COMPANY A1
AI Engineering
SeniorMachineLearningEngineer
Neural analysis suggests this role is
optimal for Senior candidates.
“Senior Machine Learning Engineer at COMPANY A1. Skills: Machine Learning, AI product. Build core ML systems. Own work end-to-end”
What You'll Achieve.
Meet accuracy targets; Meet latency targets; Meet reliability targets; Meet efficiency targets; Resolve production issues; Robust pipelines; Scalable pipelines; Maintainable pipelines; Drive measurable improvements; Provide mentorship; Raise ML engineering standard; Integrate ML features; Meet business goals
Industry & Context.
Debug model failures; Debug system issues; Problem solving
What They're Looking For.
Must Have
Built and shipped ML systems, Production-quality code, Systems thinking, Ownership, Independent work, Push work across finish line, Learn fast, Communicate clearly, Improve through iteration
Nice to Have
Understand ML model behavior in production, Understand ML model misbehavior in production
What You'll Do.
Build core ML systems
Turn research ideas into systems
Collaborate with research
Collaborate with product
Collaborate with engineering
Work under production constraints
How You'll Work.
Team & Collaboration
Cross-functional teams; Research; Product; Engineering
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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