Staff Engineer, Machine Learning
$172,000–$236,500 year
On-site · Boston, Massachusetts, United States
Job Summary
The Role: The Algorithmic Solutions Group develops cutting-edge, efficient algorithms to bring intelligence to the physical world. We fuse state-of-the-art machine learning with deep domain expertise to convert raw physical data into actionable insights. The ideal candidate will be a Computer Architect or AI Researcher with deep silicon understanding, leading architecture-hardware co-design across algorithms and compute substrates to power next-generation intelligent systems. Responsibilities include defining strategic problems, leading architectural design to validated proof-of-concept, feasibility analysis, and driving system-level co-design from neural architectures to hardware accelerators. Qualifications emphasize PhD in AI hardware domains, 3+ years industry experience, hands-on RTL, publications, and experience mapping AI models to silicon while optimizing memory, dataflow, and power/area constraints. This role operates in a startup-like, experiment-focused environment in ADI's Boston Innovation Lab (The Analog Garage), with collaboration across hardware and software teams to solve high-impact problems in autonomous systems, robotics, and smart factories.
Required Qualifications
- PhD specialized in Computer Architecture or Integrated Circuit Design for AI workloads
- 3+ years of industry experience applying architectural principles to real-world engineering constraints
- Deep System-Level Hardware Expertise: understand organization of computation and data, memory hierarchies, on-chip networks, and execution models
- Edge AI & Model Optimization: hardware-aware deep learning, proficiency with PyTorch and/or JAX, capable of training or fine-tuning models to validate architectural hypotheses
- Hands-on RTL Experience: familiarity with Verilog/SystemVerilog or modern hardware construction languages (Chisel, PyMTL)
- Strong publication record in top conferences and/or journals
- Experience leading design of complex AI SoCs or validating novel architectures through rigorous simulation
- Ability to map computational graphs to silicon, orchestrating dataflow, tiling, and quantization to maximize arithmetic intensity within edge power budgets
- Proven ability to tape out a complex SoC or a custom AI accelerator
- DSP & Signal Fluency: Fourier transforms, noise floors, and sampling rates
- Experience with memory hierarchy, dataflow, and precision impact on system performance
- Experience in architecture/design for AI workloads and hardware-aware optimization
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