Principal Engineer – Time-Series & Sensor Reasoning Models Careers at Analog Devices in San Jose, California | Apply Now
Analog Devices
Principal Engineer – Time-Series & Sensor Reasoning Models Careers at Analog Devices in San Jose, California | Apply Now
Job Description
Principal Engineer – Time-Series & Sensor Reasoning Models Careers at Analog Devices in San Jose, California
Overview
Imagine pioneering the future of Physical Intelligence at Analog Devices' Lorenz Labs, where sensors meet sophisticated AI to create systems that truly understand the physical world. As a Principal Engineer – Time-Series & Sensor Reasoning Models in San Jose, California, you'll lead groundbreaking research at the intersection of time-series foundation models, multimodal sensor fusion, and edge AI deployment. Analog Devices, a global semiconductor powerhouse with over $9 billion in revenue and 24,000 employees worldwide, is at the forefront of bridging physical and digital realms.
Lorenz Labs represents ADI's bold venture into advanced AI engineering, focusing on foundation models that reason about time, signals, and embodied experiences. Your role will be central to developing PhysGPT—a suite of physically-intelligent models that extend beyond language and vision into audio, motion, photonic, and physiological signals. Based in the heart of Silicon Valley's innovation hub in San Jose, California, and with opportunities in Rio Robles, California, you'll collaborate with world-class hardware engineers, signal processing experts, and AI researchers to transform industries like healthcare, industrial automation, and robotics.
This isn't just engineering; it's architecting the Artificial Engineer—an AI with human-like intuition for electro-physical systems. With access to ADI's unparalleled sensor data ecosystem and edge hardware, you'll tackle challenges like anomaly detection in industrial systems, predictive health monitoring via biosignals, and context-aware robotics through environmental sensing. Expect to publish at premier venues like NeurIPS, ICLR, and ICASSP, while shaping Lorenz Labs' strategic vision for foundation-scale physical reasoning.
A Day in the Life
Your morning in San Jose, California begins with a team stand-up at Lorenz Labs, reviewing progress on Tiny Recursive Models for edge deployment. Diving into your workstation, you'll analyze multi-sensor streams—fusing IMU data with audio cues to train State-Space Transformers that predict equipment failures hours in advance. By mid-morning, you're prototyping a LoRA-fine-tuned model on physiological signals (PPG and motion) for wearable health tech, leveraging ADI's cloud pipelines on AWS.
Lunch sparks cross-disciplinary discussions with hardware architects from Rio Robles, California, brainstorming energy-efficient inference for photonic sensors. Afternoon hours involve mentoring junior researchers on DPO alignment techniques, followed by experimental runs on Liquid Neural Networks for real-time audio reasoning. As the day winds down, you draft a paper section on cross-modal sensor fusion, preparing for an upcoming ICML submission. Evenings might include 10% travel prep for industry partnerships, returning inspired by San Jose's vibrant tech ecosystem.
This rhythm blends deep research autonomy with collaborative impact, all while enjoying California's sunny climate and proximity to Stanford and UC Berkeley talent pools.
Why San Jose, California
San Jose, California anchors Silicon Valley's epicenter, home to tech giants and endless innovation. As Analog Devices' West Coast hub, it offers unmatched access to venture capital, top-tier universities, and a thriving AI community. Live minutes from Levi's Stadium, yet immersed in diverse neighborhoods from Japantown to Willow Glen. The region's Mediterranean climate—mild winters, sunny summers—perfects outdoor pursuits like hiking in nearby Santa Cruz Mountains or biking along Guadalupe River Trail.
Rio Robles, California complements with its engineering legacy, offering a quieter pace while remaining connected to San Jose's buzz. Both locations boast robust public transit, international airports, and cultural richness—from San Jose Museum of Art to wineries in nearby Santa Clara Valley. With median home prices reflecting high demand for talent, ADI's competitive compensation ensures financial comfort amid California's premium lifestyle.
Career Growth
At Lorenz Labs, growth accelerates through leadership in Physical Intelligence. Principal Engineers mentor teams, influence PhysGPT roadmap, and lead multi-year R&D initiatives. Expect rapid advancement to Distinguished Engineer or Lab Director roles, backed by ADI's $9B+ R&D investment. Publish prolifically, secure patents, and collaborate with partners like leading AI labs—your work shapes industry standards.
ADI's global mobility programs enable rotations across Ireland, Thailand, and Massachusetts sites. Internal academies offer executive training, while Lorenz Labs' flat structure fosters direct impact on C-suite strategy. With 24,000 employees, networking spans digitized factories to digital healthcare, positioning you as a physical AI luminary.
