СШАTechnical Lead, Multimodal Research
3 нед. назад
СШАLeadOnsite
video processinglidarradargpumachine learning
Lead technical development for multimodal AI research and infrastructure.
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.
- Eventual 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.
Обязанности
- As Technical Lead, Multimodal Research, you’ll own the execution of our technical vision behind everything Eventual can understand about a video. Physical AI teams have video, lidar, radar, and sim outputs scattered across object stores with no way to find what they need without weeks of human annotation. Eventual runs vision/language models and pipelines over every clip in a corpus along axes the customer cares about (gripper type, failure mode, object class, scene, motion density), so a researcher can ask “left-arm grasp failures on deformable objects” and get a curated dataset in minutes.
- You’ll decide which models, representations, and evaluation methods get us there, and prove them in production at petabyte scale, over hundreds of thousands of hours of video. This is a senior individual contributor role rather than a management one: you set research direction and make the architectural decisions, while staying hands-on with papers, models, and experiments.
- Own modeling strategy across the platform rather than one customer’s taxonomy: which model families, representations, and training approaches we invest in, which get prototyped, and when to move off one.
- Take approaches from prototype into production inference at corpus scale, working with the data systems and storage teams on what the index and the loader require.
- Define the evaluation standard — the benchmarks and acceptance criteria any model meets before it reaches a customer.
- Own the cost curve for understanding: architecture-level decisions on distillation, cascades, routing, and quantization that keep a 10K-hour corpus at single-digit cents per hour of video.
- Translate customer research needs into scoped technical programs — taxonomy, model plan, datasets, quality instrumentation — and set the technical direction for multimodal work across the company. No direct reports.
Требования
- 5+ years in applied computer vision or multimodal ML.
- PhD or MS in computer science, electrical engineering, robotics, or applied mathematics with a computer vision or machine learning focus. A comparable publication or production record is acceptable in place of the degree.
- Depth in modern vision and multimodal modeling — VLMs, VQA, embeddings, representation learning, detection, tracking, segmentation, retrieval — with judgment about what is deployable today rather than competitive on a leaderboard.
- Hands-on training and evaluation of these models at scale on real video and sensor data, and comfort across the research and engineering boundary: PyTorch prototyping alongside inference performance, GPU utilization, throughput, and cost.
- Background from a perception or multimodal team at a self-driving, robotics, or Physical AI company, a frontier research lab, or a visual-data company, ideally as the senior-most person on that problem.
Будет плюсом
- Publications at CVPR, ICCV, ECCV, NeurIPS, ICML, or ICLR.
- Built or fine-tuned VLMs or other multimodal foundation models.
- Long-form video, temporal reasoning, embeddings, retrieval, or content-aware indexing at scale.
- Multimodal sensor data beyond RGB — lidar, radar, depth, or simulation output.
- Evaluation frameworks, labeling taxonomies, or large-scale annotation programs, or inference and training optimization across large GPU clusters.
Условия
- In-person, tight-knit team — 4 days/week in our SF Mission office.
- Competitive comp and meaningful startup equity.
- Catered lunches and dinners for SF employees.
- Commuter benefit.
- Team-building events and poker nights.
- Health, vision, and dental coverage.
- Flexible PTO.
- Latest Apple equipment.
- 401(k) plan with match.