Software Engineer, AI Engineer (Applied​/Software), Machine Learning​/ ML Engineer

We are a well-funded, early-stage AI research and product lab focused on building the next generation of frontier multimodal AI models. Founded by former Deep Mind researchers—including Andrew Dai, previously an engineering leader on Gemini—our mission is to push the frontier of visual and multimodal intelligence. Our first model, Glimpse, launched publicly in October 2026.

Our team currently consists of 25 world-class scientists and engineers. We raised a $55M seed round led by Altimeter Capital, Menlo Ventures, Striker VC, and NVIDIA. We are tackling foundational problems in artificial intelligence and translating cutting-edge multimodal research into scalable, real-world systems.

As a Software Engineer – Applications
, you will build the engineering bridge between our core multimodal research and real-world production deployments. This is an engineering-first, high-impact role where you will design and implement reference architectures, production-grade integration frameworks, developer SDKs, evaluation harnesses, and bespoke end-to-end applications powered by Glimpse.

You will sit at the intersection of product engineering, model evaluation, and customer architecture. Working directly with founders, early enterprise partners, and our internal research team, you will turn complex multimodal requirements into robust, high-performance systems and feed real-world constraints directly into our model roadmaps.

• Architect & Build Production Integrations: Design, build, and deploy low-latency, scalable application architectures integrating Glimpse into complex enterprise data pipelines and high-throughput workflows.

• Develop Reference Systems & Tooling: Create open-source reference implementations, production-ready demo applications, developer tooling, and SDK components that establish the standard for building on Glimpse.

• Build Evaluation & Benchmarking Infrastructure: Design and implement rigorous evaluation pipelines and benchmarking harnesses to systematically test Glimpse against other frontier multimodal models on diverse, real-world visual datasets.

• Debug Across the Stack: Systematically isolate and resolve bottlenecks across application pipelines, data ingestion layers, model inference APIs, and prompt/orchestration logic.

• Partner Technically with Early Adopters: Serve as the core technical architect for engineering teams at design-partner companies, guiding technical discovery, proof-of-concept development, and deployment to production.

• Influence Model & Platform Roadmaps: Translate real-world application failure modes, edge cases, and performance requirements into prioritized specifications for our core research and model infrastructure teams.

• Strong Core Engineering: 5+ years of experience building and shipping high-performance, production software in Python (and ideally Type Script or Go/Rust). You write clean, maintainable code and can speak deeply to your architectural decisions.

• Hands‑on LLM/Multimodal Application Engineering: Experience building complex systems beyond basic API calls—such as agentic workflows, structured tool‑use frameworks, model evaluation pipelines, or orchestration layers for vision‑language models.

• Model Understanding & Evaluation: Hands‑on experience evaluating, fine‑tuning, or benchmarking ML models. You can systematically differentiate between model failure modes, pipeline errors, and data‑quality issues.

• Full‑Lifecycle Ownership: A builder mindset with a bias for shipping quickly. Experience as a founding engineer, startup core contributor, or forward‑deployed engineer is highly valued.

• Systems & Data Architecture: Familiarity with modern cloud architecture, API design, vector/multimodal indexing,…