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Boolean search strings for machine learning engineers: serving, MLOps, LLMs and the ML, AI and LLM collisions

For agency recruiters 7 min read Updated

On this page
  1. Layers, titles and certifications that change what an ML engineer search finds
  2. Ten Boolean strings by layer of the ML stack
  3. What LinkedIn, Dice, Google and GitHub actually support
  4. Narrowing by layer, scale and domain
  5. False positives worth excluding with NOT
  6. A worked example: three passes on a fraud model serving search
  7. What a search cannot verify
  8. Questions people ask

Machine learning engineer searches suffer from vocabulary that everyone borrows. "Machine learning" and "AI" appear on product managers', salespeople's and marketers' profiles; "ML" also means milliliters on lab and pharmacy profiles; "AI" is a file format to designers; "LLM" is a law degree; "transformers" are also power equipment. What separates the engineer a client wants is production work: serving a model, running training pipelines, cutting inference latency, operating the platform. Lead with that layer and the string stops returning people who only talk about AI.

Below are ten strings by layer of the ML stack, and what LinkedIn, Dice, Google and GitHub's own search support as of November 2026. For the operators themselves, see Boolean search strings for recruiters; for research-and-analysis roles, see Boolean search strings for data scientists.

Layers, titles and certifications that change what an ML engineer search finds

LayerTerms candidates writeCommon titles
Model serving and applied MLModel serving, real-time inference, feature store, PyTorch, latencyMachine learning engineer, applied ML engineer
Platform and MLOpsKubeflow, MLflow, SageMaker, Vertex AI, Kubernetes, model registryMLOps engineer, ML platform engineer, ML infrastructure engineer
Training at scaleDistributed training, DeepSpeed, FSDP, CUDA, multi-GPUResearch engineer, ML infrastructure engineer
LLM and generative AIFine-tuning, LoRA, retrieval-augmented generation, embeddings, evaluationAI engineer, LLM engineer, generative AI engineer

Ten Boolean strings by layer of the ML stack

Swap in the frameworks, cloud and metro from your own intake; these are examples. Search on the work, never on terms that stand in for age, sex, race, religion, national origin, disability or veteran status, and keep graduation years out of every string.

Production ML and MLOps

1. Machine learning engineer, production models — LinkedIn Recruiter, Job titles + Keywords filters
("machine learning engineer" OR "ml engineer" OR "applied machine learning engineer") AND (pytorch OR tensorflow) AND ("model serving" OR "model deployment" OR "real-time inference") NOT (intern OR recruiter OR "data analyst")

2. MLOps or ML platform engineer — LinkedIn general search
("mlops engineer" OR "ml platform engineer" OR "machine learning infrastructure") AND (kubeflow OR mlflow OR sagemaker OR "vertex ai") AND (kubernetes OR "feature store" OR "model registry") NOT ("sales engineer" OR "solutions architect")

3. Distributed training engineer — Dice resume search
("machine learning engineer" OR "research engineer" OR "ml infrastructure engineer") AND ("distributed training" OR deepspeed OR fsdp OR "multi-gpu") AND (cuda OR nccl)

LLMs, inference and ranking

4. LLM or generative AI engineer — LinkedIn Recruiter, Keywords filter
("machine learning engineer" OR "ai engineer" OR "llm engineer" OR "generative ai engineer") AND ("retrieval-augmented generation" OR "fine-tuning" OR lora OR "large language models") AND (embeddings OR "vector database" OR "model evaluation") NOT ("master of laws" OR attorney OR copywriter)

5. Inference optimization engineer — Dice resume search
("machine learning engineer" OR "inference engineer") AND ("model optimization" OR quantization OR tensorrt OR onnx) AND (latency OR throughput)

6. Recommendation and ranking engineer — LinkedIn general search
("machine learning engineer" OR "ranking engineer" OR "recommendation systems engineer") AND ("recommender systems" OR "learning to rank" OR "candidate generation") AND ("online experiments" OR "a/b tests") NOT ("data analyst" OR intern)

Vision, certifications, GitHub and X-ray

7. Computer vision engineer — LinkedIn general search
("computer vision engineer" OR "perception engineer" OR "machine learning engineer") AND ("object detection" OR segmentation OR opencv) AND (pytorch OR "edge deployment") NOT (optometrist OR optician)

8. Certified ML engineer — LinkedIn Recruiter, Keywords filter
("machine learning engineer" OR "ml engineer" OR "ai engineer") AND ("aws certified machine learning engineer" OR "professional machine learning engineer" OR "machine learning operations engineer" OR "databricks certified machine learning")

9. GPU-focused developers in a metro — GitHub user search
language:cuda location:seattle type:user

10. Public profiles by layer and metro — Google X-ray
site:linkedin.com/in "machine learning engineer" "model serving" "seattle" -jobs -recruiter -intern

What LinkedIn, Dice, Google and GitHub actually support

As of November 2026, LinkedIn's Boolean help page requires capital AND, OR and NOT, supports quotes and parentheses, evaluates quotes first, then parentheses, NOT, AND and OR, and does not support wildcards, so "fine-tuning" and "finetuning," or "mlops" and "ml ops," need writing out. LinkedIn Recruiter's Boolean page lists Job titles, Companies and Keywords among the Boolean fields and warns that too many facets in one query can cause a search result issue, which long framework lists invite. Put titles in Job titles and the layer in Keywords.

