Machine Learning Engineer Job Description
What companies actually mean when they post this role — skills, salary, and responsibilities based on 131 live listings.
Avg Salary
$178K–$248K
Listings
131
Remote
40%
Also called
Machine Learning Engineer
What "Machine Learning Engineer" means
The ML Engineer role in 2026 has shifted significantly. Traditionally, an ML Engineer trained models, tuned hyperparameters, and built training pipelines. That work still exists — but the majority of ML Engineer postings now expect fluency with large language models and RAG, reflecting the market's shift toward LLM-powered products.
Across 113 active ML Engineer listings, the dominant pattern is a hybrid role: someone who understands the fundamentals of model training and evaluation (PyTorch, TensorFlow, MLflow) but who also builds LLM-powered features in production. Companies posting "ML Engineer" generally want more ML depth than a typical AI Engineer role — stronger on model architecture, training methodology, and evaluation rigour.
Required skills
Frequency = % of 131 active listings that mention this skill. Priority is derived from frequency and listing emphasis.
Core skills
Important skills
Day-to-day responsibilities
- 1Design, train, and evaluate ML models — from classical methods to fine-tuned transformer models
- 2Build and maintain data pipelines for model training: collection, cleaning, labelling, augmentation
- 3Implement RAG systems and LLM integrations for production AI features
- 4Set up experiment tracking (MLflow, W&B) and maintain reproducibility of training runs
- 5Run model evaluations: offline benchmarks, A/B tests in production, regression detection after updates
- 6Optimise model inference for latency and cost — quantisation, batching, hardware-aware serving
- 7Collaborate with data scientists, product engineers, and AI platform teams
How it differs from related roles
vs AI Engineer
ML Engineers go deeper on model fundamentals — training, architecture, evaluation. AI Engineers focus on product integration using pre-trained models. Salary premium for ML Engineers reflects the deeper technical floor.
vs Data Scientist
Data Scientists analyse data and build models for insight. ML Engineers take models to production and own their ongoing performance at scale.
vs AI Research Engineer
AI Research Engineers work on novel model capabilities, usually in a research setting. ML Engineers work on productionising models, whether trained in-house or fine-tuned from foundation models.
vs MLOps Engineer
MLOps Engineers own the infrastructure that models run on. ML Engineers own the models themselves. In smaller teams, one person does both; at larger companies these are distinct roles.
Salary by level
Junior / Associate (0–2 yrs)
$110K – $150K
Mid-level (2–4 yrs)
$155K – $200K
Senior (4–7 yrs)
$195K – $260K
Staff / Principal (7+ yrs)
$250K – $330K
Ranges from listings with disclosed compensation. US market. Total comp including equity varies significantly by company stage.
Browse Machine Learning Engineer jobs
131 live listings
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