, not generic latency. Match the unit to the JD.

• Mistake

  No mention of monitoring or evals.

  Fix

  Surface eval discipline and monitoring. ML hiring panels in 2026 read for production maturity.

• Mistake

  Two-page resume with fewer than 7 years experience.

  Fix

  One page. ML hiring panels move fast.

Strong verbs lead strong bullets. Replace generic openers (worked on, helped with, was responsible for) with the specific verb that matches what you actually did.

ATS pipelines weight your Skills section as a structured list. Include 15-25 of the items below if they match your experience — not soft skills.