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Machine Learning Infrastructure Engineer

SpecterSan Francisco
On-site Full-time

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Experience Level

Entry Level

Qualifications

Preferred Qualifications:Strong experience with ML frameworks (e.g., PyTorch, TensorFlow). Proficiency in software development languages such as Python and C++. Experience with cloud platforms and services (AWS, Azure, Google Cloud) for deploying ML models. Familiarity with data pipeline technologies (e.g., Apache Kafka, Spark) for real-time data processing. Knowledge of computer vision algorithms and methodologies. Strong problem-solving skills and ability to work in a collaborative team environment.

About the job

To achieve this, we are developing a robust hardware-software ecosystem leveraging multi-modal wireless mesh sensing technology. This innovation allows us to significantly reduce the cost and time involved in sensor deployment by a factor of ten. Ultimately, our platform aims to serve as the perception engine for businesses, facilitating real-time visibility and autonomous management of their operational perimeters.

Our co-founders, Xerxes and Philip, are deeply committed to empowering our partners in the rapidly evolving landscape of physical AI and robotics. We are a dynamic, rapidly expanding team comprised of talent from Anduril, Tesla, Uber, and the U. S. Special Forces.

Position Overview
Specter is seeking a dedicated Machine Learning Infrastructure Engineer to construct and optimize the ML systems that drive real-time perception and inference capabilities across our edge-cloud platform. This position will involve overseeing the training, deployment, and enhancement of computer vision and sensor fusion models, aimed at enabling autonomous monitoring and decision-making for our clients' physical assets.

Key Responsibilities Include:

  • Design and implement scalable ML training pipelines for computer vision applications, including object detection, tracking, classification, and segmentation.

  • Develop efficient model serving infrastructures to facilitate real-time inference on edge devices with limited computational and power resources.

  • Optimize models for deployment on embedded hardware, employing techniques such as quantization, pruning, TensorRT, ONNX, and CoreML.

  • Create continuous training and evaluation systems to enhance model performance through feedback loops derived from production data.

  • Establish data pipelines for the ingestion, labeling, versioning, and management of extensive multi-modal sensor datasets, including video, radar, lidar, and thermal data.

  • Implement model monitoring frameworks, A/B testing methodologies, and performance analytics for deployed perception systems.

  • Collaborate with perception researchers to transition models from research environments to scalable production across thousands of edge nodes.

  • Construct tools and infrastructure for distributed training, hyperparameter optimization, and experiment tracking.

About Specter

Specter is at the forefront of creating a revolutionary software-defined control plane for physical environments, enhancing the way businesses perceive and manage their assets. Our innovative approach combines cutting-edge hardware and software solutions, enabling unprecedented efficiency and insight.

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