Machine Learning Engineer résumé: what employers are asking for right now
We read 297 open machine learning engineer postings on employers' own job boards, at companies including OpenAI, Anthropic and Pinterest. Of the 176 whose requirements we have read, the skills asked for most are Python (88%), Machine Learning (53%) and PyTorch (30%) — so those belong near the top of a machine learning engineer résumé, in the words the posting uses. The median posting asks for 5+ years of experience.
From 297 open postings on employers’ own job boards, as of .
Skills machine learning engineer postings ask for
| Skill | Share of postings | Bar |
|---|---|---|
| Python | 88% | |
| Machine Learning | 53% | |
| PyTorch | 30% | |
| LLMS | 25% | |
| TypeScript | 20% | |
| Deep Learning | 20% | |
| SQL | 20% | |
| Distributed Systems | 19% | |
| TensorFlow | 18% | |
| Spark | 16% | |
| Generative AI | 15% | |
| System Design | 15% | |
| Prompt Engineering | 14% | |
| Data Pipelines | 14% | |
| Git | 14% |
Share of the 176 postings whose requirements we have read.
A skills section built from those postings
List only what you can talk about in an interview, and use the posting’s own words for it — a screener searching for “Python” will not match a synonym. Ordered by how often employers ask:
Skills
Python · Machine Learning
PyTorch · LLMS · TypeScript · Deep Learning · SQL · Distributed Systems · TensorFlow · Spark · Generative AI · System Design
Prompt Engineering · Data Pipelines · Git
- Line 1: asked for by a third or more of postings.
- Line 2: often asked for.
- Line 3: sometimes asked for.
Sample machine learning engineer résumé
An illustrative example, not a real person: the employers and results are made up. The skills are the ones the postings above ask for, so it shows how those requirements read on a page.
Senior Machine Learning Engineer
Senior Machine Learning Engineer with 5+ years building ML and GenAI systems in Python across product and platform teams. Experience spans PyTorch, TensorFlow, SQL, Spark, distributed systems, and production LLM workflows from data pipelines to deployment.
Experience
Senior Machine Learning Engineer · Series B generative AI software company
2022 present
- Built LLM-powered retrieval and prompt engineering workflows in Python and PyTorch, reducing manual review time by 38% across a high-volume support product.
- Shipped Generative AI features with TypeScript and Python, coordinating model APIs, evaluation harnesses, and frontend integration for internal and customer-facing tools.
- Designed distributed systems for inference and batch scoring using Ray and Spark, increasing throughput 4x while keeping latency and cloud spend within target.
- Implemented data pipelines and SQL-based feature generation for training and monitoring, improving data freshness from daily to hourly refresh cycles.
Machine Learning Engineer · Regional marketplace platform
2019 2022
- Developed deep learning models in TensorFlow and PyTorch for ranking and classification, lifting model quality by 11% and stabilizing weekly retraining.
- Created Python and SQL pipelines to assemble training datasets from event logs, adding validation checks that cut label and schema issues by 60%.
- Partnered on system design for ML services in Kubernetes and Docker, making model releases repeatable across staging and production environments.
- Maintained Git-based workflows for experiments, reviews, and release branches, shortening model iteration cycles across multiple product teams.
Skills
Python · Machine Learning · PyTorch · LLMS · TypeScript · Deep Learning · SQL · Distributed Systems · TensorFlow · Spark · Generative AI · System Design · Prompt Engineering · Data Pipelines · Git
More machine learning engineer bullet points to adapt
- Built Python services for LLM evaluation and model routing, improving response quality and reducing fallback usage in production.
- Trained Transformer-based classifiers in PyTorch and TensorFlow, raising offline metrics and supporting a higher-volume launch.
- Designed SQL feature stores and batch Data Pipelines that unified event, click, and transaction data for model training.
- Implemented Prompt Engineering patterns for customer workflows, improving task completion rates across multiple use cases.
- Scaled Spark jobs for embedding generation and offline inference, cutting nightly processing time from hours to under one hour.
- Created distributed inference workflows with Ray, increasing parallelism for experiments and large backfills.
- Integrated TypeScript application code with model APIs to support production GenAI features in web products.
- Standardized Git-based experiment tracking and code review practices, reducing regressions in model and pipeline changes.
- Worked with GCP and AWS services to support training, deployment, and monitoring across multiple environments.
- Containerized ML workloads with Docker and Kubernetes to simplify local testing, CI, and production rollout.
What sets an application apart
Listed as a plus rather than a requirement: Kubernetes (15%), Docker (11%), Cursor (9%), GCP (7%), TypeScript (7%), AWS (6%), Langchain (6%) and LLMS (6%). Worth naming if you have it, and never worth claiming if you do not.
Level, location and pay
- Most are senior roles (51% of postings that state a level).
- 35% are listed as remote.
- Listed mainly in United States, United Kingdom and India.
- US postings that publish a yearly salary: median $278k, middle half $242k–$313k (from 29 postings).
Who is hiring machine learning engineers
- OpenAI45 open
- Anthropic19 open
- Pinterest19 open
- Databricks17 open
- Reddit14 open
- Airbnb13 open
- Zoox13 open
- Stripe10 open
- Elastic9 open
- Spotify8 open
- Cloudflare7 open
- Coinbase7 open