Machine Learning Engineer role guide
Machine Learning Engineer careers in the United States: responsibilities, stack (machine learning, ml engineer, pytorch, tensorflow), training paths, salaries, and 15 active job listings on Ganloss.
Machine Learning Engineer is a core profile on the US artificial intelligence job market. Ganloss currently lists 15 open roles for this profile. This page covers day-to-day work, skills, typical career paths, and resources for applying in 2026.
Day-to-day responsibilities
Train, optimize, and deploy ML/DL models: feature engineering, distributed training, latency optimization, collaboration with data and backend teams.
Stack & skills employers want
PyTorch or TensorFlow, Python, CUDA on GPU workloads, ML system design, offline/online metrics, Git, ML code review.
Typical career path
Mid-level ML engineers are in high demand at AI scale-ups and digital enterprises. Paths toward MLOps, research engineer, or tech lead.
Cross-functional skills
Market keywords: machine learning, ml engineer, pytorch, tensorflow, deep learning. Strong communication, experimentation discipline, product sense, and collaboration with business teams are expected on most Machine Learning Engineer roles.
Training & career switch
Common paths: CS/ML bachelor's or master's, accredited bootcamps, or self-taught portfolios with production projects. Browse LLM, ML, and MLOps training on Ganloss before you apply.
First steps to apply
1) Match your resume to ML keywords. 2) Document 2 production or POC projects. 3) Check salary guides for your target city. 4) Apply on Ganloss with your candidate profile.
Go further
FAQ
- ML engineer vs MLOps?
- ML engineers focus on models and algorithmic performance; MLOps focuses on pipelines, infra, monitoring, and production at scale.
- Where to find Machine Learning Engineer jobs in the United States?
- Browse the Machine Learning Engineer job hub on Ganloss, filter by city or remote, and enable email alerts for new listings.
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