{"id":696723,"url":"https://alion.io/job/ambient-ai-software-engineer-ai-infrastructure-lvm-inference-evaluation","title":"Software Engineer, AI Infrastructure - LVM Inference & Evaluation","company":{"id":26316,"name":"Ambient AI","domain":"ambient.ai","url":"https://alion.io/company/ambient","size_band":"51-200","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Ashby","truth_index":{"grade":"A","score":91,"open_postings":8,"ghost_share":0,"stale_share":0.375,"repost_share":0,"time_to_fill_p50_days":58,"computed_at":"2026-10-28T05:59:10Z"}},"role":"Backend","role_family":"Backend","seniority":"junior","employment_type":"full_time","work_mode":"hybrid","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Redwood City, United States"],"countries":["US"],"hiring_countries":[],"hiring_countries_total":0,"salary":{"min":168000,"max":205000,"currency":"USD","period":"year","gross":null,"usd_annual":205000},"salary_estimate":null,"experience_years_min":2,"visa_sponsorship":false,"relocation_package":false,"has_equity":true,"technologies":[{"name":"Computer Vision","optional":false},{"name":"LLM","optional":false},{"name":"LLM Evaluation","optional":false},{"name":"Machine Learning","optional":false},{"name":"Multimodal AI","optional":false},{"name":"Python","optional":false},{"name":"Quantization","optional":false},{"name":"RAG","optional":false},{"name":"SentinelOne","optional":false},{"name":"ServiceNow","optional":false},{"name":"Triton Inference Server","optional":false},{"name":"vLLM","optional":false},{"name":"CUDA","optional":true},{"name":"Human-in-the-Loop","optional":true},{"name":"Knowledge Distillation","optional":true},{"name":"Model Distillation","optional":true},{"name":"NCCL","optional":true},{"name":"ONNX","optional":true},{"name":"PyTorch","optional":true},{"name":"Speculative Decoding","optional":true},{"name":"TensorRT","optional":true}],"status":"live","first_seen_at":"2026-09-10T23:31:37Z","employer_posted_date":"2026-09-10","last_verified_at":"2026-10-28T14:26:18Z","board_verified":true,"closed_at":null,"days_open":47,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":47},"description":"Build a safer world with us, one incident at a time.\nAmbient.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.\nThe 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.\nFounded 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.\nReady to learn more? Connect with us on LinkedIn and YouTube\nAbout the role:\nReporting to Raghu Nallamothu, you will design, build, and optimize the AI infrastructure that powers Ambient.ai ’s real-time intelligence platform.\nIn 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.\nThis 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.\nWhat you'll do:\nDesign, build, and maintain cutting-edge AI infrastructure for real-time computer vision, LLM, LVM, and multimodal inference workloads.\n\nBuild scalable systems for running state-of-the-art models across large volumes of video and sensor data.\n\nOptimize inference performance across latency, throughput, GPU utilization, reliability, and cost.\n\nDevelop robust evaluation harnesses and benchmarking systems to measure model quality, system performance, regressions, and production readiness.\n\nBuild infrastructure for continuous model evaluation, experimentation, and deployment.\n\nPartner with research scientists to productionize the latest advances in computer vision, LLMs, LVMs, RAG, and multimodal AI.\n\nImprove model-serving architecture, including batching, caching, routing, quantization, model parallelism, and hardware utilization.\n\nDevelop data engines and feedback loops for collecting training data, evaluating model behavior, and continuously improving AI performance.\n\nCreate reliable observability, monitoring, and debugging tools for production AI systems.\n\nHelp define best practices for deploying, evaluating, and operating AI systems in real-world enterprise environments.\n\nWhat you'll bring:\n2+ years of industry experience building infrastructure, distributed systems, machine learning platforms, or production AI systems.\n\nBS/MS in Computer Science or a related technical field, or equivalent practical experience.\n\nStrong programming background, especially in Python, with solid software engineering fundamentals.\n\nExperience designing and building scalable machine learning infrastructure for training, inference, evaluation, and deployment.\n\nHands-on experience running deep learning models in production, ideally including LLMs, LVMs, vision-language models, or multimodal models.\n\nStrong understanding of inference optimization techniques, including batching, caching, quantization, parallelism, memory optimization, GPU utilization, and latency reduction.\n\nExperience with model-serving frameworks or systems such as vLLM, Triton Inference Server or similar technologies.\n\nExperience building evaluation frameworks, test harnesses, benchmarks, regression tests, or model-quality measurement systems.\n\nStrong background in machine learning and deep learning; computer vision experience is a strong plus.\n\nExperience designing data engines or pipelines for collecting, managing, and curating training and evaluation data.\n\nFamiliarity with integrating advanced AI systems such as LLMs, LVMs, RAG pipelines, embedding models, or multimodal models into production applications.\n\nExperience with cloud infrastructure, containers, orchestration, distributed systems, and GPU-based workloads.\n\nStrong collaboration and communication skills, with the ability to work effectively with research scientists, product teams, infrastructure teams, and stakeholders.\n\nProactive problem-solving ability, a strong ownership mindset, and adaptability to incorporate new AI technologies and methodologies.\n\nNice to Have\nExperience operating large-scale GPU infrastructure or distributed inference systems.\n\nExperience with CUDA, NCCL, PyTorch, TensorRT, ONNX, or similar ML systems technologies.\n\nExperience with video understanding, real-time computer vision, multimodal AI, or physical-world AI systems.\n\nExperience with model compression, speculative decoding, distillation, pruning, or low-latency serving techniques.\n\nExperience with prompt evaluation, model regression testing, human-in-the-loop evaluation, or automated quality gates.\n\nFamiliarity with retrieval-augmented generation, vector databases, embedding models, re-rankers, or search infrastructure.\n\nExperience building internal ML platforms or tools used by researchers and applied ML teams.\n\nWhat Success Looks Like\nYou will be successful in this role if you can build practical, scalable infrastructure that helps 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.\nThis 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.\nWhy join us:\nWe 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\n\nWe partner with an incredible customer roster of F500 companies, including Adobe, TikTok, Gap and SentinelOne\n\nRegular Full-time employees receive stock options for the opportunity to share ownership in the success of our company\n\nComprehensive health + welfare package (Medical, Dental, Vision, Life, EAP, Legal Services, 401k plan)\n\nWe 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)\n\nThe latest tech and awesome swag will be delivered to your door\n\nEnjoy a full range of opportunities to connect with your awesome co-workers\n\nWe love tohike, are foodies, and love music! Check out our most recentAmbient Spotify Playlist\n\nWe’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.\nReady to learn more? Connect with us onLinkedIn | YouTube\nAmbient.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. 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