SDE III - Machine Learning

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Skills you'll use

What you'll do

• Inference Platform Architecture: Architect low-latency recommendation inference systems, distributed vector search, feature store integrations, and real-time streaming updates.
• Evaluation Infrastructure: Build comprehensive offline and online evaluation pipelines, golden dataset testing tools, tool-call tracking, and automated regression suites.
• Agentic Core & New Initiatives: Lead the engineering execution for rapid 0→1 prototyping of AI shopping agent features—building flexible orchestration runtimes, tool registries, and gateway middleware.
• Technical Leadership: Partner with Applied Science, Product, and Data Platform teams to translate complex research concepts into clean, production-grade systems.
• System Architecture: Track record of designing, building, and scaling distributed ML systems and production serving pipelines.
• Deep ML Systems Knowledge: Experience with model optimization (quantization, TensorRT, torch.compile), vector search indexing, and real-time feature delivery.
• Agent & LLM Infrastructure: Understanding of LLM orchestration, agentic tool-use harnesses, and multimodal model serving.
• Technical Influence: Ability to mentor engineers, guide design reviews, and raise the engineering bar across teams.

Who can apply

• BTech or MTech in Computer Science, Machine Learning, or a related quantitative field.
• 6+ years of software engineering experience, with 5+ years dedicated to ML platforms, inference engines, or recommendation systems.
• Expertise in building high-throughput, low-latency recommendation inference, vector search, and feature serving infrastructure.
• Proficiency in Python or Java, and production ML tools across PyTorch, Triton, and distributed frameworks (Ray/Spark).
• Experience architecting automated evaluation harness suites and production monitoring for ML systems.
• Strong communication skills—able to simplify complex systems and align stakeholders across product and engineering.
• Self-directed, curious, and comfortable driving ambiguous 0→1 technical initiatives.

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