Lead the intelligence layer for a real-time voice AI agent: design and optimize model orchestration, prompt engineering and evals, build agentic workflow orchestration, ensure LLM reliability and regression testing, and select/optimize speech and LLM models balancing quality, latency, and cost.
About Luminade
We're building the conversational voice layer for how people work. People have found dictation useful for input and text-to-speech useful for output. We're going further: a single conversation you never have to step out of, where you get responses back and actually get things done.
We started with email, the place where most work actually lives. Now we're expanding to calendar, documents, and more, so our AI has complete context on what matters in a person's day. The goal is simple: let people work without being hostage to a screen.
We're building first for people with vision impairment, ADHD, and dyslexia, communities where this kind of interface isn't a nice-to-have. But this is not a niche product. The same way curb cuts were designed for wheelchair users and ended up benefiting everyone, we're using accessibility as the design constraint that forces us to build something genuinely better. The endgame is hundreds of millions of sighted users who want to stay productive during their commute, away from a screen, or simply in flow.
The TeamOur CEO, Sriram, is an engineer and entrepreneur (1 exit) who started this company because of his own experience with vision loss. He knows this problem from the inside. Our CTO, Mikhail, is a former Google engineer and four-time world champion in competitive skydiving. He brings the same precision and intensity to systems architecture that he brings to everything else.
We're backed by South Park Commons, have raised $2M, and work in-person in San Francisco.
What You'll OwnThe quality of our AI agent IS the product. You'll own the intelligence layer — this isn't a research role. This is the person who makes the agent reliable, fast, and smart enough that users trust it with their actual work.
The voice agent pipeline. Designing and optimizing the full model orchestration chain for real-time, low-latency conversational interactions. You'll decide which models to use, how to stitch them together, and how to make the whole thing feel instant.
Prompt engineering and evals. Crafting, testing, and iterating on prompts across the product. Building eval frameworks that catch regressions before users do.
Agentic workflow orchestration. Building the multi-step reasoning and action-taking capabilities that let users manage email, calendar, and documents through natural conversation. Context management, memory, and knowing when the agent should act versus ask.
LLM reliability and regression testing. LLMs are nondeterministic. You'll build the systems that ensure consistent, high-quality responses across thousands of user interactions.
Model selection and optimization. Evaluating and integrating the right LLMs, speech-to-text, text-to-speech, or speech-to-speech models. Making hard tradeoffs between quality, latency, and cost at every layer of the stack.
What We're Looking ForYou've worked deeply with AI/ML in production and felt the pain of making these systems reliable at scale. You've shipped LLM-powered applications where model output goes directly to humans, not dashboards. Ideally you've built real-time or voice AI systems — but more than any specific experience, you take initiative, you ship, and you think about the person on the other side of every interaction.
CompensationThe base pay range for this role is $150,000 – $250,000 per year.
Similar Jobs
Angel or VC Firm • Artificial Intelligence
Design, build, and operate end-to-end training and inference infrastructure for large language and multimodal models. Improve efficiency (memory, parallelism, kernel optimizations), ensure robust scalable training and RL pipelines, optimize low-latency/high-throughput serving (quantization, caching, speculative decoding), manage multi-GPU and multi-cloud orchestration, and productionize new algorithms with strong observability and reproducibility.
Top Skills:
C++CachingCudaDeepspeedFeature StoresFlashattentionGoGrafanaKubernetesMegatronOpentelemetryPrometheusPulumiPythonPyTorchQuantizationRayRustSglangSpeculative DecodingTerraformTgiTritonVector DatabasesVllm
Artificial Intelligence • Software • PropTech • Automation
Lead development of production AI systems for building-system design including vision pipelines for PDF/CAD parsing, LLM+RAG workflows for code-compliant designs, AI agent orchestration, and full-stack features across a Typescript/React/Node/Postgres platform. Significant ownership and technical direction at an early-stage startup.
Top Skills:
EmbeddingsExpressInstance SegmentationLlmMcpMulti-Agent ReasoningNode.jsNumpyObject DetectionPostgresPythonPyTorchRagReactSemantic SegmentationTypescriptVector DatabaseWorkflow Orchestration
Artificial Intelligence • Software • Industrial • Manufacturing
Design, train, evaluate, and optimize agent-native LLMs and RAG pipelines for enterprise use. Build training and RL pipelines (RLHF/DPO/PPO), embedding-based memory, evaluation harnesses, observability, and inference optimization across cloud and on-prem environments.
Top Skills:
ChromaCohereDeepspeedDelta LakeDuckdbDustFaissFalconFlashattentionFsdpHuggingface TransformersIcebergJavaScriptJinaKubernetesLambdalabsLangchainLanggraphLangsmithLlama 3LlamaindexLoraMistralMixtralModalNeo4JOpenllm EvalsOwlParquetPineconePostgresPuppygraphPythonQdrantQloraRagasRayRdfRustSagemakerTgiTrulensVllmWeaviateWeights & Biases
What you need to know about the San Francisco Tech Scene
San Francisco and the surrounding Bay Area attracts more startup funding than any other region in the world. Home to Stanford University and UC Berkeley, leading VC firms and several of the world’s most valuable companies, the Bay Area is the place to go for anyone looking to make it big in the tech industry. That said, San Francisco has a lot to offer beyond technology thanks to a thriving art and music scene, excellent food and a short drive to several of the country’s most beautiful recreational areas.
Key Facts About San Francisco Tech
- Number of Tech Workers: 365,500; 13.9% of overall workforce (2024 CompTIA survey)
- Major Tech Employers: Google, Apple, Salesforce, Meta
- Key Industries: Artificial intelligence, cloud computing, fintech, consumer technology, software
- Funding Landscape: $50.5 billion in venture capital funding in 2024 (Pitchbook)
- Notable Investors: Sequoia Capital, Andreessen Horowitz, Bessemer Venture Partners, Greylock Partners, Khosla Ventures, Kleiner Perkins
- Research Centers and Universities: Stanford University; University of California, Berkeley; University of San Francisco; Santa Clara University; Ames Research Center; Center for AI Safety; California Institute for Regenerative Medicine
