Job VC
ML Engineer
Technologies
Description
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
role
is 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.
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 Offer:
Remote work and flexible working hours.
Legal support.
Paid sick leaves.
Paid vacations.
Medical insurance.
Free English classes.
Machine Learning Engineer
to design and implement intelligent systems that power personalized digital experiences across connected devices and mobile applications.
This
role
is 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.
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 Offer:
Remote work and flexible working hours.
Legal support.
Paid sick leaves.
Paid vacations.
Medical insurance.
Free English classes.