Software Engineer, AI Infrastructure – LVM Inference & Evaluation

Ambient Security

Software Engineer building inference, evaluation, and serving infrastructure for Ambient.ai’s physical security AI platform. Optimizing computer vision, LLM, and multimodal systems for real-time enterprise use.

Posted 9/11/2026full-timeRedwood City • California • 🇺🇸 United StatesJuniorMid-Level💰 $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 model evaluation, deployment, and performance optimization. Proficient in collaborating with cross-functional teams to enhance model-serving architectures and ensure production readiness.

Highest-signal resume keywords

Python ProgrammingMachine Learning InfrastructureInference OptimizationModel-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 DesignBatching and CachingQuantization TechniquesGPU UtilizationModel Quality MeasurementProduction AI Systems

Soft Skills

CollaborationCommunicationProblem-SolvingOwnership MindsetAdaptability

Tools & Technologies

VLLMTriton Inference ServerCUDAPyTorchTensorRTONNXContainersOrchestrationDistributed SystemsVideo Understanding

Industry Keywords

Computer VisionLLMsLVMsMultimodal AIRAG PipelinesEmbedding ModelsModel CompressionSpeculative DecodingPruningVector Databases

Tech Stack

Tools & technologies

CloudDistributed SystemsPythonPyTorch

About the role

Key responsibilities & impact

Design, build, and maintain AI infrastructure for real-time computer vision, LLM, LVM, and multimodal inference workloads
Build scalable systems for state-of-the-art models across large volumes of video and sensor data
Optimize inference for latency, throughput, GPU utilization, reliability, and cost
Develop evaluation harnesses and benchmarking systems for model quality, system performance, regressions, and production readiness
Build infrastructure for continuous model evaluation, experimentation, and deployment
Partner with research scientists to productionize advances in computer vision, LLMs, LVMs, RAG, and multimodal AI
Improve model-serving architecture through batching, caching, routing, quantization, model parallelism, and hardware utilization
Develop data engines and feedback loops for training data collection, model behavior evaluation, and continuous AI improvement
Create observability, monitoring, and debugging tools for production AI systems
Define best practices for deploying, evaluating, and operating AI systems in enterprise environments
Collaborate with research scientists, product engineering, infrastructure teams, and stakeholders

Requirements

What you’ll need

2+ 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, 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
Experience designing data engines or pipelines for training and evaluation data
Familiarity with LLMs, LVMs, RAG pipelines, embedding models, or multimodal models in production applications
Experience with cloud infrastructure, containers, orchestration, distributed systems, and GPU-based workloads
Nice to have: large-scale GPU infrastructure, CUDA, NCCL, PyTorch, TensorRT, ONNX, video understanding, model compression, speculative decoding, distillation, pruning, prompt evaluation, vector databases, re-rankers, search infrastructure, or internal ML platforms
Strong collaboration and communication skills
Proactive problem-solving ability, ownership mindset, and adaptability

Benefits

Comp & perks

Stock options for regular full-time employees
Comprehensive health and welfare package: Medical, Dental, Vision, Life, EAP, Legal Services, 401k plan
Flexible time off
Winter Break time off between Christmas and New Year's for most roles, depending on customer demand
Latest tech and swag delivered to your door
Opportunities to connect with co-workers
Hiking, food, and music activities