Senior Applied AI Engineer (Multimodal Perception & Reasoning)

Volt • San Francisco, CA

6d

About The Position

VOLT is building the next generation of AI perception systems for the physical world, focused on safety, security, and real-time risk detection.
We are seeking a Senior Applied AI & Machine Learning Engineer to design, optimize, and ship multimodal AI models that operate reliably in real-world environments. This is a deeply applied role, centered on taking models from data to production—across both edge devices and cloud infrastructure.
You will work on vision, video, and language-based models that understand real-world scenes and events, and you will be accountable for their accuracy, latency, robustness, and cost in production systems.
This role reports directly to the Head of Engineering and plays a critical role in advancing VOLT AI’s core perception platform.

Requirements

• 8+ years of experience in applied machine learning or AI systems
• Strong hands-on experience with vision, video, or multimodal models
• Proven experience taking models into production, not just research prototypes
• Deep understanding of model optimization (quantization, pruning, performance tuning)
• Proficiency in Python and modern ML frameworks (e.g., PyTorch)
• Experience evaluating models using real-world metrics and constraints
• Ability to operate independently and own complex technical systems end to end

Nice To Haves

• Experience with multimodal or vision-language models (CLIP-like, BLIP-like, or custom)
• Experience deploying models to edge or resource-constrained environments
• Familiarity with inference optimization stacks (ONNX, TensorRT, CUDA)
• Experience working on physical-world perception systems (video, sensors, environments)
• Background in safety, security, robotics, or autonomous systems
• Experience mentoring senior engineers or providing technical leadership

Responsibilities

• Build, fine-tune, and deploy production-grade multimodal models for safety and security applications, with a focus on visual and video perception, language-assisted and multimodal reasoning, and temporal understanding of real-world environments
• Own the full applied ML lifecycle, including data collection, labeling strategies, and dataset curation, model fine-tuning, evaluation, and iteration, and deployment, monitoring, and continuous improvement in production
• Drive model performance in real-world conditions, optimizing for high precision and recall, low false positives and false negatives, and robustness to noise, lighting changes, occlusion, and domain shift
• Optimize models for edge and cloud deployment, including quantization, pruning, and model compression, latency, throughput, and memory optimization, and hardware-aware tuning for GPUs and edge accelerators
• Build and maintain training and inference pipelines that support scalable experimentation and evaluation, reproducibility and model versioning, and reliable production deployment
• Collaborate closely with infrastructure and systems engineers to integrate models into real-time perception pipelines, balance accuracy, performance, and cost constraints, and diagnose and resolve production inference issues
• Use real-world deployment feedback and metrics to drive data and model improvements

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level
• Education Level: No Education Listed