inDrive

Global ride-hailing platform with fare negotiation

Senior ML Engineering Manager - AV Data

Engineering LeadershipUpdated on Nov 13

Full-timeAbu Dhabi - United Arab EmiratesNo salary listedHybrid

About the Job

Requirements

At least 10 years of engineering experience in applied machine learning, autonomous systems, or large-scale data and machine learning platforms, including at least 3 years in the autonomous vehicle industry or closely adjacent autonomy domains such as AV data, simulation or evaluation platforms, advanced driver-assistance systems, robotics perception, or high-definition mapping, and at least 4 years managing engineering teams, with a track record of building teams from scratch and delivering from zero to one.
Strong command of the modern autonomous vehicle and foundation-model landscape, including vision-language models, multimodal perception, 3D reconstruction and neural rendering, and the NVIDIA AV ecosystem, with the depth to set direction, review designs, and make evidence-based technical decisions.
Experience successfully leading data engineering, platform, or infrastructure delivery, in addition to machine learning research, and evaluating and unblocking work across those disciplines.
Proven ability to take large-scale machine learning systems from conception to production, including data pipelines, training and evaluation infrastructure, and models operating under real-world constraints.
Senior-level experience owning technical partnerships, vendor and licensing relationships, or customer-facing technical engagements.
Deep fluency in the modern machine learning stack, including PyTorch or an equivalent, large-scale training and inference, Python, Linux, and cloud data platforms at multi-terabyte scale.
A bachelor's degree in Computer Science, Engineering, or a related field.

Responsibilities

Own the autonomous-vehicle data workstream end to end, including roadmap, execution, quality, and outcomes from vehicle data capture through processing, annotation, and delivery of autonomous-vehicle-grade datasets.
Build the team from zero by hiring and leading a multidisciplinary group covering large-scale data engineering, search and serving, machine learning engineering across divergence detection, edge inference, and vision-language-model-based auto-labeling, 3D reconstruction research, and data annotation operations; own the hiring bar and the team's growth.
Manage the charter of machine learning engineers across the cluster.
Set technical direction across disciplines and provide design authority for the full pipeline, including onboard capture, cloud ingestion and fleet-scale processing, semantic and causal labeling, and neural scene reconstruction; make key technical decisions based on evidence.
Work closely with leaders of the data platform, machine learning operations, geography and mapping, and wider autonomous vehicle initiatives to align roadmaps, share infrastructure, and unblock delivery.
Own external technical partnerships, including relationships with the NVIDIA autonomous-vehicle ecosystem and prospective partners consuming the data.
Balance research and delivery by framing milestones as decision gates, redirecting work based on evidence, and keeping the team shipping measurable results on schedule.

Desired Qualifications

Experience in autonomous-vehicle offline evaluation, data mining, annotation platforms, or simulation, distinct from on-vehicle stack development.
An advanced degree (MS or PhD) in machine learning, robotics, or computer vision.
Experience operating in emerging markets and comfort building where no established playbook exists.

Autonomous vehicles are coming to the markets in which inDrive serves, but the AV industry has a blind spot: it has almost no data from the cities where we operate. inDrive runs one of the world's largest ride-hailing fleets, concentrated in exactly the emerging markets global AV players have yet to reach. We are building the team that turns that fleet into an AV grade data platform: large scale multi sensor collection from real vehicles on real trips, evaluation of driving models against human drivers, automated and human in the loop causal annotation, and camera first 3D scene reconstruction for simulation.

We are hiring the founding leader of this workstream. This is a rare true zero to one: you will hire the team, stand up the vehicles and the data platform, set the technical bar, and own delivery end to end.

Own the AV data workstream end to end. Roadmap, execution, quality, and outcomes, from vehicle data capture through processing, annotation, and delivery of AV-grade datasets
Build the team from zero. Hire and lead a multi disciplinary team covering large scale data engineering, search and serving, ML engineering across divergence detection, edge inference, and VLM based auto labeling, 3D reconstruction research, and data annotation operations. Own the hiring bar and the team's growth
Management: Manage ML engineers' charter across the cluster
Set technical direction across disciplines. Provide design authority for the full pipeline, including onboard capture, cloud ingestion and processing at fleet scale, semantic and causal labeling, and neural scene reconstruction, and make the key technical decisions on evidence
Partner with leaders across the organization. Work closely with the leaders of data platform, ML ops, geo and mapping, and inDrive's wider autonomous vehicle initiatives to align roadmaps, share infrastructure, and unblock delivery
Own external partnerships. Drive the technical relationships this work depends on, including the NVIDIA AV ecosystem and prospective AV partners consuming our data
Balance research and delivery. Several of the core questions here are open research. You frame milestones as decision gates, redirect work based on evidence, and keep the team shipping measurable results on a schedule

10+ years of engineering experience in applied ML, autonomous systems, or large-scale data and ML platforms, including 3+ years in the autonomous vehicle industry or closely adjacent autonomy domains (AV data, simulation, or evaluation platforms, ADAS, robotics perception, HD mapping), and 4+ years managing engineering teams, with a track record of building teams from scratch and delivering from zero to one
Strong command of the modern AV and foundation model landscape: vision-language models, multi-modal perception, 3D reconstruction and neural rendering, and the NVIDIA AV ecosystem. Deep enough to set direction, review designs, and make evidence-based technical calls
Genuine engineering leadership range: you have successfully led data engineering, platform, or infrastructure delivery, not only ML research, and can evaluate and unblock work across those disciplines
Proven ability to take large-scale ML systems from conception to production: data pipelines, training and evaluation infrastructure, and models running under real-world constraints
Strong external presence: experience owning technical partnerships, vendor and licensing relationships, or customer-facing technical engagements at a senior level
Deep fluency in the modern ML stack (PyTorch or equivalent, large-scale training and inference, Python and Linux) and in cloud data platforms at multi-terabyte scale
Bachelor's degree in Computer Science, Engineering, or a related field

Nice to have:

AV industry experience specifically in offline evaluation, data mining, annotation platforms, or simulation, as distinct from on-vehicle stack development
Advanced degree (MS or PhD) in ML, robotics, or computer vision
Experience operating in emerging markets, and comfort building where the playbook does not exist yet

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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