Step 1: Define Role Clusters & Your Keyword Map

ML titles vary by employer. Build clusters so your searches catch synonyms and adjacent roles.

LLMs & NLP

• Titles: LLM Engineer, NLP Engineer/Scientist, Generative AI Engineer, Conversational AI.

• Stack: PyTorch, TensorFlow, JAX, Transformers, Hugging Face, vLLM, LoRA/QLoRA/PEFT, RAG, vector databases (FAISS/Milvus/Pinecone), LangChain/LlamaIndex/DSPy, prompt evaluation (Ragas), guardrails/safety.

Computer Vision & Perception

• Titles: Computer Vision Engineer/Scientist, Perception Engineer, Imaging ML Engineer.

• Stack: PyTorch/TensorFlow, OpenCV, Detectron/YOLO/SegFormer, ONNX Runtime, TensorRT, OpenVINO, DeepStream, tracking, multi-view geometry.

Core ML / Applied ML

• Titles: Machine Learning Engineer, Applied Scientist, Data Scientist (ML-leaning).

• Stack: scikit-learn, XGBoost/LightGBM/CatBoost, feature engineering, SHAP, model monitoring, batch & streaming inference, FastAPI.

Recommenders & Ranking

• Titles: Recommender Systems Engineer, Ranking/Personalisation Engineer.

• Stack: implicit MF, two-tower/sequence models, bandits, ANN search, metrics (MAP/NDCG/CTR), feature stores.

MLOps & Platform

• Titles: MLOps Engineer, ML Platform Engineer, ML Infra Engineer.

• Stack: Docker, Kubernetes, Kubeflow/Flyte, KServe/BentoML/Seldon, feature stores (Feast/Tecton), MLflow/Weights & Biases/Comet, Airflow/Prefect/Dagster, model registry, CI/CD, observability/monitoring (Arize/Fiddler/WhyLabs), data versioning (DVC), Ray/Spark.

Performance / Distributed / Compilers

• Titles: ML Systems Engineer, Performance Engineer, Training Platform Engineer.

• Stack: CUDA, Triton (compiler), XLA, FSDP/ZeRO/DeepSpeed, mixed precision, NCCL, sharding, quantisation.

Research & Applied Science

• Titles: Research Scientist, Applied Scientist.

• Topics: pre-training, fine-tuning, SOTA benchmarks, evaluation harnesses, publications/open source.

Edge ML / On-device

• Titles: Edge ML Engineer, Inference Engineer.

• Stack: ONNX, TensorRT-LLM, OpenVINO, Core ML, TFLite, quantisation/pruning, Jetson/Coral/iOS/Android NN.

UK locations & modes

• London, Cambridge, Oxford, Bristol/Bath, Manchester, Edinburgh/Glasgow, Leeds, Birmingham, Reading/Thames Valley, Belfast.

• Modes: Remote UK, Hybrid, On-site. Regulated/Gov work may need security clearance.

• Modifiers: “Visa sponsorship”, “Skilled Worker visa”, “Graduate”, “Internship”, “Permanent”.

Capture this keyword map—you’ll reuse it in alerts, feeds, and prompts.

Step 2: Build Precise Boolean Searches (Copy & Paste)

Start broad in Google, then add site: filters for high-signal employer & ATS domains.

General UK ML Search

("Machine Learning Engineer" OR "Applied Scientist" OR "LLM Engineer" OR "NLP Engineer" OR "Computer Vision Engineer")
(PyTorch OR TensorFlow OR JAX OR "Hugging Face" OR Transformers OR "MLflow" OR Kubernetes)
(UK OR "United Kingdom" OR London OR Cambridge OR Oxford OR Manchester OR Bristol OR Edinburgh)
("Permanent" OR "Full-time") -site:indeed.co.uk -site:glassdoor.co.uk

LLM / NLP

("LLM Engineer" OR "NLP Engineer" OR "Generative AI Engineer" OR "Conversational AI")
(Transformers OR "Hugging Face" OR RAG OR embeddings OR LoRA OR vLLM OR "prompt" OR "guardrails")
(PyTorch OR JAX OR TensorFlow) (UK OR "Remote UK")

Computer Vision

("Computer Vision" OR "Perception Engineer" OR "Imaging Scientist")
(PyTorch OR TensorFlow OR "OpenCV" OR "object detection" OR segmentation OR "TensorRT" OR "OpenVINO")
(UK OR "Remote UK")

Core ML / Applied ML

("Machine Learning Engineer" OR "Applied Scientist")
("scikit-learn" OR XGBoost OR LightGBM OR CatBoost OR SHAP OR "feature engineering" OR "model monitoring")
(UK OR "Remote UK")

