---
title: 'Software Engineer, AI Infrastructure - LVM Inference & Evaluation at Ambient.ai'
canonical: 'https://feeny.ai/job/software-engineer-ai-infrastructure-lvm-inference-evaluation-ambient-ai-redwood-164ky4epwh0c'
type: 'job'
last_seen: '2026-09-17'
---

# Software Engineer, AI Infrastructure - LVM Inference & Evaluation at Ambient.ai

- **Company:** Ambient.ai
- **Location:** Redwood City, CA
- **Employment:** full-time
- **Work type:** hybrid
- **Posted:** 2026-09-10
- **Last confirmed live:** 2026-09-17
- **Apply:** https://jobs.ashbyhq.com/ambient.ai/e56b113b-69a1-4d38-8b9c-eb3460b9447e

## Job description

Build a safer world with us, one incident at a time. Ambient.ai is the category creator and leader in Agentic Physical Security. Powered by Ambient Pulsar, the first reasoning Vision-Language Model purpose-built for physical security, our platform seamlessly integrates with existing security cameras and physical access control systems to unify monitoring, access control, threat assessment, response, and investigations through an always-on reasoning layer that augments security operators with superhuman capabilities. The results: 95% fewer false alarms, investigations 20x faster, and 10x faster response. The momentum speaks for itself: we doubled new ARR in FY26, and have delivered results for world-class customers including Cisco, ServiceNow, SentinelOne, TikTok, Bayer, and MoMA. That kind of momentum creates an environment where great people thrive, and it shows: we recently ranked #71 out of 500 on the [Forbes best startup employers list](https://www.forbes.com/lists/americas-best-startup-employers/). Founded in 2017 and backed by Andreessen Horowitz, Y Combinator, and Allegion Ventures, Ambient.ai is on a fast-paced journey to fulfill our mission: prevent every security incident possible. Ready to learn more? Connect with us on [LinkedIn](https://www.linkedin.com/company/ambient-ai/?viewAsMember=true) and [YouTube](https://www.youtube.com/@ambient-ai-inc) About the role: Reporting to Raghu Nallamothu, you will design, build, and optimize the AI infrastructure that powers [Ambient.ai](http://Ambient.ai)’s real-time intelligence platform. In this role, you will work on the systems required to run state-of-the-art deep learning models across many terabytes of video data in real time. You will help build and scale infrastructure for inference, evaluation, and continuous model improvement across computer vision models, large language models, large vision models, and multimodal AI systems. This role is ideal for someone with a strong blend of infrastructure engineering, production ML systems, LLM/LVM inference, evaluation harnesses, and inference optimization experience. You will partner closely with research scientists and product engineering teams to bring the latest AI advancements into production for our customers. What you'll do:

- 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.

What you'll bring:

- 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 techniques, including batching, caching, quantization, parallelism, memory optimization, GPU utilization, and latency reduction.
- Experience with model-serving frameworks or systems 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, with the ability to work effectively with research scientists, product teams, infrastructure teams, and stakeholders.
- Proactive problem-solving ability, a strong ownership mindset, and adaptability to incorporate new AI technologies and methodologies.

## Nice to Have

- Experience operating large-scale GPU infrastructure or distributed inference systems.
- Experience with CUDA, NCCL, PyTorch, TensorRT, ONNX, or similar ML systems technologies.
- Experience with video understanding, real-time computer vision, multimodal AI, or physical-world AI systems.
- Experience with model compression, speculative decoding, distillation, pruning, or low-latency serving techniques.
- Experience with prompt evaluation, model regression testing, human-in-the-loop evaluation, or automated quality gates.
- Familiarity with retrieval-augmented generation, vector databases, embedding models, re-rankers, or search infrastructure.
- Experience building internal ML platforms or tools used by researchers and applied ML teams.