Dice's Boolean guide supports parentheses, quotes, AND, OR and NOT and says its database fills in word endings automatically. Google's search operators page documents quotes, site:, the minus sign and filetype:, but not OR, so string 10 uses none. GitHub's user search documentation lists language:, location:, followers:, repos: and type:user; language: matches users whose repositories are mostly in that language, so language:cuda is a narrow signal for GPU work.

Narrowing by layer, scale and domain

Ask the client where the work sits: research engineering, applied models inside a product, the platform other teams deploy on, or LLM features built on hosted or open models. Each layer has its own words, and a strong platform engineer may never have trained a model from scratch. Scale words come next: "real-time," "low latency," "batch," "edge," "on-device." They separate people who ran models under production constraints from those who trained them offline.

Domain then narrows to people who already know the data: fraud and risk, ads and ranking, search, speech, robotics and autonomy, healthcare imaging. For seniority, look for "designed the serving stack," "owned the training pipeline," "on-call for model services" and "led the migration," against "experimented with" and "built a demo." LinkedIn's premium search filters list Years of Experience and Seniority Level only in Recruiter and Sales Navigator.

False positives worth excluding with NOT

  • The ML, AI and LLM collisions. "ML" is milliliters on lab and pharmacy profiles, "AI" is Adobe Illustrator to designers and artificial insemination in agriculture, and "LLM" is the Master of Laws. Never let the abbreviations stand alone; keep them inside blocks that require engineering words.
  • Electrical transformers. Utility and electrical profiles write "transformers" daily. Pair the word with "hugging face" or "attention" if you use it at all.
  • "Inference" in the statistical sense. Data scientists write "causal inference" and "statistical inference." For serving roles, write "real-time inference" or "inference latency."
  • Talkers, not builders. Product managers, sales engineers, consultants and marketers write "machine learning" and "AI-powered." Exclude "sales engineer," "account executive" and "marketing," and require a production block.
  • API wrappers, for platform roles. Developers who called a hosted model's API list LLM terms without training or serving models. Require training, evaluation or serving words when the client needs depth.
  • Students and course projects. Exclude "intern," "student" and "bootcamp" for experienced roles; course projects use every framework name.

A worked example: three passes on a fraud model serving search

An invented search, shown as it actually gets narrowed.

Intake: A payments company needs a machine learning engineer to move fraud models from notebooks into real-time scoring on Kubernetes and keep them monitored. Not looking for analysts or researchers without production experience.

Pass 1: "machine learning" AND python: data scientists, analysts, product managers, students and engineers in one list.

Pass 2: ("machine learning engineer" OR "ml engineer") AND ("model serving" OR "real-time inference") AND (kubernetes OR sagemaker) NOT (intern OR "data analyst"): production engineers, though spread across every domain.

Pass 3: add AND (fraud OR "risk models" OR "feature store"). Those terms tend to come from engineers who served models on transaction data. The list is short enough to call, and the screen asks how they set a latency budget and how they rolled back a bad model.

The production block removed the people who talk about ML; the domain block found the people who already know the data. Add the second only after the first has done its work.

What a search cannot verify

A string can find someone who lists the right layer, frameworks and cloud. It cannot show whether a model they mention reached production, whether they were on call for it, or whether a certification is current under its old or new name. The machine learning engineer screening questions test production depth, the interview guide for software engineers covers the engineering rounds, and Boolean search strings for software engineers and for Python developers help when the client will consider strong backend engineers moving into ML.

Questions people ask

Why does searching LLM return lawyers?

Because LL.M. is the Master of Laws degree, and many attorneys list it. Write "large language models" in full alongside LLM, require engineering terms such as fine-tuning, retrieval or inference, and exclude "master of laws" and "attorney" with NOT.

Which certifications matter for machine learning engineers in 2026?

They are optional evidence. As of November 2026, AWS offers the Machine Learning Engineer – Associate, Google Cloud the Professional Machine Learning Engineer and Databricks the Machine Learning Professional. Microsoft retired the Azure AI Engineer Associate on June 30, 2026 and points to the Azure AI Apps and Agents Developer Associate, and AWS gave March 31, 2026 as the last day to take its Machine Learning – Specialty exam.

What is the difference between a machine learning engineer search and a data scientist search?

A data scientist search leads with methods such as experiment design or causal inference; a machine learning engineer search leads with production words such as serving, deployment, latency, pipelines and Kubernetes. If the results are full of notebooks and analysis, add a production block.

Can I search GitHub for machine learning engineers?

Yes, with GitHub's own user search. Its documentation lists qualifiers such as language:, location:, followers: and repos:, and language: matches users whose repositories are mostly in that language, so language:cuda with a location finds GPU-focused developers. Work done for employers is often private, so a thin account proves little.

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