Recommenders / Ranking

("Recommender" OR "Recommendation" OR "Ranking Engineer" OR "Personalisation")
(MAP OR NDCG OR "two-tower" OR "bandit" OR embeddings OR "ANN" OR Faiss)
(UK OR "Remote UK")

MLOps / Platform

("MLOps Engineer" OR "ML Platform Engineer" OR "ML Infrastructure")
(Kubernetes OR KServe OR Seldon OR BentoML OR "model registry" OR MLflow OR "feature store" OR Kubeflow OR Flyte OR Airflow OR Prefect)
(UK OR "Remote UK")

Performance / Distributed Training

("ML Systems" OR "Performance Engineer" OR "Training Platform" OR "Distributed Training")
(CUDA OR Triton OR XLA OR "FSDP" OR "ZeRO" OR "DeepSpeed" OR NCCL OR "mixed precision")
(UK OR "Remote UK")

Research & Applied Science

("Research Scientist" OR "Applied Scientist") (pretraining OR "fine-tuning" OR "benchmark" OR "SOTA" OR "open source")
(PyTorch OR JAX OR TensorFlow) (UK OR "Remote UK")

Edge / On-device

("Edge ML" OR "On-device" OR "Inference Engineer")
(ONNX OR "TensorRT-LLM" OR "OpenVINO" OR "Core ML" OR "TFLite" OR quantisation OR pruning)
(UK OR "Remote UK")

Graduate & Early Career

("Graduate" OR "Junior" OR "Internship") ("machine learning" OR "applied scientist" OR "computer vision" OR NLP) (Python OR PyTorch OR TensorFlow) (UK OR "Remote UK")

Visa/clearance (optional)

("Machine Learning" OR "AI Engineer") ("visa sponsorship" OR "Skilled Worker visa" OR "security clearance") (UK)

ATS & Employer Career Sites (cuts aggregator noise)

("Machine Learning Engineer" OR "Applied Scientist" OR "LLM Engineer" OR "Computer Vision")
(site:boards.greenhouse.io OR site:lever.co OR site:workable.com OR site:ashbyhq.com OR site:smartrecruiters.com OR site:icims.com OR site:successfactors.com)
(UK OR "Remote UK")

Step 3: Turn Searches Into Google Alerts & RSS

Let new postings come to you automatically.

Setup (quick)

1. Open Google Alerts.

2. Paste one Boolean string.

3. Show options → choose At most once a day (or As-it-happens if you’re sprinting).

4. Deliver to: pick RSS feed (paste into Feedly/Inoreader) or your email.

5. Create separate alerts per cluster: LLM/NLP, Vision, Core ML, Recommenders, MLOps/Platform, Distributed/Perf, Research, Edge, Graduate.

Good alert examples (copy-paste):

("LLM Engineer" OR "NLP Engineer" OR "Generative AI") (Transformers OR "Hugging Face" OR RAG OR LoRA OR vLLM) (PyTorch OR JAX) (UK OR "Remote UK")

("MLOps Engineer" OR "ML Platform Engineer") (KServe OR Seldon OR BentoML OR "model registry" OR "feature store" OR MLflow OR Kubeflow OR Flyte) (UK OR "Remote UK")

("Computer Vision" OR "Perception Engineer") (OpenCV OR TensorRT OR "OpenVINO" OR "object detection" OR segmentation) (UK OR "Remote UK")

("ML Systems" OR "Distributed Training" OR "Performance Engineer") (CUDA OR Triton OR XLA OR FSDP OR "DeepSpeed") (UK OR "Remote UK")

Pro tips

• One alert per intent = cleaner results.

• Pair locations sensibly: London + “Remote UK” catches most roles; add Cambridge/Oxford/Bristol for deep-tech.

• Use -site: to mute noisy domains that flood your feed.

Prefer RSS? Tag/star items and export starred roles as CSV—ideal for a weekly planning pass with ChatGPT.

Step 4: Use ChatGPT as Your “ML Job Scout”

Alerts & RSS supply raw listings; ChatGPT turns them into a ranked shortlist with actions so you apply faster & better.

Reusable system prompt (edit to your targets):

System role: You are my ML Job Scout for UK roles. Parse pasted job listings (title, company, location, link, snippet), remove duplicates by company+title+location, and produce a ranked shortlist that matches my criteria. Then provide tailored actions for each role.

