СШАSoftware Engineer, Multimodal Backend Systems
1 нед. назад
150k–250k USD / годСШАMiddleOnsite
pythondistributed systemsawsdockerkubernetestensorflowpytorch
Development of multimodal backend systems for Physical AI video data processing.
О компании
- From humanoid robots to autonomous vehicles, every Physical AI model is trained on petabytes of video, lidar, radar, and sensor data. Today's data platforms (Databricks, Snowflake) were built for spreadsheet-like analytics, not video corpora. And understanding that video still means paying a person to watch it, ten dollars an hour of footage at the low end. So teams check a sample and hope it represents the rest. The footage grows every year; the budget to look at it doesn't.
- was founded in 2022 to close that gap. Our open-source engine, Daft , is purpose-built for multimodal AI: 2 PB/day at Amazon, 60-100 PB at another FAANG company, and in production at companies like Mobileye, TogetherAI. On top of it we're building the infrastructure that finds any situation you can describe across a fleet's entire video history, and turns it into a training set or an alert someone can still act on. We fine-tune and run the vision models ourselves, which makes indexing every hour cheaper than annotating a sample.
- We're building this with the top Physical AI labs and GPU cloud providers. We've raised $30M from investors like Felicis, CRV, Y Combinator, and angels from the co-founders of Databricks and Perplexity. Our team comes from AWS, Lyft, and Tesla. We powered the last generation of Physical AI in self-driving; now we're doing it for the next.
- Join our small (but powerful!) team, 4 days/week in our SF Mission District office.
- Our goal is to build Scenario Mining and Data Curation for robot fleet data. We empower Physical AI and robotics teams to instantly find, curate, and stream the data they need to train frontier models.
- is an agile team where every engineer has high ownership across the stack from our compute infrastructure, to our data storage/querying layers and model training/deployment.
Обязанности
- As a Software Engineer working on our Multimodal Backend Systems, you will be responsible for building 's core products and architecture. You will ship features that will be immediately used by our customers and will work with a tight-knit team that values open communication and cross-functional collaboration. We move quickly to solve a wide range of complex technical and product challenges. While we are an experienced team that can provide constant guidance and mentorship, we value engineers who can autonomously scope and solve difficult technical challenges. Key Responsibilities:
- We are seeking engineers with deep expertise in at least one of the following core domains:
Требования
- We are looking for strong engineers who are problem-solvers at heart—combining excellent coding and architectural fundamentals in languages like Rust, C++, Python, or Go with a drive to reach for lower-level primitives when performance and efficiency demand it.
Условия
- In-person tight knit team with 4x a week in office
- Competitive comp and startup equity
- Catered lunches and dinners for SF employees
- Commuter benefit
- Team building events & poker nights
- Health, vision, and dental coverage
- Flexible PTO
- Latest Apple equipment
- 401k plan with match!
Другое
- 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.