COMPANY A1
Technology
AppliedAIEngineer
Neural analysis suggests this role is
optimal for Mid+ candidates.
“Applied AI Engineer at COMPANY A1. Skills: Applied AI, Machine learning, Systems engineering, Product development. Build AI features. Ship AI features”
What You'll Achieve.
ML models meet targets; Production issues identified quickly; Root causes addressed; Data pipelines robust; Training loops robust; Inference systems robust; Deliver reliable ML features; Iterations driven by signals; Iterations driven by improvements
Industry & Context.
Problem-solving
What They're Looking For.
Must Have
Ability to write production-quality code, Comfort working across abstraction layers, Problem-solving skills in ambiguous environments
Nice to Have
Foundation in machine learning, Experience with model training, Experience with model fine-tuning, Experience with model deploying, Bias toward shipping, Bias toward iteration, Bias toward continuous improvement
What You'll Do.
Design agent workflows
Iterate on agent workflows
Turn model outputs into behaviors
Debug issues across stack
Optimize for reliability
Develop evaluation frameworks
Measure real-world performance
Work with engineering
Translate problems into systems
How You'll Work.
Team & Collaboration
Product teams; Engineering teams; Research teams
Full Job Description
COMPANY A1 is building a proactive AI smart assistant for everyday users to bring intelligence to conversations, errands, organising 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. 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 tar
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