Applied AI Engineer

About A1

A1 is building a proactive AI smart assistant for everyday users to bring intelligence to conversations, errands, organizing, and workflows. Our product focuses on achieving high reliability for long-running workflows, persistent context, and real-world task completion. The system must handle multi-step reasoning, interact with external tools, and remain reliable despite non-deterministic model behavior.

The Role: Applied AI Engineer

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 Areas

• 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.

Expected 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 a truly magical AI product in the hands of our users.

Interview Process

If there appears to be a fit, we'll reach out to schedule 3, but no more than 4 interviews. 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.

Note: Please mention the word IDOLIZED and tag #RMjYwMzo4MDAzOjQyMDA6NTY0MDplNTk5OjkxOTk6ZmIwZTphYjNm when applying.