About the role
About ActAI
There are over 5 billion users using basic applications today such email, notes, tasks, calendar and they're not AI-native. Our mission is to build proactive applications for anyone in the world, who are not used to complex prompting. We aim to bring intelligence to conversations, errands, organising and workflows, with minimal to no prompting.
Our product focuses on achieving high reliability for long-running workflows, persistent context, and real-world task completion. We believe products will greatly reduce hallucinations.
Our objective is to organise anyone's life, allowing us all to spend time on valuable and meaningful things.
Role
As an Applied AI Engineer, you will turn model capabilities into real product behavior. You will own problems end-to-end, from shaping model behavior, to building the systems around it, to ensuring it performs reliably in production.
This role sits at the intersection of machine learning, systems, and product, focusing on making AI actually work for users, not just in demos, but in real-world usage.
Focus
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, and predictable behaviors
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 translate ambiguous problems into working systems
Tech Stack
Python
PyTorch / JAX
LLMs (OpenAI-style APIs, LLaMA, Qwen, etc.)
Inference / serving (e.g. vLLM)
Vector DB
Ideal Experience
Strong foundation in machine learning and modern neural network architectures.
Hands-on experience with 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
Bias toward shipping, iteration, and continuous improvement
Outcomes
ML models in production meet expected accuracy, latency, and reliability targets.
Production issues are identified quickly, debugged effectively, and root causes addressed.
Data pipelines, training loops, and inference systems are robust, reproducible, and maintainable.
Collaborates effectively with engineers, product, and research teams to deliver reliable ML-powered features.
Iterations on models and systems are driven by real-world signals and measurable improvements.
How We Work
The best products today in the world were built by small, world class teams. We make decisions collectively, move at rapid speed, striking a balance between shipping high quality work and learning. Joining our team requires the ability to bring structure, exercise judgment, and execute independently. Our goal is to put in hands of our users a truly magical AI product.
Interview process
If there appears to be a fit, we'll reach to schedule 3, but no more than 4 interviews.
Applications are evaluated by our technical team members. Interviews will be conducted via virtual meetings and/or onsite.
We value transparency and efficiency, so expect a prompt decision. If you've demonstrated the exceptional skills and mindset we're looking for, we'll extend an offer to join us. This isn't just a job offer; it's an invitation to be part of a team that's bringing AI to have practical benefits to billions globally.
Originally posted on Himalayas