7 часов назад
ML Software Engineer (Physical AI)
100 000 - 300 000$
Вакансия из Hirify Global, списка международных tech-компанийДля мэтча и отклика нужен Plus
Мэтч & Сопровод
Для мэтча с этой вакансией нужен Plus
Описание вакансии
Текст:
TL;DR
ML Software Engineer (Physical AI): Building multimodal data pipelines, foundation-model training and evaluation workflows, and production inference systems for autonomous ground vehicles with an accent on LLM, VLM, and VLA architectures, dataset scaling, and low-latency serving. Focus on integrating simulators for closed-loop evaluation, optimizing training and inference performance, and designing reliable, observable ML infrastructure from scratch.
Location: Remote, associated with San Francisco, United States
Salary: $100,000–$300,000 per year
Company
is developing an autonomous vehicle platform using physical AI to address challenges in modern freight and ground transportation.
What you will do
- Build multimodal data pipelines for ingesting, validating, sharding, and packaging datasets.
- Develop reproducible training and evaluation workflows, including manifests, checkpoints, and failure handling.
- Implement and iterate on LLM, VLM, and VLA architectures, including tokenization, inference runners, and output heads.
- Integrate simulators for closed-loop evaluation and create tools for metrics, visualization, and experiment management.
- Deliver deterministic, low-latency serving and inference tooling for bench, mule, and future vehicle deployments.
- Own systems end to end, from architecture and implementation through testing, documentation, and iteration.
Requirements
- MS in computer science, machine learning, or robotics, or a BS with at least 2 years of experience building ML, data, or evaluation systems.
- Strong Python fundamentals, including data structures, testing, debugging, modular design, and production-quality APIs.
- Experience building data and ML pipelines, evaluation tooling, and training or inference workflows with PyTorch, TensorFlow, or JAX.
- Experience packaging, sharding, and validating large multimodal datasets.
- Practical experience improving training or inference throughput and latency through techniques such as mixed precision, efficient batching, or model parallelism.
- Experience with cloud storage, containerization, CI/CD, experiment tracking, logging, and metrics.
Nice to have
- Experience with perception, detection, or multimodal models.
Culture & Benefits
- Full-time remote work associated with the San Francisco location.
- Work in a small, fast-moving stealth team founded by engineers who scaled autonomous driving systems.
- Close collaboration between software engineering, research, and product.
- Emphasis on ownership, independence, code quality, reliability, and observability.
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