Software Engineer (ML Platform)

Job Description

Batch : 2018 & Earlier

People at Apple don’t just build products — they craft the kind of experience that has revolutionised entire industries. The diverse collection of our people and their ideas encourage innovation in everything we do. Imagine what you could do here! Join Apple, and help us leave the world better than we found it. At Apple, new ideas have a way of becoming extraordinary products, services, and customer experiences very quickly. Every single day, people do amazing things at Apple. The AML (Applied Machine Learning) Solution team build solution that impacts across Apple. This is a role of a Software engineer who will be responsible for the design, build and enhancements of a scalable highly concurrent distributed platform. Knowledge of popular Machine Learning tools and packages is helpful but can be an ongoing skill acquisition as part of the job. The mentality required and to be developed is how to process thousands of transactions per second, how to achieve the consistency without sacrificing the performance.

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Key Qualifications

• 5+ years of software development in Python and Go/Java/Rust
• Solid understanding of innovative DNN acceleration architectures and their key performance (compute/memory) tradeoffs
• Strong applied experience with compiler technology to work with CPU, GPU, and ML accelerators
• Working experience with Kubernetes and ML offering from AWS and GCP
• Experience designing and building CI/CD pipeline for Cloud native deployments with tools such as GitOps, Terraform, CloudFormation, Ansible, and Kubernetes toolset (e.g, kubectl, kustomize)
• Experience with Distributed Systems technologies (NoSql, Caching, Streaming)
• Experience with software benchmarking, performance analysis
• Experience in complexities of ML model serving (TorchServe, TensorFlow Serving, NVIDIA, Triton inference server, etc.)

Description

– Developing machine learning infrastructure that will be used by various teams for developing, evaluating and deploying machine learning models. – Develop and maintain large code base by writing readable, modular and well tested code. – Build and integrate end to end lifecycles of large-scale, distributed machine learning systems using the latest open source technologies – Build system for telemetry and tracing to support MLOps – Improve distributed cloud GPU training approaches for deep learning model

Education & Experience

BS in Computer Science or equivalent