Machine Learning Engineer — Niche but massively sought-after in 2025.

About the Role

Data is our fuel, but you are the engine. We are looking for a

Machine Learning Engineer

who treats model development as an engineering discipline, not just an experiment.

In this role, you will bridge the gap between data science and production software. You will design

predictive models

, build automated

ML pipelines

, and ensure our algorithms run efficiently at scale. You are perfect for this role if you love

Python

, live in

Linux

, and dream in

vectors

.

### What You Will Do

• Model Development:

Design, train, and validate machine learning models (Regression, Classification, Clustering, Deep Learning) to solve business problems.

• Production Engineering (MLOps):

Build scalable pipelines using tools like

Kubeflow

,

MLflow

, or

Airflow

to automate model training and deployment.

• Optimization:

Improve model inference time and reduce latency for real-time applications.

• Data Wrangling:

Write complex SQL queries and build ETL processes to prepare massive datasets for training.

• Feature Engineering:

Identify and extract key features from raw data to improve model accuracy.

• Collaboration:

Work closely with Data Scientists to take research prototypes and turn them into production-grade code.

Requirements

What We Are Looking For

• Experience:

2\+ years in Machine Learning Engineering or a heavy Data Science role with production responsibility.

• Core Languages:

Advanced proficiency in

Python

(Pandas, NumPy) and familiarity with

C\+\+

or

Java

is a plus.

• ML Frameworks:

Deep experience with

TensorFlow

,

PyTorch

,

Scikit-learn

, or

XGBoost

.

• Math \& Stats:

Strong foundation in probability, statistics, and linear algebra.

• Cloud Native:

Hands-on experience with

AWS SageMaker

,

Google Vertex AI

, or

Azure ML

.

• Big Data:

Familiarity with

Spark

,

Hadoop

, or

Kafka

for handling large-scale data streams.

### Preferred Tech Stack (Keywords)

• Frameworks:

PyTorch, TensorFlow, Scikit-learn, Keras

• MLOps:

Docker, Kubernetes, MLflow, Jenkins

• Data:

SQL, NoSQL, Spark, Databricks

Benefits

Compensation \& Benefits

• Salary Range:

$50,000 – $200,000 USD / year

(Based on experience and location)

• Flexible Work:

100% Remote or Hybrid options.

• Tools:

Access to high-performance compute clusters and GPUs.

• Growth:

Budget for conferences (NeurIPS, ICML) and certifications.