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About the Company Our client is an AI-native product company built to replace how billions of people manage their digital lives — starting with email, notes, and task tools that were never designed to be AI-native. It's backed by multi-million-dollar investment and built remote-first from day one, with a clear target: cut the time it takes users to get everyday things done by roughly 90%. That means solving the problems most AI products avoid — long-running workflows, persistent context, and reliable behavior under real-world, non-deterministic conditions. The team is small and high-talent-density by design, not by necessity, and moves at a pace that matches the scale of what it's building. About the Role As an Applied AI Engineer, you'll turn model capability into real product behavior. You'll own problems end-to-end — from shaping model behavior, to building the systems around it, to making sure it holds up in production. This role sits at the intersection of machine learning, systems, and product: making AI actually work for users, not just in demos. What You'll Work On Build and ship AI features end-to-end (model system user experience) Design and iterate on prompts, tools, memory, and agent workflows Turn raw model outputs into structured, reliable, predictable behavior Debug issues across the full stack — model, orchestration, infra, UX Optimize for latency, cost, and production reliability Develop lightweight evaluation frameworks to measure real-world performance Work closely with product and engineering to turn ambiguous problems into working systems Requirements Strong foundation in machine learning and modern neural network architectures Hands-on experience training, fine-tuning, or deploying ML models Ability to write clean, production-quality code Comfort working across abstraction layers — model, infra, product Strong problem-solving skills in ambiguous, fast-moving environments A bias toward shipping, iteration, and continuous improvement Tech Stack Python · PyTorch / JAX · LLMs (OpenAI-style APIs, LLaMA, Qwen, etc.) · inference/serving (e.g. vLLM) · vector DB What Success Looks Like ML models in production meet accuracy, latency, and reliability targets Production issues are found quickly, debugged effectively, and root-caused Data pipelines, training loops, and inference systems stay robust, reproducible, maintainable Effective collaboration with engineers, product, and research to ship reliable ML-powered features Iteration is driven by real-world signals and measurable improvement What to Expect The best products in the world are built by small, world-class teams. Decisions are made collectively, at rapid speed — balancing high-quality shipping with fast learning. You'll be expected to bring structure, exercise judgment, and execute independently. Compensation and benefits are competitive and vary by location; the package includes base salary and equity, discussed openly with you as part of the process. If there's a fit, expect 3–4 interviews total, followed by a prompt, transparent decision. Originally posted on Himalayas