Software Engineer, Multimodal Backend Systems

Eventual•San Francisco, CA

4d•Hybrid

About The Position

Eventual is building the infrastructure for Physical AI, focusing on scenario mining and data curation for robot fleet data. Our open-source engine, Daft, is designed for multimodal AI data, handling massive video, lidar, radar, and sensor data. We aim to empower Physical AI and robotics teams to instantly find, curate, and stream the data they need to train frontier models. As an agile team, every engineer has high ownership across the stack, from compute infrastructure to data storage, querying, and model training/deployment. This role involves building Eventual's core products and architecture, shipping features immediately used by customers, and working in a collaborative team environment that values open communication. While guidance and mentorship are available, we seek engineers who can autonomously solve complex technical challenges.

Requirements

Strong problem-solving skills.
Excellent coding and architectural fundamentals in languages like Rust, C++, Python, or Go.
Drive to reach for lower-level primitives when performance and efficiency demand it.

Responsibilities

Build our Streaming Architecture: Design, build, and optimize real-time WebRTC media pipelines and custom signaling mechanisms to stream multi-camera video feeds from robots to our platform.
Manage Hardware-Accelerated Video Pipelines: Integrate and tune video codecs (H.264, HEVC/H.265, AV1) leveraging GPU acceleration (NVENC/NVDEC) for hardware decoding and dynamic bitrate adaptation.
Scale Video Ingest & Storage: Engineer high-throughput video ingestion and distributed transcoding services that compress, index, and write petabyte-scale media corpora to object storage (AWS S3, GCS) for long-term retention and retrieval.
Build Spatial Data Pipelines: Construct specialized storage formats, spatial indexes, and processing workflows to ingest, align, and query dense 3D LiDAR point clouds, depth maps, and multi-camera spatial datasets.
Stream High-Frequency Telemetry at Scale: Develop high throughput ingestion pipelines capable of capturing, parsing, and storing real-time sensor streams and high-frequency robot state telemetry across our customers’ fleets.
Ensure Sensor-to-Video Synchronization: Coordinate time-sync protocols (PTP/NTP) across disparate sensor feeds to temporally align LiDAR point clouds, IMU telemetry, and video frames into unified data structures for downstream consumption.
Develop Data-Dense User Interfaces: Architect intuitive, high-performance web applications using React, TypeScript, and modern state management to handle continuous streams of data.
Visualize 3D Spatial & Video Streams: Build custom frontends to render 3D LiDAR point clouds, spatial bounding boxes, and multi-camera video feeds.
Build Agentic/AI-Native Product Workflows: Create agentic data-exploration, curation and search tools that empower robotics operators and AI researchers to review, annotate, and analyze complex physical AI datasets.