My criteria:
• Target clusters: LLM/NLP, Computer Vision, Core ML, Recommenders, MLOps/Platform, Distributed/Perf, Research, Edge.
• Must-haves by cluster:
  - LLM/NLP: Transformers/HF, RAG, embeddings, LoRA/QLoRA, safety/guardrails; PyTorch/JAX.
  - Vision: detection/segmentation, OpenCV, TensorRT/OpenVINO, evaluation metrics (mAP/IoU).
  - Core ML: scikit-learn + boosting, feature engineering, SHAP, monitoring.
  - Recommenders: sequence/two-tower, ANN search, MAP/NDCG, feature store.
  - MLOps: K8s + KServe/Seldon/Bento, MLflow/W&B, registry, CI/CD, observability.
  - Distributed/Perf: CUDA, Triton/XLA, FSDP/ZeRO/DeepSpeed, NCCL, mixed precision.
  - Research: pretraining, fine-tuning, benchmarks, open source.
  - Edge: ONNX/TensorRT-LLM/OpenVINO, quantisation/pruning, device constraints.
• Location: Remote UK or London/Cambridge/Oxford/Bristol/Manchester hybrid.
• Exclude: pure BI roles, contract <3 months, agency spam.

Output:
1) Summary: counts & duplicates removed; scoring logic in 2 lines.
2) Ranked Shortlist (max 10): Title — Company — Location — Link — Score (0–100) — 1–2 line fit rationale.
3) Per-role actions:
   - 3 tailored CV bullets (impact-led, stack & outcomes).
   - 6–10 keywords to mirror (frameworks, methods, metrics).
   - A 3-sentence message to the hiring contact referencing one concrete requirement.
4) Today plan: the order to apply with time estimates.

Daily run (paste your feed)

Here are today’s roles (Title — Company — Location — Link — Snippet):
1) ...
2) ...
Apply the ML Job Scout system prompt.

Deep-dive on a single role (for the perfect match)

Analyse this spec for must-haves, repeated terms & implied priorities. Then:
• Write 3 CV bullets that mirror the spec (cluster-appropriate), each ending with a measurable outcome.
• Draft a 120-word cover note referencing the product/domain & one 30-day quick win.
• List 10 keywords/phrases to include naturally (frameworks, methods, metrics).
• Provide 6 likely interview questions with succinct model answers using my background.

Job spec: [paste]
My background: [4–8 bullets with stack & outcomes]

Fast CV tailoring prompts (cluster-specific)

LLM/NLP

Create 5 “Recent Impact” bullets showing Transformers/HF, RAG, embeddings & LoRA/QLoRA—each with a latency, quality or cost metric. UK spelling, one line each.
Spec: [paste]

Vision

Produce 5 bullets covering detection/segmentation, data augmentation, TensorRT/OpenVINO optimisation & evaluation (mAP/IoU)—each with an accuracy/FPS/latency metric.
Spec: [paste]

Core ML

Write 5 bullets on feature engineering, model selection, SHAP insights & monitoring—each with AUROC/F1/lift or business impact.
Spec: [paste]

Recommenders

Draft 5 bullets on two-tower/sequence models, ANN retrieval, ranking metrics (MAP/NDCG) & cold-start strategies—each with measurable gains.
Spec: [paste]

MLOps/Platform

Output 5 bullets on KServe/Seldon/Bento, MLflow registry, feature store, CI/CD & monitoring/drift—each with reliability/cost or time-to-ship improvement.
Spec: [paste]

Distributed/Perf

Provide 5 bullets on FSDP/ZeRO/DeepSpeed, CUDA/Triton, mixed precision & NCCL scaling—each with throughput, memory or training time reduction.
Spec: [paste]

Edge

Write 5 bullets on ONNX/TensorRT-LLM/OpenVINO, quantisation/pruning & device constraints—each with accuracy vs. latency/power trade-offs.
Spec: [paste]

Step 5: Optional No-Code Automation (Email, Slack, Notion)

• Email filters: Route alert emails into an “ML-Jobs” label. Each morning, paste the best items into ChatGPT and run your ML Job Scout prompt.

• RSS rules: Tag feeds by cluster (LLM/NLP, Vision, Core ML, Recommenders, MLOps, Distributed, Research, Edge). Star the best and export weekly as CSV for prioritisation.

• Notion/Sheets: Keep one tracker; paste it into ChatGPT for daily application order and follow-ups.

• Slack/Discord: Pipe starred roles into a private channel for quick triage via webhook.