What Success Looks Like You will be successful in this role if you can build practical, scalable infrastructure that helps [Ambient.ai](http://Ambient.ai) deploy better AI models faster and more reliably. You should be comfortable working across the full stack of production AI systems, from model behavior and evaluation to serving architecture, GPU performance, observability, and customer-facing reliability. This is a hands-on engineering role for someone excited to help bring the next generation of AI, computer vision, LLMs, and LVMs into real-world production environments. Why join us:

- We are creating an entirely new category within a 180+ billion-dollar physical security industry and looking for team members who are also passionate about our mission to prevent every security incident possible
- We partner with an incredible customer roster of F500 companies, including Adobe, TikTok, Gap and SentinelOne
- Regular Full-time employees receive stock options for the opportunity to share ownership in the success of our company
- Comprehensive health + welfare package (Medical, Dental, Vision, Life, EAP, Legal Services, 401k plan)
- We offer flexible time off to rest and recharge, including Winter Break (time off between Christmas and New Year’s for most roles, depending on customer demand)
- The latest tech and awesome swag will be delivered to your door
- Enjoy a full range of opportunities to connect with your awesome co-workers
- We love to[hike](https://www.alltrails.com/us/california/redwood-city), are foodies, and love music! Check out our most recent[Ambient Spotify Playlist](https://open.spotify.com/playlist/2kx8dM5h65Tac5UOo0kuWr?si=cebFZmkHQhKEvnoall2rcQ)

We’ve found that in-person time meaningfully supports collaboration, creativity, and team alignment. Our talent, engineering, product, design, and marketing teams work from our Redwood City office three days a week. All other Bay Area employees join on Fridays to stay connected and close out the week together. Ready to learn more? Connect with us on[LinkedIn](https://www.linkedin.com/company/ambient-ai/?viewAsMember=true)|[YouTube](https://www.youtube.com/@ambient2599) #LI-Hybrid

Ambient.ai is proud to be an Equal Opportunity Employer.  Ambient does not unlawfully discriminate on the basis of race, color, religion, sex (including pregnancy, childbirth, breastfeeding, or related medical conditions), gender identity, gender expression, national origin, ancestry citizenship, age, physical or mental disability, legally protected medical condition, family care status, military or veteran status, marital status, registered domestic partner status, sexual orientation, genetic information, or any other basis protected by local, state, or federal laws. Ambient is an E-Verify participant.

## About Ambient.ai

## Company Overview
- **One-liner**: Ambient.ai provides an AI-powered agentic physical security platform that transforms existing camera and access control infrastructure into a unified intelligence layer to prevent security incidents in real time.
- **Entity Type**: Private (Series B)
- **Headquarters**: Redwood City, California, United States
- **Founded**: 2016
- **Founders**: Shikhar Shrestha (CEO) and Vikesh Khanna (CTO)

## Core Business
- **Primary industry**: AI-powered enterprise physical security (category creator of “Agentic Physical Security”)
- **Target customers**: Large enterprises with 1,000–100,000+ employees, including Fortune 500 companies, top U.S. technology firms, healthcare systems, educational institutions, data centers, and critical infrastructure operators. B2B, security teams (CSOs, CISOs, SOC managers).
- **Mission**: Prevent every security incident possible.

## Products & Services
- **Ambient Foundation (Base Platform)**: Always‑on situational awareness layer that unifies cameras, access systems, and sensors. Features Agentic Video Walls, Semantic Search (natural‑language video querying), multi‑site management, and PACS Visual Previews.
- **Ambient Advanced Forensics**: Compresses investigations from hours/days to seconds. Includes Similarity Search, License Plate Recognition, and Incident Timeline creation – up to 20× faster investigation speed.
- **Ambient Access Intelligence**: Agentic solution for access control monitoring. Links access control alarms with visual context to auto‑validate events, eliminating 90–95% of false alarms (DFO/DHO events) and saving up to $500K annually.
- **Ambient Threat Detection**: Real‑time threat analysis with 150+ validated threat signatures (perimeter breaches, tailgating, assault, brandished firearms, loitering, etc.). Contextual Threat Analysis Engine interprets intent; 90% of alerts resolved in under one minute.
- **Ambient Pulsar (Vision‑Language Model)**: The industry’s first always‑on, edge‑optimized reasoning VLM purpose‑built for physical security. Trained on 1M+ hours of ethically sourced enterprise video, NVIDIA‑accelerated, continuously learning.

