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Specter

ML Research Engineer

Reposted 5 Days Ago
In-Office
San Francisco, CA, USA
5-5 Annually
Senior level
In-Office
San Francisco, CA, USA
5-5 Annually
Senior level
The ML Research Engineer will implement and deploy deep-learning models for perception AI, manage data pipelines, and design user interfaces.
The summary above was generated by AI

Company Background:

Specter is creating a software-defined “control plane” for the physical world. We are starting with protecting American businesses by granting them ubiquitous perception over their physical assets.

To do so, we are creating a connected hardware-software ecosystem on top of multi-modal wireless mesh sensing technology. This allows us to drive down the cost and time of deploying sensors by 10x. Our platform will ultimately become the perception engine for a company’s physical footprint, enabling real-time perimeter visibility and autonomous operations management.

Our co-founders Xerxes and Philip are passionate about empowering our partners in the fast approaching world of physical AI and robotics. We are a small, fast growing team who hail from Anduril, Tesla, Uber, and the U.S. Special Forces.

Role + Responsibilities:

Specter is hiring a perception AI engineer responsible for turning sensor data pipelines into actionable insights for our customers.

Responsibilities include:

  • Implementing and deploying a variety of deep-learning based vision, vision-language, and large language models to our world-class distributed perception system

  • Building and scaling a production-grade data-collection, labelling, and model re-training platform

  • Driving the design behind a multimodal software user interface

Qualifications:

  • 5+ years of experience training, implementing, and deploying deep-learning based computer vision models in tasks such as object detection, semantic segmentation, object tracking, etc. (both single and multi-frame) in frameworks such as PyTorch, TensorRT, and ONNX

  • Experience fine-tuning, implementing, and deploying vision-language models and large language models in frameworks such as PyTorch, TensorRT-LLM, and ONNX

  • Experience optimizing model runtimes utilizing techniques such as quantization, pruning, low-rank adaptation, etc. where appropriate

  • Experience building production-grade RAG pipelines, and scaling vector databases in production

  • Strong experience in C++/Rust development in embedded systems and knowledge of Linux fundamentals

  • Strong knowledge of CUDA fundamentals

  • Experience with image/video processing, filtering, and enhancement. Knowledge of various video codecs desirable.

  • Experience with variety of sensor types such as EO and IR cameras

  • Familiarity with Rust (or ability to come up the curve quickly!)

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