A1
AI Engineering
AppliedAIEngineer
Neural analysis suggests this role is
optimal for Mid+ candidates.
“Applied AI Engineer at A1. Skills: Machine Learning, Systems Engineering, Product Development. Build and ship AI features. Design and iterate on prompts”
What You'll Achieve.
ML models in production meet targets; Production issues identified quickly; Production issues debugged effectively; Root causes addressed; Data pipelines robust; Training loops robust; Inference systems robust; Deliver reliable ML-powered features; Iterations driven by real-world signals; Iterations driven by measurable improvements
Industry & Context.
problem-solving skills in ambiguous, fast-moving environments
What They're Looking For.
Must Have
foundation in machine learning, modern neural network architectures, training ML models, fine-tuning ML models, deploying ML models, write clean, production-quality code, working across abstraction layers, problem-solving skills
Nice to Have
Hands-on experience with training, fine-tuning, or deploying ML models, Comfort working across abstraction layers (model → infra → product), problem-solving skills in ambiguous, fast-moving environments, Bias toward shipping, iteration, and continuous improvement
What You'll Do.
Build and ship AI features
Design and iterate on prompts
Turn raw model outputs
Debug issues across stack
Optimize for production reliability
Develop evaluation frameworks
Work closely with product
Work closely with engineering
How You'll Work.
Team & Collaboration
Collaborates effectively with engineers; Collaborates effectively with product; Collaborates effectively with 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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