Rewards and Benefits
Compensation leads industry: $170,775–$256,163 base, plus performance bonuses tied to PhysGPT milestones. Full medical, dental, vision coverage; 401k matching; generous PTO, holidays, sick leave. Equity grants align with ADI's NASDAQ: ADI growth. Unique perks include conference travel budgets, hardware prototyping labs, and Lorenz Labs' innovation sabbaticals.
Our Culture
Lorenz Labs embodies ADI's Ahead of What's Possible™ ethos—curiosity-driven, collaborative, inclusive. Diverse teams (race, gender, veteran, neurodiverse) thrive in open-plan labs fostering serendipitous breakthroughs. Weekly Physical AI seminars, hackathons, and offsites build camaraderie. As an equal opportunity employer, ADI champions EEO, supporting parental leave, accessibility, and belonging for all.
How to Apply
Join the Physical Intelligence revolution. Submit your resume, PhD transcript, publication list, and GitHub/portfolio via ADI's careers portal. Highlight time-series projects, sensor fusion experience, and alignment technique implementations. Selected candidates interview with Lorenz Labs directors, hardware leads, and peers—technical deep-dive plus cultural fit. U.S. export compliance applies; non-citizens may require licensing review. Apply now—shape the Artificial Engineer at Analog Devices in San Jose, California.
FAQ
Locations
- San Jose, California, United States
- Rio Robles, California, United States
Salary
170,775 - 256,163 USD / yearly
170,775 - 256,163 USD / yearly
Source: ai estimated
* This is an estimated range based on market data and may vary based on experience and qualifications.
Skills Required
- Time-series machine learningintermediate
- Signal processing expertiseintermediate
- Foundation models (Chronos, TimesFM, TimeGPT)intermediate
- Sensor modeling and fusionintermediate
- Multimodal reasoningintermediate
- Audio perception and reasoningintermediate
- Biosignals analysis (PPG, IMU)intermediate
- Representation learningintermediate
- Causal inference in temporal dataintermediate
- Motif discovery in high-dimensional dataintermediate
- Model benchmarking and evaluationintermediate
- LoRA/Q-LoRA fine-tuningintermediate
- DPO (Direct Preference Optimization)intermediate
- RLAIF (Reinforcement Learning from AI Feedback)intermediate
- Python and PyTorch proficiencyintermediate
- Large-scale training pipelinesintermediate
- AWS/GCP distributed systemsintermediate
- Edge hardware optimizationintermediate
- Tiny Recursive Modelsintermediate
- Liquid Neural Networksintermediate
- State-Space Transformersintermediate
- Sensor fusion architecturesintermediate
- Physics-aligned AI modelsintermediate
- Cross-modal alignmentintermediate
Required Qualifications
- Ph.D. in Electrical Engineering, Computer Science, or Applied Physics (experience)
- 10+ years research and industrial experience in ML/signal processing (experience)
- Deep expertise in time-series foundation models (experience)
- Strong background in sensor modeling (PPG, IMU, audio, photonics) (experience)
- Experience in context-aware multimodal reasoning (experience)
- Proficiency in representation learning for temporal data (experience)
- Hands-on with parameter-efficient fine-tuning (LoRA, Q-LoRA) (experience)
- Expertise in reward-based optimization (DPO, PPO, RLAIF) (experience)
- Fluency in Python, PyTorch, and distributed training (experience)
- Proven track record in ML publications (NeurIPS, ICLR, ICML) (experience)
- Leadership in bridging sensing hardware with AI models (experience)
- Experience with embedded sensing and edge deployment (experience)
- Record of patents or open-source contributions (experience)
- Ability to collaborate across ML, hardware, systems teams (experience)
- Demonstrated innovation in physical intelligence systems (experience)
Responsibilities
- Lead R&D on time-series foundation models for multi-sensor streams
- Develop compact recursive models for edge hardware deployment
- Advance sensor fusion across acoustic, inertial, photonic domains
- Create audio reasoning models for context and intent interpretation
- Build benchmarking pipelines for time-series model evaluation
- Apply LoRA, Q-LoRA, adapter-tuning for sensor datasets
- Implement DPO and RLAIF for physical reasoning tasks
- Partner with hardware teams for energy-efficient sensing
- Publish research at top venues (NeurIPS, ICLR, ICASSP)
- Mentor junior researchers in Lorenz Labs
- Shape strategy for PhysGPT and Artificial Engineer vision
- Explore Tiny Recursive Models and Liquid Neural Networks
- Investigate cross-domain temporal modeling robustness
- Co-design architectures for real-time edge inference
- Develop tools for physics-aligned model optimization
- Conduct anomaly detection and forecasting research
Benefits
- general: Competitive salary $170,775 - $256,163 USD
- general: Discretionary performance-based bonus
- general: Comprehensive medical coverage
- general: Vision and dental insurance
- general: 401k retirement savings plan
- general: Paid vacation time
- general: Paid holidays
- general: Paid sick time
- general: Work at the frontier of Physical Intelligence
- general: Access to ADI's global hardware ecosystem
- general: Collaboration with world-class AI researchers
- general: Opportunities for publications at top conferences
- general: Mentorship and leadership development
- general: 10% travel for industry partnerships
- general: Cutting-edge tools and cloud infrastructure
- general: Influence on PhysGPT foundation models
Tags & Categories
Answer 10 quick questions to check your fit for Principal Engineer – Time-Series & Sensor Reasoning Models Careers at Analog Devices in San Jose, California | Apply Now @ Analog Devices.
