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Machine learning engineer resume example

A senior machine learning engineer’s resume with eight years of experience in search, ranking and LLM systems. ML engineering managers look for models that run in production at scale, with latency, cost and quality all measured, so every bullet carries at least one of those numbers.

Machine learning engineer resume example, page 1 of 1Baek Hyun-wooSenior Machine Learning Engineerhyunwoo.baek@example.com · (206) 555-0138 · Seattle, WA · linkedin.com/in/hyunwoobaek-ml ·github.com/example-hwbaekSummaryMachine learning engineer with eight years building search, ranking and LLM systems in production. I takemodels from a notebook to a service that answers in milliseconds, then build the evaluation and monitoring thatkeep them good after launch.ExperienceSenior Machine Learning EngineerApr 2022 – PresentLarkspur Commerce · Seattle, WA•Lead the search ranking platform (PyTorch, Triton, Kubernetes) serving 9,000 queries a second at 35 msp99; a new two-tower retrieval model lifted search revenue 5.1%.•Built the retrieval-augmented support assistant (vLLM, pgvector) answering 40% of seller questions withouta human, with a 600-question evaluation set run on every change.•Cut GPU serving costs by 46% with INT8 quantization, dynamic batching and autoscaling on traffic.•Built the feature store (Feast on Redis and Snowflake) that removed training-serving skew for 11 models.•Tech lead for 5 ML engineers; run the weekly model launch review with data science and product.Machine Learning EngineerJan 2020 – Mar 2022Cascadia Transit Data Lab · Seattle, WA•Shipped an arrival-time prediction model for 400 bus routes to a rider app with 1.2 million monthly users,cutting mean error from 3.8 to 1.9 minutes.•Built Kubeflow training pipelines with automatic retraining and drift alerts for 6 models.Software EngineerJul 2018 – Dec 2019Cedarline Media · Bellevue, WA•Built the event logging and recommendation API behind a news app with 3 million readers.EducationM.S. in Computer Science (Machine Learning)2019 – 2022Georgia Institute of Technology · OnlineB.S. in Computer Science2014 – 2018University of Washington · Seattle, WASkillsPython · PyTorch · Ranking and retrieval · LLM serving (vLLM) · RAG and evaluation · Triton Inference Server ·Kubernetes · Ray · Feast · Kubeflow · AWS (SageMaker, EKS) · Spark · Go · Model monitoringLanguagesKorean Native · English Native
Machine learning engineerUse this example

What recruiters look for

  • Production serving: queries per second, latency, uptime. ML engineers are hired to make models work outside a notebook.
  • Online results: A/B test lifts on business metrics, not only offline accuracy.
  • Infrastructure and cost: GPU spend, batching, quantization, autoscaling. Inference cost is a top concern for every team running models.
  • Evaluation and monitoring: evaluation sets, drift detection, retraining pipelines. Especially important for LLM systems, where quality is hard to see.
  • Software engineering depth: clean services, tests, code review. Many ML engineer interviews include a standard coding round.

Writing the summary

Years, the systems you build (ranking, recommendations, vision, LLM applications), and your strength in the lifecycle: training, serving or platform. A specific scale figure helps.

The summary in this example

Machine learning engineer with eight years building search, ranking and LLM systems in production. I take models from a notebook to a service that answers in milliseconds, then build the evaluation and monitoring that keep them good after launch.

MLOps or ML platform engineer

ML platform engineer with six years building the training and serving infrastructure for 40 data scientists. I make it possible to go from an experiment to a monitored production model in a day, on Kubernetes, MLflow and a shared feature store.

Software engineer moving into ML engineering

Backend engineer with six years in Go and Python, the last two serving recommendation models and building their feature pipelines. Finished Georgia Tech’s online M.S. with a machine learning focus and looking for a full-time ML engineering role.

Bullet points that work

Strong lines for this role, beyond the ones in the example. Borrow the shape and put in your own numbers.

  • Moved batch recommendations to real-time inference, lifting click-through 8% while keeping p99 latency under 50 ms.
  • Distilled a 7-billion-parameter model into a 1-billion-parameter one for ticket classification, cutting serving cost 85% at a 1-point accuracy loss.
  • Built an LLM evaluation harness with 2,000 graded examples and automatic regression checks, blocking 6 bad prompt or model changes in a quarter.
  • Reduced training time for the ranking model from 30 hours to 5 with distributed data parallel training on 16 GPUs.
  • Set up shadow deployments and canary releases for models, ending 3 years of launches that required a rollback.
  • Built a near-duplicate image detector with CLIP embeddings that removed 2 million duplicate listings.
  • Added drift monitoring on 120 features, catching an upstream schema change within an hour instead of a week.
  • Cut cold-start latency of the model server from 90 seconds to 8 by changing how weights were loaded.
  • Wrote the internal guide to serving models on Kubernetes, used by 6 teams to launch 20 services.

Skills to list

Hard skills

  • Python and one systems language (Go, C++, Java or Rust)
  • PyTorch or TensorFlow
  • Model serving (Triton, TorchServe, vLLM, KServe)
  • Retrieval, ranking or recommendation systems
  • LLM application patterns (RAG, fine-tuning, evaluation)
  • Kubernetes and Docker
  • Distributed training (Ray, DeepSpeed, PyTorch DDP)
  • Feature stores and data pipelines (Spark, Kafka)
  • Model monitoring and MLOps (MLflow, Kubeflow, SageMaker)

Soft skills

  • Weighing quality against latency and cost
  • Working with data scientists as partners
  • Writing design documents
  • Leading launch reviews

Certifications and licenses worth listing

  • AWS Certified Machine Learning Engineer, Associate: useful where the stack is SageMaker and AWS.
  • Google Cloud Professional Machine Learning Engineer: respected, and relevant for Vertex AI teams.
  • Certified Kubernetes Application Developer (CKAD): a plus for ML platform roles.
  • Course certificates (online deep learning specializations): list them only early in a career change; shipped systems matter far more.

Length and format

One or two pages. At eight years, two pages are fine, but the current role and its biggest production result belong in the top third. Older software roles get one line that shows your engineering base.

Common mistakes

  • Offline metrics only. “Improved NDCG by 4%” needs the online result that followed.
  • Listing every model architecture you have read about. Name the ones you trained or served.
  • Leaving out latency and cost. For ML engineers, these numbers are often more persuasive than accuracy.
  • LLM bullets without evaluation. “Built a chatbot with GPT” tells a reader nothing about quality or adoption.
  • Blurring the line with data science. If you own serving and infrastructure, make that obvious in the first bullets.

Questions and answers

What is the difference between an ML engineer and a data scientist resume?

Data scientist resumes lead with questions, methods and experiments. ML engineer resumes lead with systems: serving, latency, scale, pipelines and reliability, plus the online results those systems produced.

How should I describe LLM or generative AI experience?

Like any production system: the use case, the architecture in a few words (retrieval, fine-tuning, model size), how you evaluated it and what changed for users or costs.

Do I need a PhD to be a machine learning engineer?

No. Most ML engineering roles hire from strong software backgrounds with a master’s or equivalent experience. Research scientist roles are where a PhD is usually expected.

Should I list research papers?

If you have papers or patents related to the role, add a short Publications section. Otherwise, a link to an open-source project you maintain says more.

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