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TurboML (https://turboml.com) | Real-Time AI platform | Remote

AI Engineer:

* Build and curate high-quality datasets, leveraging them to train, fine-tune, and optimize Large Language Models.

* Responsibilities include data pipeline development, distributed model training, model evaluation, and performance analysis to improve model accuracy and efficiency.

* Previously worked in small teams.

Apply here: https://turboml.com/careers


TurboML (https://turboml.com/) | Remote | Software Engineer / ML Engineer

TurboML is an ML platform reinvented for real-time. We specialize in bringing real-time ML—similar to the tech behind TikTok—to improve use cases such as fraud detection and recommendations.

Software Engineer: Experience building data, ML, or analytics platforms; Experience with streaming data frameworks such as Apache Kafka and Apache Flink; Proficient in C++ and Rust; previously worked with small teams

ML Engineer: Understand the production lifecycle of ML models; Experience managing multiple ML models in production including retraining; Experience with incremental model training and ML experimentation

Apply here: https://turboml.com/careers


TurboML (https://turboml.com/) | Remote | Software Engineer / ML Engineer / Enterprise Sales / Technical Writer

TurboML is an ML platform reinvented for real-time. ML is data-driven, and TurboML turbo-boosts your ML with real-time data.

Software Engineer: Experience building data, ML, or analytics platforms; Experience with streaming data frameworks such as Apache Kafka and Apache Flink; Proficient in C++ and Rust; previously worked with small teams

ML Engineer: Understand the production lifecycle of ML models; Experience managing multiple ML models in production including retraining; Experience with incremental model training and ML experimentation

Enterprise Sales: Experienced 0-1 journey selling a Data/ML/MLOps platform; An established network within the technology sector

Technical Writer: Experienced in writing blog posts, technical documentation, and web content that simplify MLOps tools for a broader audience.

Apply here: https://turboml.com/careers


TurboML (https://turboml.com/) | India | Enterprise Sales Manager/ Software Engineer / ML Engineer

TurboML is an ML platform reinvented for real-time. ML is data-driven, and TurboML turbo-boosts your ML with real-time data. This means your features, models, and metrics are continuously updated with the freshest data, allowing you to perform ML experimentation and analytics on live data.

Enterprise Sales Manager: Experienced 0-1 journey selling a Data/ML/MLOps platform; An established network within the technology sector

Software Engineer: Experience building data, ML, or analytics platforms; Experience with streaming data frameworks such as Apache Kafka and Apache Flink; Proficient in C++ and Rust; previously worked with small teams

ML Engineer: Understand the production lifecycle of ML models; Experience managing multiple ML models in production including retraining; Experience with incremental model training and ML experimentation

Apply here: https://forms.gle/nARrgHzgPoc4zWHd8


TurboML (https://turboml.com/) | India | DevOps / MLOps / ML / Streaming Systems / UI Designer

TurboML is an ML platform reinvented for real-time. Imagine having all the capabilities of an ML platform – from creating data sources, features, models, deployments and metrics to continuously maintaining and improving them – while simultaneously being able to use the freshest real-time data. How quickly would your team be able to iterate if they could directly test their hypotheses on the live production data? How effective and relevant would your models be if they could learn from real-time data?

By joining TurboML, you'll be part of an early-stage, well-funded team committed to democratizing real-time ML. Send an email with your resume or questions: siddharth@turboml.com


TurboML (https://turboml.com/) | India | Remote | Full-time/Intern | Business Development Manager / Technical Content Architect

TurboML is a real-time machine learning platform designed for fast-paced ML use cases where the freshness of data and low latency are essential.

Business Development Manager (Enterprise Sales): Seeking candidates with a proven track record in enterprise sales, ideally with experience in selling MLOps or ML platforms and tools.

Technical Content Architect: For individuals experienced in writing blog posts, technical documentation, and web content that simplify MLOps tools for a broader audience.

By joining TurboML, you'll become part of an early-stage, well-funded team committed to democratizing real-time ML. Send an email with your resume or questions: siddharth@turboml.com


TurboML | India | Remote | Full Time / Intern | MLOps / DevOps / Streaming / Full Stack

We're building a real-time ML platform for fast-moving use-cases where data freshness and low latency are of utmost importance. We keep your ML models up-to-date and provide live experimentation. We're well funded and looking for Founding Engineers.

Send an email with your resume: siddharth@turboml.com


TurboML | Founding Engineer | Remote | Full Time | Real-Time ML

We're building a real-time machine learning platform for fast-moving ML use-cases such as fraud detection, ad bidding, dynamic pricing, recommender systems. We offer continual learning to keep your ML models up to date with the freshest data, online predictions for ultra-low latency and live experimentation of new features and models.

Looking for Founding Engineers with experience in Streaming Systems/MLOps/DevOps

Send an email with your resume or questions: siddharth@turboml.com


Just as any asset depreciates over time, so does data. Ask yourself how long it takes for you to update your models with today’s data? Is it weeks, or perhaps months? Imagine using stale gasoline in a Ferrari. It might chug along, but with compromised efficiency and potential damage. Similarly, relying on outdated data might keep your models operational, but it exposes you to significant risks, underwhelming user experiences and missed opportunities.


We design a classification algorithm called Clustering Aware Classification (CAC), to find clusters in data that are tailor made to be easily classifiable when used as training datasets by classifiers for each underlying subpopulation. CAC is theoretically motivated, efficient, convergent and provably guaranteed to improve the performance of classifiers using the Logistic Loss functions. The CAC framework improves the performance of 9 different Machine Learning classifiers on 5 standard and 1 large real world dataset. Preprint of the paper can be found at https://arxiv.org/pdf/2102.11872.pdf.


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