$170,000
mediana · por año, USD anualizado
$189,000
mediana · por año, USD anualizado
Staff+ Research Engineer, RL Data Platform
Data Infrastructure Engineer, Pre-training
Data Engineer
Senior Data Engineer
Staff / Principal Data Engineer
Software Engineer - Data Aquisition (systems)
Software Engineer, Data Acquisition
Senior Staff Backline Engineer - Data & AI
Machine Learning Engineer, Distributed Data Systems - Robotics
Software Engineer, Frontier Data Products
Senior Machine Learning Engineer, DevOps/SRE
Staff Machine Learning Operations Engineer
Staff Engineer - ML Infra / MLOps
Staff ML Ops Engineer
Lead Software Platform Engineer, MLOps
Senior ML Ops Engineer (Machine Learning Infrastructure)
MLOps Engineer
MLOps Engineer (JAX, PyTorch, Pallas/Triton)
ML Ops Infrastructure Engineer
Senior Machine Learning Engineer, Operations Research
| Empresa | Mediana | Puestos |
|---|---|---|
Speechify
|
$170,000 | 102 |
Mindrift
|
$83,200 | 32 |
Accenture Federal Services
|
$144,250 | 27 |
Olsson
|
$121,500 | 21 |
OpenAI
|
$315,000 | 17 |
Esri
|
$113,360 | 15 |
Anduril Industries
|
$181,500 | 14 |
Anthropic
|
$362,500 | 13 |
| Empresa | Mediana | Puestos |
|---|---|---|
Torc Robotics
|
$142,650 | 2 |
Garner Health
|
$324,500 | 1 |
Roku
|
$254,875 | 1 |
Quince
|
$251,500 | 1 |
LVT
|
$242,650 | 1 |
TetraScience
|
$235,000 | 1 |
| AM Atomic Machines | $225,000 | 1 |
Instacart
|
$212,750 | 1 |
| Ubicación | Mediana | Puestos |
|---|---|---|
| San Francisco | $217,500 | 166 |
| New York | $213,281 | 168 |
| Seattle | $203,750 | 42 |
| Boston | $185,000 | 29 |
| Los Angeles | $175,000 | 21 |
| Chicago | $175,000 | 34 |
| Austin | $168,935 | 17 |
| Denver | $150,000 | 28 |
| Atlanta | $145,163 | 12 |
| London | $144,187 | 11 |
La mediana de Data Engineer es de $170,000 por año (normalmente $143,125–$215,000), frente a $189,000 de MLOps ($152,900–$225,000). Eso pone a MLOps alrededor de un 11% por delante en la mediana.
Ambas cifras se calculan en vivo a partir de ofertas activas en JobsRadar y se normalizan a USD anualizado, de modo que Data Engineer y MLOps se comparan en igualdad de condiciones independientemente de la moneda en la que se publicó originalmente cada puesto. Solo los puestos que publican un rango salarial alimentan las medianas; las cifras se actualizan cada pocas horas a medida que se publican nuevos puestos y se cierran los antiguos, así que esta comparación refleja el mercado actual y no una encuesta fija. Úsala como referencia orientativa al sopesar un camino frente a otro.