Software Engineer II - Fleet Enablement & Insights (Annotation Platform)

About this role

About the Company

At Torc, we have always believed that autonomous vehicle technology will transform how we travel, move freight, and do business. A leader in autonomous driving since 2007, Torc has spent over a decade commercializing our solutions with experienced partners.  Now a part of the Daimler family , we are focused solely on developing software for automated trucks to transform how the world moves freight. Join us and catapult your career with the company that helped pioneer autonomous technology, and the first AV software company with the vision to partner directly with a truck manufacturer.

Meet the Team

The Software Engineer II will be a core member of the Fleet Enablement & Insights team, building Torc's in-house annotation platform: the web-based tooling that turns multi-sensor autonomy data into the labeled datasets that train and validate the autonomous truck platform. This role develops the interactive 2D/3D annotation editor and the services behind it which fuses HD map data and multi-camera, lidar, and other sensor context into the labeling workflow. The platform also serves adjacent use cases across the data organization, including data QC and scene-selection review. The ideal candidate is a collaborative engineer who thrives in a fast-paced environment and is passionate about high-performance visualization of large sensor datasets, thoughtful annotator-facing UX, and the pipelines that connect human review to machine learning at scale. This position offers the opportunity to work at the intersection of software engineering, computer graphics, and machine learning with a direct line of sight to the data that trains and validates the AV stack.

What You’ll Do

• Design, develop, and maintain the TypeScript/React web application for 2D and 3D annotation, including cuboid and polygon editing, cross-frame interpolation and track propagation, attribute editing, and review-first (accept/reject) workflows.

• Build high-performance point cloud and image rendering with three.js/WebGL: octree/LOD-based streaming formats, predictive prefetching for smooth frame scrubbing, camera-LiDAR projection, and multi-sensor overlays across a high camera count.

• Design and build the services behind the editor: label storage and versioning, task assignment and QA workflow, authentication, and multi-user isolation guardrails.

• Integrate pre-labeling and pseudo-labeling pipeline outputs into the annotation workflow, and instrument acceptance-rate and throughput metrics that drive the auto-labeling feedback loop.

• Fuse HD map data into annotation and QC workflows as priors and reference layers for labeling and validation.

• Build data converters and ingestion paths from Torc's multi-sensor scene data (multiple LiDARs, many cameras, calibration data) into the platform's formats.

• Deliver dataset exports compatible with downstream ML training and validation consumers, with the lineage and auditability the safety case requires.

• Leverage AWS cloud services and Databricks adjacency to host scene data and deploy scalable, reliable services.

• Collaborate closely with the Data Annotation team (the platform's primary users), Autonomy/ML, Scene Selection, Mapping, and Data Engineering to align the tool with real annotator workflows and downstream requirements.

• Participate in agile ceremonies, sprint planning, and weekly demos with annotators and stakeholders to keep the tool grounded in real use.

• Contribute to a culture of engineering excellence through code reviews, documentation, and knowledge sharing, including hardening prototype code into production systems.

• Identify and address technical debt, performance bottlenecks, and reliability gaps across the annotation platform.

What You’ll Need to Succeed

• Strong proficiency in TypeScript and React, with experience building and shipping production-quality web applications.

• Experience with browser-based 3D graphics (three.js, WebGL, or similar), or strong graphics fundamentals and a demonstrated ability to ramp quickly.

• Proficiency in Python for building production backend services and APIs.

• Experience working with large binary or sensor datasets (point clouds, imagery, video) and optimizing data-heavy user interfaces for performance.

• Strong proficiency with SQL and hands-on experience with PostgreSQL for label, task, and metadata storage.

• Experience with AWS services for hosting data and deploying services.

• Proficiency with Git and GitHub for version control, branching strategies, and collaborative code workflows.

• Familiarity with JIRA or similar project management tools for tracking tasks, bugs, and sprint deliverables.

• Solid understanding of software engineering fundamentals including data structures, algorithms, and system design.

• Strong written and verbal communication skills with the ability to effectively collaborate across technical and non-technical stakeholders, including non-engineer users of the tools you build.

• Bachelor's degree in Computer Science, Software Engineering, or a related technical field or equivalent experience.

Bonus Points

• Experience with point cloud rendering and streaming formats (octree/LOD structures such as Potree or COPC, 3D Tiles, LAS/LAZ) or large-scale spatial data structures.

• Experience building or extending annotation/labeling tools.

• Familiarity with robotics data and visualization ecosystems (e.g., Rerun, Foxglove, MCAP, ROS bags).

• Background in machine learning workflows for perception: object detection, tracking, segmentation, model-assisted or auto-labeling, active learning.

• Understanding of multi-view geometry and sensor calibration (camera intrinsics/extrinsics, LiDAR-camera projection, time synchronization).

• Prior experience working with High-Definition (HD) maps, map formats (e.g., OpenDRIVE, NDS, Lanelet2), or fusing map data into perception or labeling workflows.

• Familiarity with PostGIS or spatial queries for geometry operatio

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