Applied AI Engineer (ES, PT)
About this role
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
