Senior Software Engineer, AI Infrastructure - LVM Inference & Evaluation

Ambient Security

Design and optimize AI infrastructure powering Ambient.ai's real-time intelligence platform. Collaborate with teams on cutting-edge AI models across large-scale video data.

Posted 7/14/2026full-timeRedwood City • California • 🇺🇸 United StatesSenior💰 $168,000 - $205,000 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in building and optimizing AI infrastructure for real-time computer vision and multimodal inference workloads, with a strong focus on performance optimization and model evaluation. Proficient in deploying scalable machine learning systems and integrating advanced AI models into production environments.

Highest-signal resume keywords

Python ProgrammingMachine Learning InfrastructureInference Optimization TechniquesModel-Serving FrameworksCloud Infrastructure

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

AI Infrastructure DesignDeep Learning ModelsScalable Systems DevelopmentEvaluation FrameworksData Engine DesignModel Quality MeasurementBatching TechniquesCaching TechniquesQuantization TechniquesGPU Utilization

Soft Skills

CollaborationCommunicationProblem-SolvingOwnership Mindset

Tools & Technologies

VLLMTriton Inference ServerCloud InfrastructureContainersOrchestration

Industry Keywords

Computer VisionLLMLVMMultimodal AIDistributed Systems

Tech Stack

Tools & technologies

CloudDistributed SystemsPython

About the role

Key responsibilities & impact

Design, build, and maintain cutting-edge AI infrastructure for real-time computer vision, LLM, LVM, and multimodal inference workloads.
Build scalable systems for running state-of-the-art models across large volumes of video and sensor data.
Optimize inference performance across latency, throughput, GPU utilization, reliability, and cost.
Develop robust evaluation harnesses and benchmarking systems to measure model quality, system performance, regressions, and production readiness.
Build infrastructure for continuous model evaluation, experimentation, and deployment.
Partner with research scientists to productionize the latest advances in computer vision, LLMs, LVMs, RAG, and multimodal AI.
Improve model-serving architecture including batching, caching, routing, quantization, model parallelism, and hardware utilization.
Develop data engines and feedback loops for collecting training data, evaluating model behavior, and continuously improving AI performance.
Create reliable observability, monitoring, and debugging tools for production AI systems.
Help define best practices for deploying, evaluating, and operating AI systems in real-world enterprise environments.

Requirements

What you’ll need

4+ years of industry experience building infrastructure, distributed systems, machine learning platforms, or production AI systems.
BS/MS in Computer Science or a related technical field, or equivalent practical experience.
Strong programming background, especially in Python, with solid software engineering fundamentals.
Experience designing and building scalable machine learning infrastructure for training, inference, evaluation, and deployment.
Hands-on experience running deep learning models in production, ideally including LLMs, LVMs, vision-language models, or multimodal models.
Strong understanding of inference optimization techniques, including batching, caching, quantization, parallelism, memory optimization, GPU utilization, and latency reduction.
Experience with model-serving frameworks such as vLLM, Triton Inference Server or similar technologies.
Experience building evaluation frameworks, test harnesses, benchmarks, regression tests, or model-quality measurement systems.
Strong background in machine learning and deep learning; computer vision experience is a strong plus.
Experience designing data engines or pipelines for collecting, managing, and curating training and evaluation data.
Familiarity with integrating advanced AI systems such as LLMs, LVMs, RAG pipelines, embedding models, or multimodal models into production applications.
Experience with cloud infrastructure, containers, orchestration, distributed systems, and GPU-based workloads.
Strong collaboration and communication skills.
Proactive problem-solving ability, a strong ownership mindset.

Benefits

Comp & perks

Comprehensive health + welfare package (Medical, Dental, Vision, Life, EAP, Legal Services, 401k plan)
Flexible time off including Winter Break
Stock options
Opportunity to share ownership in company success
The latest tech and awesome swag delivered to your door