Job VC
Machine Learning Engineer (ML)
Technologies
Description
Overview
We are looking for a
Machine Learning Engineer
to design and implement intelligent systems that power personalized digital experiences across connected devices and mobile applications.
This is a
hands-on role focused on applied machine learning and data engineering
, ideal for someone who thrives at the intersection of
ML models, data pipelines, and product integration
. You will collaborate closely with backend engineers, data scientists, and product managers to bring ML-driven features into production and continuously optimize them based on real-world usage.
Responsibilities:
Design, train, and deploy machine learning models for use cases such as
personalization, recommendations, and visual analysis;
Build and maintain scalable, production-grade data pipelines (batch and real-time);
Collaborate with product and engineering teams to translate product ideas into ML-powered features;
Implement model observability, monitoring, and retraining workflows;
Optimize inference performance and model serving in resource-constrained environments (e.g., edge or mobile);
Contribute to the architecture of a modern ML platform within a cloud-native microservices environment;
Work with backend teams to expose model outputs via APIs and integrate them into customer-facing experiences;
Stay up to date with recent ML/AI advancements and evaluate promising methods for future implementation.
Requirements:
3+ years of experience
in applied machine learning or data science;
Strong knowledge of:
Supervised learning, recommendation systems, or computer vision;
Python and ML libraries such as
TensorFlow, PyTorch, scikit-learn;
Building, deploying, and maintaining
ML models in production.
Familiarity with:
MLOps tools
(e.g., MLflow, TFX, Airflow, Vertex AI);
Data pipelines and messaging systems
(e.g., Kafka, BigQuery, dbt);
Model serving frameworks
(e.g., FastAPI, TensorFlow Serving, TorchServe, ONNX);
Comfortable collaborating with product, engineering, and analytics teams;
Experience with cloud platforms (GCP, AWS, or Azure) and container-based deployments;
Bonus: Experience with edge AI, mobile ML, or video/image processing pipelines
.
We are looking for a
Machine Learning Engineer
to design and implement intelligent systems that power personalized digital experiences across connected devices and mobile applications.
This is a
hands-on role focused on applied machine learning and data engineering
, ideal for someone who thrives at the intersection of
ML models, data pipelines, and product integration
. You will collaborate closely with backend engineers, data scientists, and product managers to bring ML-driven features into production and continuously optimize them based on real-world usage.
Responsibilities:
Design, train, and deploy machine learning models for use cases such as
personalization, recommendations, and visual analysis;
Build and maintain scalable, production-grade data pipelines (batch and real-time);
Collaborate with product and engineering teams to translate product ideas into ML-powered features;
Implement model observability, monitoring, and retraining workflows;
Optimize inference performance and model serving in resource-constrained environments (e.g., edge or mobile);
Contribute to the architecture of a modern ML platform within a cloud-native microservices environment;
Work with backend teams to expose model outputs via APIs and integrate them into customer-facing experiences;
Stay up to date with recent ML/AI advancements and evaluate promising methods for future implementation.
Requirements:
3+ years of experience
in applied machine learning or data science;
Strong knowledge of:
Supervised learning, recommendation systems, or computer vision;
Python and ML libraries such as
TensorFlow, PyTorch, scikit-learn;
Building, deploying, and maintaining
ML models in production.
Familiarity with:
MLOps tools
(e.g., MLflow, TFX, Airflow, Vertex AI);
Data pipelines and messaging systems
(e.g., Kafka, BigQuery, dbt);
Model serving frameworks
(e.g., FastAPI, TensorFlow Serving, TorchServe, ONNX);
Comfortable collaborating with product, engineering, and analytics teams;
Experience with cloud platforms (GCP, AWS, or Azure) and container-based deployments;
Bonus: Experience with edge AI, mobile ML, or video/image processing pipelines
.