Principal Applied Scientist, Hardware Silicon and Systems Group

Amazon; Asti; In loco; EUR 80.000 - 120.000; Tempo pieno

Descrizione del lavoro

A leading technology company in Italy seeks a Principal Applied Scientist in the Hardware Silicon and Systems Group. The role focuses on developing optimized ML models for consumer devices, influencing the future of AI capabilities. Candidates should have extensive experience in machine learning and a strong background in hardware optimization. This position offers an exciting opportunity to directly shape customer interactions with AI technologies across numerous devices.

Competenze

• 8+ years of experience in machine learning focused on model architecture design.
• Strong background in computer architecture and efficient inference algorithms.
• Hands-on experience with model compression techniques like pruning and distillation.

Mansioni

• Own the technical architecture for ML models across Amazon’s device ecosystem.
• Develop novel model architectures optimized for custom silicon.
• Create an evaluation framework for model efficiency.

Conoscenze

Machine learning

Model architecture design

Hardware optimization

Deep learning frameworks (e.g., TensorFlow, PyTorch)

Computer architecture

Formazione

Masters degree in Computer Science or Electrical Engineering

PhD in Computer Science or Electrical Engineering

Strumenti

TensorFlow

PyTorch

ONNX

Descrizione del lavoro

Principal Applied Scientist, Hardware Silicon and Systems Group

Hardware Silicon and Systems Group leads the development and optimization of on-device ML models for Amazon's hardware products, including audio, vision, and multi-modal AI features. We work at the critical intersection of ML innovation and silicon design, ensuring AI capabilities can run efficiently on resource-constrained devices.

Currently, we enable production ML models across multiple device families, including Echo, Ring/Blink, and other consumer devices. Our work directly impacts Amazon's customer experiences in the consumer AI device market. The solutions we develop determine which AI features can be offered on-device versus requiring cloud connectivity, ultimately shaping product capabilities and customer experience across Amazon's hardware portfolio.

This is a unique opportunity to help shape the future of AI in consumer devices at unprecedented scale. You'll be at the forefront of developing industry-first model architectures and compression techniques that will power AI features across millions of Amazon devices worldwide. Your innovations will directly enable new AI features that enhance how customers interact with Amazon products every day.

As Principal Applied Scientist you will blend expertise at the intersection of ML and hardware optimization for model training, build cutting-edge architectures for vision, language, and multi-modal tasks. Role requires a specialist in hardware-aware quantization, with hands‑on experience in model compression techniques like pruning and distillation. You will be responsible for computer architecture, ML accelerator designs, efficient inference algorithms and low‑precision arithmetic.

Key job responsibilities

• Own the technical architecture and optimization strategy for ML models deployed across Amazon's device ecosystem, from existing to yet‑to‑be‑shipped products.
• Develop novel model architectures optimized for our custom silicon, establishing new methodologies for model compression and quantization.
• Create an evaluation framework for model efficiency and implement multimodal optimization techniques that work across vision, language, and audio tasks.
• Define technical standards for model deployment and drive research initiatives in model efficiency to guide future silicon designs.
• Spend the majority of your time doing deep technical work - developing novel ML architectures, writing critical optimization code, and creating proof‑of‑concept implementations that demonstrate breakthrough efficiency gains.
• Influence architecture decisions impacting future silicon generations, establish standards for model optimization, and mentor others in advanced ML techniques.

Basic Qualifications

• Masters degree in Computer Science, Electrical Engineering, or a related technical field
• 8+ years of experience in machine learning, with a focus on model architecture design, optimization, and deployment
• Expertise in developing and deploying deep learning models for real‑world applications, including vision, language, and multi modal tasks
• Strong background in computer architecture, hardware acceleration, and efficient inference algorithms
• Hands‑on experience with model compression techniques such as pruning, quantization, and distillation
• Proficiency with deep learning frameworks like TensorFlow, PyTorch, or ONNX

Preferred Qualifications

• PhD in Computer Science, Electrical Engineering, or a related technical field
• 10+ years of experience in machine learning, with a track record of developing novel model architectures and optimization techniques
• Proven expertise in co‑designing ML models and hardware accelerators for efficient on‑device inference
• In‑depth understanding of the latest advancements in model compression, including techniques like knowledge distillation, network pruning, and hardware‑aware quantization
• Experience working on resource‑constrained embedded systems and deploying ML models on edge devices
• Demonstrated ability to influence technical strategy and mentor cross‑functional teams
• Strong communication skills and the ability to effectively present complex technical concepts to both technical and non‑technical stakeholders

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