Ottometric Serbia

Ottometric helps automotive OEMs and Tier-1 suppliers speed ADAS validation and AV training by turning raw vehicle sensor and system data into prioritized, traceable scenarios. Its SaaS platform uses…

ADASAIAutomotiveAV TrainingData CollectionSimulationSoftwareValidationottometric.com

Ottometric Serbia

ADASAIAutomotiveAV TrainingData CollectionSimulationSoftwareValidationottometric.com

HQWaltham, US

Team Size47

Open JobsUnknown

Total Funding$15M

Latest Fundraiselast year

TL;DR

Product: SaaS platform that turns vehicle sensor and system data into prioritized, traceable ADAS/AV validation scenarios, KPIs, and visual reviews (OttoViz).

Tech: Proprietary AI, computer vision, big-data analytics, generative AI; deployed on AWS and Linux.

Founded: 2018 (company materials also reference 2019 in some places).

HQ: Waltham, Massachusetts (880 Winter Street).

Funding: Multiple rounds including Seed (Jan 18, 2023) and Series A (Apr 9, 2025); total funding reported ~$15,178,891 USD.

Company Overview

Problem Domain

ADAS and autonomous-vehicle validation and training using large-scale vehicle sensor and system data.

Founded

2018

Industry

Information Technology

Tech Stack

Amazon CloudFront

Amazon S3

Amazon SSL

nginx

Express

IPv6

DNSSEC

DigiCert SSL

Apple Mobile Web App Capable

Apple Mobile Web App Status Bar Style

AWS

Linux

Funding Track Record

Pre-seed- 2019-08

Reported pre-seed round in Aug 2019

Seed- 2023-01-18

Seed round dated Jan 18, 2023

Series A- 2025-04-09

10000000 USD

Series A announced Apr 9, 2025; Schooner Capital listed as lead

Investor Signal

“Automotive-focused and venture investors including Schooner Capital, Rally Ventures, Proeza Ventures, Goodyear Ventures, Automotive Ventures, Trucks Venture Capital, and others”

Founders

What we do

Join the Team

Senior Data Architect

On-SiteNovi Sad, Vojvodina, RS

On-Site • Novi Sad, Vojvodina, RS

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As a Principal Data Architect, you will be the primary visionary for our global data strategy. You will tackle the "unsolved" problems of autonomous vehicle data: how to efficiently store, index, and query petabytes of high-dimensional, multi-modal sensor data.

You will lead the transition of our data infrastructure into a state-of-the-art Open Lakehouse

architecture, leveraging Apache Iceberg

and the Hadoop

ecosystem to create a deterministic, high-performance environment for ML research and safety-critical validation.

This role would require you to work for two years in our Serbian office, with the potential of then moving to the US office.

Core Responsibilities

Required Qualifications

Preferred Skills & "Edge" Expertise

Automotive Safety Standards:

Understanding of data integrity requirements for ISO 26262

or SOTIF

(Safety of the Intended Functionality).

How this role impacts our mission

In the AV world, the company with the best data loop wins. This role is not just about moving data; it’s about creating the mathematical and structural framework

that allows our engineers to find the "needle in the haystack"—the specific sensor frame that will help us solve the next great autonomous driving challenge.

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Architectural Innovation:

Lead the R&D and design of a next-generation data lakehouse that supports the unique requirements of ADAS/AV, including 4D spatial-temporal querying and multi-modal data fusion.

Deep Optimization:

Go beyond standard implementations of Apache Iceberg

to develop custom partitioning schemes, Z-ordering, and hidden indexing strategies tailored for LiDAR, radar, and video metadata.

Theoretical Leadership:

Apply advanced research in distributed systems to solve challenges regarding data consistency, deterministic "replay" of vehicle logs, and massive-scale data lineage.

Strategic Storage R&D:

Develop novel algorithms for data deduplication and "intelligent tiering," ensuring that rare "edge-case" driving data is preserved while optimizing the cost-to-performance ratio of the petabyte-scale lake.

Cross-Functional Research:

Partner with ML Research and Simulation teams to ensure the data architecture supports emerging paradigms like Foundation Models

and End-to-End Autonomous Driving

architectures.

Technical Mentorship:

Act as a high-level consultant and mentor to the broader Data Engineering organization, fostering an environment of analytical rigor and engineering excellence.

Education:

Master's or PHD in Computer Science, Database Systems

, or a related quantitative field.

Specialized Experience:

5+ years of experience in data systems, with a significant track record of designing large-scale distributed architectures.

Iceberg & Hadoop Internals:

Deep, "under-the-hood" knowledge of Apache Iceberg

(specification and implementation) and the Hadoop ecosystem

(HDFS, Spark, Trino/Presto).

Computational Foundations:

Expert-level understanding of query optimization, file format internals (Parquet/Avro), and the trade-offs of distributed consensus protocols.

Geospatial Mastery:

Experience with H3, S2, or other spatial indexing systems for high-frequency GPS and trajectory data.

Cloud Economics:

Proven ability to manage the financial architecture of massive cloud deployments (AWS/Azure/GCP).