## Market Standing
- **Valuation/Market Cap**: Not publicly disclosed.
- **Key Metric**: Total funding – Series B round led by Andreessen Horowitz (a16z) with strategic investment from Allegion Ventures (largest check Allegion has written). Also backed by Y Combinator, SV Angel, and individual investors including Jyoti Bansal (AppDynamics), George Kurtz (CrowdStrike), Frederic Kerrest (Okta), and Mark Leslie (Veritas).
- **Notable Investors/Partners**: a16z (led Series A & B), Allegion Ventures, Y Combinator, SV Angel.
- **Growth Signals**: 4×+ top‑line and customer growth since emerging from stealth in 2022. Used by most of the top 10 U.S. technology companies and multiple Fortune 500 organizations. Recognized as a Forbes Cloud 100 Rising Star, YC Top Company, and SIW Reader’s Choice Awards winner.

## Competitive Advantages
- **Category creator** of Agentic Physical Security – distinct from legacy video analytics or AI‑powered cameras.
- **Hybrid edge+cloud architecture**: processes video on‑premise (raw video never leaves customer environment) while providing centralized cloud SOC console; works with 200+ ONVIF‑compliant IP camera brands and integrates with major VMS/PACS providers (Genetec, Milestone, LenelS2, Honeywell Pro‑Watch).
- **Privacy‑by‑design**: no facial recognition, no PII stored, SOC 2 Type II certified, GDPR/CCPA‑aligned.
- **Purpose‑built VLM (Ambient Pulsar)** enables real‑time temporal reasoning and open‑set threat understanding at machine speed.
- **Proven outcomes**: 90–95% false alarm reduction, 20× faster investigations, up to $500K annual labor savings.

## Strategic Focus
- **Scale enterprise adoption**: expanding beyond top tech companies into healthcare, education, financial services, energy, manufacturing, defense, and government.
- **Deepen AI capabilities**: continuous improvement of Ambient Pulsar and expansion of threat signature library.
- **Agentic autonomy**: moving toward more autonomous response (escalation, dispatch, talkdown) while keeping humans in the loop.

## Why Work Here
Culture and benefits information sourced from [ambient.ai/careers](https://www.ambient.ai/careers):
- **Mission‑driven**: “If improving lives through technology is what gets you up in the morning” – team directly prevents school shootings, robberies, and asset theft.
- **Perks & benefits**: 95% medical premium coverage, 75% dental & vision, 401K (pre‑tax/Roth via Guideline), Flexible PTO, early stock option exercise via Employee Incentive Plan.
- **Culture**: Employee Resource Groups (ERGs), team outings (hiking, good food, celebrating with music), “We’re serious about our jobs—but we’re people too.”
- **Engineering culture**: Built on frontier AI (vision‑language models, edge computing); founders are Stanford AI researchers. Small but growing team (~140+ employees). Emphasis on innovation, autonomy, and real‑world impact.
- **Work model**: Not explicitly stated as remote/hybrid/office; careers page lists open roles globally. Redwood City HQ likely onsite or hybrid.

## Sources
1. [ambient.ai - Homepage](https://www.ambient.ai/)
2. [ambient.ai - Careers](https://www.ambient.ai/careers)
3. [ambient.ai - About](https://www.ambient.ai/about)
4. [ambient.ai - AI Info (structured data)](https://www.ambient.ai/ai-info)
5. [jobs.ashbyhq.com - Ambient.ai Job Board](https://jobs.ashbyhq.com/ambient.ai)

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