ZipRecruiter

Log In

1

Compact Device Modeling Jobs in California (NOW HIRING)

Ayar Labs

Sr. Engineer, Research Software

San Jose, CA · On-site

$170K - $192K/yr

Working knowledge of at least one of: photonic device physics, circuits, signal processing, or statistical analysis. * Prior work on compact models, SPICE-like simulators, or link/yield statistical ...

Ayar Labs

Sr. Engineer, Research Software

San Jose, CA

$170K - $192K/yr

Working knowledge of at least one of: photonic device physics, circuits, signal processing, or statistical analysis. * Prior work on compact models, SPICE-like simulators, or link/yield statistical ...

Apple

Biomedical Research Engineer

Cupertino, CA · On-site

... in a compact form-factor, thus enabling the next-generation of optical health sensors. The ... The candidate must be able to employ simulation tools to develop models of sensor behavior. Lastly ...

Apple

Biomedical Research Engineer

Cupertino, CA · On-site

... in a compact form-factor, thus enabling the next-generation of optical health sensors. The ... The candidate must be able to employ simulation tools to develop models of sensor behavior. Lastly ...

Arbor Energy

Sr. Heat Exchanger Engineer

El Segundo, CA · On-site

$165K - $180K/yr

Lead in-house design and development of advanced compact heat exchangers, including printed circuit ... models to test data. * Collaborate closely with thermodynamic cycle analysts, mechanical systems ...

Hark

Product Design Engineer, FEA

San Jose, CA · On-site

$120K - $300K/yr

To get there, we're developing multimodal models and next-generation AI hardware together ... hardware, aerospace, medical device, or precision manufacturing environment * Expert-level ...

Hark

Product Design Engineer, FEA

San Jose, CA · On-site

$120K - $300K/yr

To get there, we're developing multimodal models and next-generation AI hardware together ... hardware, aerospace, medical device, or precision manufacturing environment * Expert-level ...

Apple

Biomedical Research Engineer

Cupertino, CA

$184K - $324K/yr

... in a compact form-factor, thus enabling the next-generation of optical health sensors. The ... The candidate must be able to employ simulation tools to develop models of sensor behavior. Lastly ...

previous pagenext page

Showing results 21-40

All JobsCompact Device Modeling JobsCompact Device Modeling Jobs in California

Compact Device Modeling information

What are the key skills and qualifications needed to thrive as a compact device modeling engineer, and why are they important?

To thrive as a Compact Device Modeling Engineer, you need a strong background in semiconductor physics, device modeling, and typically an advanced degree in electrical engineering or a related field. Proficiency with simulation tools like SPICE, TCAD software, and programming languages such as Python or MATLAB, as well as knowledge of industry-standard modeling languages (e.g., Verilog-A), is essential. Strong analytical thinking, attention to detail, and effective communication skills help you collaborate with cross-functional teams and interpret complex data. These skills are crucial for developing accurate models that drive innovation and reliability in semiconductor device design.

What types of teams or departments does a compact device modeling engineer typically collaborate with?

Compact Device Modeling engineers often work closely with circuit design teams, process technology groups, and EDA (Electronic Design Automation) tool developers. Collaboration is essential because accurate device models are critical for reliable circuit simulations and successful chip fabrication. Regular communication with these departments ensures that the models reflect real-world device behavior and are compatible with evolving design requirements and manufacturing technologies. This cross-functional teamwork provides valuable exposure to multiple aspects of semiconductor development and can open doors for broader career growth.

What is compact device modeling?

Compact device modeling is the process of creating simplified mathematical models that accurately represent the electrical behavior of semiconductor devices, such as transistors, within electronic circuits. These models are essential for circuit simulation tools, enabling engineers to predict circuit performance without resorting to complex, time-consuming physical simulations. Compact models balance accuracy and computational efficiency, making them a cornerstone in the design and verification of integrated circuits.

