inDrive

Global ride-hailing platform with fare negotiation

Middle Machine Learning Engineer - Computer Vision

Computer VisionUpdated on Nov 13

Full-timeIslamabad, Islamabad Capital Territory, PakistanNo salary listedHybrid

About the Job

Requirements

At least 3 years of experience in the field.
Previous software engineering experience is preferred.
Strong proficiency in Python and common data-processing frameworks for streaming, batch, and asynchronous data.
Foundational knowledge of classical machine learning, deep learning, and advanced mathematics.
Familiarity with MLOps principles and experience using MLOps tools to manage the machine-learning model lifecycle.
Experience with backend systems and infrastructure, and willingness to learn technologies such as Golang for integration.
Ability to design solutions using common design patterns and design tools.
Experience in experiment design and ability to validate the credibility of information from various sources.
Understanding of the business value of delivered features.
Ability to explain the background and consequences of technical decisions to non-technical stakeholders, including product and business managers.
English communication skills.

Responsibilities

Develop, deploy, and maintain machine-learning models and systems.
Conduct in-depth data analysis, preparation, and processing.
Contribute to ML system design, focusing on data-related aspects such as annotation and processing.
Follow established security guidelines and identify potential risks.
Collaborate with peers to review designs and ensure they are effective, flexible, and reusable.
Solve issues and provide feedback on engineering design and delivery processes.
Ensure features are deployed according to requirements and operate without unintended side effects.
Create and maintain comprehensive documentation for models and processes.
Monitor and support deployed solutions to ensure they meet performance expectations.
Seek out information and internal solutions to promote reusability and adoption of common technologies.

Desired Qualifications

Previous software engineering experience.
Strong problem-solving skills based on technical facts and reasoning.
Communication and collaboration skills, including active listening and building consensus.
Drive for continuous self-development and a collaborative approach to contributing to communities.
Ability to balance quality and speed as appropriate and justify the decision.

Our AI Customer Support and Safety team is working on many areas including Computer Vision, NLP, RL etc, bringing business value to our products and improving the lives of our customers. Now we are aiming to strengthen several streams and looking for an experienced middle Machine Learning Engineer, who will help us in our journey. The primary focus for this position will be automation of the driver verification process, for which experience in computer vision is essential.

Develop, deploy, and maintain machine learning models and systems
Conduct in-depth data analysis, preparation, and processing
Contribute to the design of ML systems, focusing on data-related aspects like annotation and processing
Follow established security guidelines and identify potential risks
Collaborate with peers to review and ensure designs are effective, flexible, and reusable
Solve issues and provide feedback for engineering design and delivery processes
Ensure that features are deployed according to requirements and operate without unintended side effects
Create and maintain comprehensive documentation for models and processes
Monitor and support deployed solutions to ensure they meet performance expectations
Actively seek out information and internal solutions to ensure reusability and adoption of common technologies

3+ years of experience in the Field. Previous software engineering experience is preferrable
Strong proficiency in Python and its common data processing frameworks (e.g., for streaming, batch, and asynchronous data).
Strong foundational knowledge in classical machine learning, deep learning, and advanced mathematics.
Familiarity with MLOps principles and experience using MLOps tools for managing the ML model lifecycle.
Experience with backend systems and infrastructure, and a willingness to learn new technologies (like Golang) for integration purposes.
The ability to design solutions using common design patterns and various design tools.
Experience in experiment design and the ability to validate the credibility of information from various sources.
A solid understanding of the business value of delivered features.
Strong problem-solving skills, with an emphasis on using technical facts and reasoning.
Excellent communication and collaboration skills, including active listening and the ability to build consensus.
A strong drive for continuous self-development and a collaborative mindset for contributing to communities.
Ability to balance quality and speed depending on the case, and justify your decision
Ability to explain the background and consequences of your technical decisions to non-technical stakeholders, including product and business managers
English communication skills

About the Company

inDrive

Global ride-hailing platform with fare negotiation

inDrive operates a global mobility and urban services platform that connects passengers with independent drivers through a fare-negotiation system. Riders propose a trip price, and nearby drivers can accept, decline, or counteroffer, creating a peer-to-peer ride-hailing model. The service has expanded to intercity rides, delivery and cargo, groceries, advertising, and driver-focused fintech within its app. Unlike typical ride-hailing apps that use fixed fares, inDrive enables price negotiation and direct driver-rider interaction, and it pursues growth through a venture arm that funds ride-hailing-adjacent startups in emerging markets. This broader strategy aims to build a super-app for urban and gig-economy needs, with the goal of offering a wide range of mobility and urban services in 48 countries.

Company size: 10,001+
Company stage: Debt Financing
Total funding: $532M
Headquarters: Mountain View, CA
Founded: 2012

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