Step 6: A Simple Pipeline Tracker That Wins Interviews

Suggested columns

• Date found

• Role

• Company

• Location

• Link

• Cluster (LLM/NLP/Vision/Core/Recs/MLOps/Dist/Research/Edge)

• Match score (0–100)

• Status (To apply / Applied / Interview / Offer / On hold / Rejected)

• Deadline / due date

• Contact (name, LinkedIn/email)

• Notes (stack, methods, metrics)

• Next action (what & when)

Follow-up rhythm

• T+3 days: polite nudge if no acknowledgement.

• T+10 days: request an update; include a small proof point (e.g., a redacted evaluation table or perf chart—no confidential data).

• Post-interview: thank-you within 24 hours; reference one spec requirement and a 30-day quick win.

Shareable Prompt Library (ML-Specific)

1) Role Decoder

Explain this ML role in plain English: first 90-day deliverables, 3 hardest problems & the exact skills they truly need (frameworks, methods, metrics, infra). Then list the top 12 CV keywords they’ll search for. [paste spec]

2) Company Fit Snapshot

From the spec & site notes, infer domain (product/fintech/health/robotics), stack (PyTorch/JAX/TensorFlow), infra (K8s/registry/feature store), & maturity (prototype → prod). Output a 6-bullet “Why me, why now” pitch.
[spec + brief company notes]

3) CV Bullet Rewriter (Impact-led)

Rewrite these bullets with action+method+metric, mirroring the spec vocabulary (Transformers/RAG/LoRA, TensorRT/OpenVINO, MLflow/registry, SLOs). One line each, UK spelling.
[bullets + spec]

4) Outreach Message (120 words)

Draft a concise message for the hiring contact that references one stack detail (e.g., vLLM + LoRA on PyTorch) & proposes a 30-day quick win. Mirror 3 spec keywords; confident tone, no fluff.
[spec + company notes]

5) Interview Pack Generator

Produce 8 technical questions + short model answers tailored to this spec (cluster-appropriate), plus 5 behavioural questions with STAR hints using my background.
[spec + background]

6) Offer & Salary Prep (UK)

Given the role, my years of experience & market norms, suggest a negotiation range in GBP, non-salary levers (compute budget, conferences, training, equity), & 3 crisp value statements I can use.
[spec + experience]

Keyword & Query Bank (Use Across Alerts, Feeds & Boards)

TitlesMachine Learning Engineer, LLM Engineer, NLP Engineer/Scientist, Computer Vision Engineer/Scientist, Applied Scientist, Research Scientist, Recommender Systems Engineer, ML Platform/MLOps Engineer, ML Systems/Performance Engineer, Edge ML/Inference Engineer.

Frameworks & LibrariesPyTorch, TensorFlow, JAX, Transformers, Hugging Face, vLLM, OpenAI/Anthropic APIs, LoRA/QLoRA/PEFT, scikit-learn, XGBoost/LightGBM/CatBoost, Ray, Spark, Optuna, Ray Tune.

Inference & OptimisationONNX/ONNX Runtime, TensorRT/TensorRT-LLM, OpenVINO, KServe, Seldon, BentoML, Triton Inference Server, quantisation/pruning, mixed precision.

Platform & OpsDocker, Kubernetes, Kubeflow/Flyte, MLflow/Weights & Biases, feature stores (Feast/Tecton), model registry, CI/CD, monitoring/drift, observability tools.

Data & StorageDelta Lake, Snowflake/BigQuery/Redshift, Kafka/Kinesis/Pub/Sub, Airflow/Prefect/Dagster, DVC.

RAG & VectorsFAISS, Milvus, Pinecone, pgvector, LangChain, LlamaIndex, DSPy, evaluation (Ragas), guardrails.

MetricsAUROC/AUPRC/F1, mAP/IoU, MAP/NDCG, latency/throughput/cost, drift, coverage, SLOs.

ModifiersRemote UK, Hybrid, On-site, Permanent, Contract, Graduate, Internship, Visa sponsorship, Security clearance.

Sample Daily Workflow (7–12 Minutes)

1. Open your alert folder/RSS. Skim headlines; bin obvious mismatches.

2. Paste 10–30 items into ChatGPT with your ML Job Scout prompt.

3. Review the shortlist. Open the top 3–5 high-score roles.

4. Run the deep-dive prompt on your favourite; generate tailored CV bullets and a 120-word cover note.

5. Update your tracker & set deadlines.

6. Apply in one sitting—mirror 6–10 keywords naturally (frameworks, methods, metrics).

7. Schedule follow-ups immediately.

Consistency beats weekend blitzes.

Troubleshooting & Tuning

“Still getting noise.”Anchor searches to stack tokens (Transformers, vLLM, KServe, TensorRT, MLflow) and exclude agency spam with -site: or -"recruitment agency".