What is the difference between Compact Device Modeling vs Semiconductor Device Engineer?

AspectCompact Device ModelingSemiconductor Device Engineer
CredentialsTypically requires engineering degree, specialized modeling certificationsRequires engineering degree, often with additional certifications in device physics
Work EnvironmentResearch labs, simulation centers, R&D departmentsDesign labs, manufacturing facilities, R&D teams
Industry UsageUsed for device simulation, circuit design, and performance predictionInvolved in device development, fabrication, and testing

Compact Device Modeling focuses on creating simplified models of semiconductor devices for simulation purposes, aiding circuit design. Semiconductor Device Engineers work on designing, developing, and testing actual semiconductor devices. While both roles require engineering expertise and involve semiconductor technology, modeling is more simulation-oriented, whereas engineering involves hands-on device development.

What are popular job titles related to Compact Device Modeling jobs in California? For Compact Device Modeling jobs in California, the most frequently searched job titles are:
What job categories do people searching Compact Device Modeling jobs in California look for? The top searched job categories for Compact Device Modeling jobs in California are:
What cities in California are hiring for Compact Device Modeling jobs? Cities in California with the most Compact Device Modeling job openings:
Compact Device Modeling jobs near you

Senior Staff / Principal Machine Learning Scientist, AI Inference & Optimization

Netskope

Santa Clara, CA • On-site

Other

Posted 23 days ago

Job description

Positions are available at Senior Staff and above. Candidates are assessed individually and leveled according to their specific skills and background.

About the role

As a Senior Staff Machine Learning Scientist, you own the inference and optimization layer that makes AI in agentic workflows fast, efficient, and production-grade. You fine-tune and evaluate models, push latency and throughput on real hardware, and build the runtime that executes bounded AI tasks, validated against usage from Netskope's large customer base so you optimize where the data points, not where you guess.

What's in it for you
  • High-impact ownership. You own the model layer of a net-new product that changes the performance and economics of agentic AI.
  • Cutting-edge, unusual stack. The hard, interesting inference problems live here: quantization, KV-cache and memory management, sparsity, fine-tuning, and hardware acceleration under real-world resource constraints.
  • Real scale to build against. Netskope's customer footprint gives you production signals most teams never see, so you deploy, validate, and iterate fast.
What you will be doing
  • Build and optimize the model inference path: quantization, KV-cache optimization, batching, and latency/memory/throughput tuning on constrained, commodity hardware.
  • Fine-tune and evaluate models for bounded tasks; build eval harnesses that gate a capability to release on real accuracy, latency, and security relevance.
  • Design and grow the task execution runtime (bounded sub-agents), pushing toward dynamic task generation and context compaction.
  • Drive hardware acceleration / sparsity and support for larger models as the platform matures.
  • Partner with the systems and backend engineers to ship capabilities end-to-end and iterate on real production signals.
Required skills and experience
  • 10+ years of overall industry experience, with 4+ years hands-on in ML/AI (model development, fine-tuning, and inference optimization).
  • Hands-on with fine-tuning (e.g. LoRA/QLoRA), quantization (GGUF/AWQ/GPTQ), and inference runtimes (vLLM/SGLang, TensorRT-LLM, ONNX Runtime, llama.cpp, or MLX/CoreML). On-device or edge inference experience is a strong plus.
  • Strong Python; comfort reaching into C++ for low-level interop is a plus.
  • Solid grasp of transformer internals and the levers that move real inference performance and cost: KV cache, attention, batching, memory footprint.
  • Fluency with agentic coding systems and genuine curiosity about agent harnesses like Claude Code, Pi, and Codex, so you should already be building with them, or itching to.
  • Clear communication: able to distill a model or infra bottleneck into an actionable concept for cross-functional teammates.
Education
  • MS in Computer Science, Machine Learning, Electrical Engineering, or equivalent technical degree required, with a focus in AI/ML research; PhD in a related field strongly preferred.

Apply