Senior Applied AI Engineer (Multimodal Perception & Reasoning)

San Francisco Bay Area

Job Description

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.

VOLT is building the next generation of AI perception systems for the physical world, focused on safety, security, and real-time risk detection.

This role reports directly to the Head of Engineering and plays a critical role in advancing VOLT AI’s core perception platform.

Key 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

Required Qualifications

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

Preferred Qualifications

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

What Success Looks Like

Models ship reliably and improve measurable safety outcomes

Precision and recall improve while inference cost and latency decrease

Edge and cloud inference pipelines operate at production scale

Data and model iteration loops accelerate over time

AI perception becomes a durable competitive advantage for VOLT AI

At VOLT AI, you will build applied AI systems that run in the real world—on live video, in real environments, under real constraints. This role is for an engineer who wants to ship models, optimize them aggressively, and see their impact in production, not publish papers.

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