Applied AI Engineer

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 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 About the Role Act AI is building a proactive AI system that understands context across conversations, plans actions, and carries work forward over time.

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 Python Py Torch / JAX Inference / serving (e. g. v LLM) 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 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.

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