“Everything’s Senior/Principal.”Include (Junior OR Mid OR "2–4 years") and exclude (Senior OR Principal OR Lead).

“Remote actually means global.”Use ("Remote UK" OR "UK-based remote" OR "right to work in the UK") and exclude "anywhere" where needed.

“I’m research-leaning.”Bias to Research/Applied Scientist, add conferences (NeurIPS/ICLR/ICML/ACL/CVPR/EMNLP) and open-source signals.

“I want production MLOps.”Bias to KServe/Seldon/Bento, MLflow/registry, feature store, CI/CD, observability, SLO.

Lightweight Tracker Template (Copy Text)

Date Found | Role | Company | Location | Link | Cluster | Match (0–100) | Status | Deadline | Contact | Notes (stack/methods/metrics) | Next Action

Status: To apply / Applied / Interview / Offer / On hold / Rejected

Daily command for ChatGPT:“From my tracker (below), propose today’s top 5 applications, fill missing ‘Next Action’, and draft follow-ups where Status=Applied & T+3 days.”

Copy-Paste Pack (Everything In One Place)

1) Google Alerts seeds

("LLM Engineer" OR "NLP Engineer" OR "Generative AI") (Transformers OR "Hugging Face" OR RAG OR embeddings OR LoRA OR vLLM) (PyTorch OR JAX) (UK OR "Remote UK")
("Machine Learning Engineer" OR "Applied Scientist") ("scikit-learn" OR XGBoost OR LightGBM OR SHAP OR "model monitoring") (UK OR "Remote UK")
("Computer Vision" OR "Perception Engineer") (OpenCV OR TensorRT OR "OpenVINO" OR detection OR segmentation) (UK OR "Remote UK")
("MLOps Engineer" OR "ML Platform") (KServe OR Seldon OR BentoML OR "feature store" OR "model registry" OR MLflow OR Kubeflow OR Flyte OR Airflow) (UK OR "Remote UK")
("ML Systems" OR "Distributed Training" OR "Performance Engineer") (CUDA OR Triton OR XLA OR FSDP OR ZeRO OR DeepSpeed OR NCCL) (UK OR "Remote UK")
("Edge ML" OR "On-device") (ONNX OR "TensorRT-LLM" OR "OpenVINO" OR "TFLite" OR "Core ML" OR quantisation) (UK OR "Remote UK")
("Graduate" OR "Junior" OR "Internship") ("machine learning" OR NLP OR "computer vision") (Python OR PyTorch OR TensorFlow) (UK OR "Remote UK")

2) ATS-focused Google search

("Machine Learning Engineer" OR "LLM Engineer" OR "Applied Scientist" OR "Computer Vision")
(site:boards.greenhouse.io OR site:lever.co OR site:workable.com OR site:ashbyhq.com OR site:smartrecruiters.com OR site:icims.com OR site:successfactors.com)
(UK OR "Remote UK")

3) ML Job Scout (short version)

You are my UK ML Job Scout. From pasted listings, remove duplicates, rank by fit to my criteria, and output:
• Summary (counts + scoring)
• Top 10 roles (Title — Company — Location — Link — Score — 1-line why)
• Per-role actions (3 CV bullets, 6–10 keywords, 3-sentence outreach)
Criteria: [paste your clusters & must-haves]

4) Deep-dive tailoring

Analyse this spec. Return: 3 tailored CV bullets (action+method/infra+impact), 10 keywords, a 120-word cover note referencing the domain/stack & a 30-day quick win, and 6 interview Qs with model answers.
Spec: [paste]  |  Background: [paste]

5) Follow-up message

Please draft a concise follow-up for my application submitted on [date], referencing [one framework/method/metric] from the spec and reaffirming my fit in 2 sentences.

Final Thoughts

The edge in ML job-hunting isn’t heroic scrolling; it’s an efficient pipeline you can run every day. Put discovery on autopilot with alerts & RSS, let ChatGPT act as your ML Job Scout, and ship one excellent application daily. Mirror the frameworks, methods, infra and metrics the spec cares about, quantify impact, and keep tight feedback loops. Do this for two weeks and you’ll feel the shift—from hunting posts to booking interviews.

If you want a quick win, start with this alert:

("LLM Engineer" OR "NLP Engineer") (Transformers OR "Hugging Face" OR RAG OR LoRA OR vLLM) (PyTorch OR JAX) (UK OR "Remote UK")

Paste the first batch into ChatGPT with your ML Job Scout prompt—and enjoy your first hour back this week.