A1
AI Engineering
SeniorMachineLearningEngineer
Neural analysis suggests this role is
optimal for Senior candidates.
“Senior Machine Learning Engineer at A1. Skills: ML systems, Production, Reliability. Build core ML systems. Own work end-to-end”
What You'll Achieve.
ML models and systems meet targets; Production issues resolved; Pipelines robust and scalable; Measurable improvements in ML systems; Provides mentorship; ML features integrate seamlessly
Industry & Context.
Design practical solutions; Debug model failures; System issues
What They're Looking For.
Must Have
Built and shipped ML systems used by real users, Understand how modern ML models behave in production, Write strong, production-quality code, Think in systems, not scripts, Take ownership, Work independently, Push work across the finish line, Learn fast, Communicate clearly, Improve through iteration
Nice to Have
Experience with Python, Experience with PyTorch / JAX, Experience with GPU-based training and inference systems
What You'll Do.
Build core ML systems
Turn research ideas into working systems
Collaborate with research
Work under production constraints
How You'll Work.
Team & Collaboration
Collaborate closely with research, product, and engineering; Collaborate cross-functionally
Communication Scope
Communicate clearly
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 a Senior Member of Technical Staff, Machine Learning, you are an independent owner of critical ML subsystems in production. You take ambiguous problems, design practical solutions, and ship systems that operate reliably at scale. This is a hands-on, high-impact role focused on depth. FOCUS - Build core ML systems that power a proactive, long-horizon AI product. - Own work end-to-end: data preparation, training, evaluation, inference, and iteration. - Turn research ideas into working systems that run reliably in production. - Debug model failures and system issues using real production signals. - Iterate quickly: ship, measure outcomes, refine, and repeat. - Collaborate closely with research, product, and engineering to deliver real user impact. - Mentor and review work from other ML engineers through example and technical judgment. - Work under real production constraints: latency, cost, reliability, and safety TECH STACK - Python - PyTorch / JAX - GPU-based training and inference systems IDEAL EXPERIENCE - You have built and shipped ML systems used by real users. - You understand how modern ML models behave — and misbehave — in production. - You write strong, production-quality code and think in systems, not scripts. - You take ownership, work independently, and push work across the finish line. - You learn fast, communicate clearly, and improve through iteration. OUTCOMES - ML models and systems in production consistently meet accuracy, latency, reliability, and efficiency targets. - Complex production issues are monitored, debugged, and r
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