Related Books and Jobs
Principal Engineer – Time-Series & Sensor Reasoning Models Careers at Analog Devices in San Jose, California | Apply Now
Analog Devices
Principal Engineer – Time-Series & Sensor Reasoning Models Careers at Analog Devices in San Jose, California | Apply Now
Job Description
Principal Engineer – Time-Series & Sensor Reasoning Models Careers at Analog Devices in San Jose, California
Overview
Imagine pioneering the future of Physical Intelligence at Analog Devices' Lorenz Labs, where sensors meet sophisticated AI to create systems that truly understand the physical world. As a Principal Engineer – Time-Series & Sensor Reasoning Models in San Jose, California, you'll lead groundbreaking research at the intersection of time-series foundation models, multimodal sensor fusion, and edge AI deployment. Analog Devices, a global semiconductor powerhouse with over $9 billion in revenue and 24,000 employees worldwide, is at the forefront of bridging physical and digital realms.
Lorenz Labs represents ADI's bold venture into advanced AI engineering, focusing on foundation models that reason about time, signals, and embodied experiences. Your role will be central to developing PhysGPT—a suite of physically-intelligent models that extend beyond language and vision into audio, motion, photonic, and physiological signals. Based in the heart of Silicon Valley's innovation hub in San Jose, California, and with opportunities in Rio Robles, California, you'll collaborate with world-class hardware engineers, signal processing experts, and AI researchers to transform industries like healthcare, industrial automation, and robotics.
This isn't just engineering; it's architecting the Artificial Engineer—an AI with human-like intuition for electro-physical systems. With access to ADI's unparalleled sensor data ecosystem and edge hardware, you'll tackle challenges like anomaly detection in industrial systems, predictive health monitoring via biosignals, and context-aware robotics through environmental sensing. Expect to publish at premier venues like NeurIPS, ICLR, and ICASSP, while shaping Lorenz Labs' strategic vision for foundation-scale physical reasoning.
A Day in the Life
Your morning in San Jose, California begins with a team stand-up at Lorenz Labs, reviewing progress on Tiny Recursive Models for edge deployment. Diving into your workstation, you'll analyze multi-sensor streams—fusing IMU data with audio cues to train State-Space Transformers that predict equipment failures hours in advance. By mid-morning, you're prototyping a LoRA-fine-tuned model on physiological signals (PPG and motion) for wearable health tech, leveraging ADI's cloud pipelines on AWS.
Lunch sparks cross-disciplinary discussions with hardware architects from Rio Robles, California, brainstorming energy-efficient inference for photonic sensors. Afternoon hours involve mentoring junior researchers on DPO alignment techniques, followed by experimental runs on Liquid Neural Networks for real-time audio reasoning. As the day winds down, you draft a paper section on cross-modal sensor fusion, preparing for an upcoming ICML submission. Evenings might include 10% travel prep for industry partnerships, returning inspired by San Jose's vibrant tech ecosystem.
This rhythm blends deep research autonomy with collaborative impact, all while enjoying California's sunny climate and proximity to Stanford and UC Berkeley talent pools.
Why San Jose, California
San Jose, California anchors Silicon Valley's epicenter, home to tech giants and endless innovation. As Analog Devices' West Coast hub, it offers unmatched access to venture capital, top-tier universities, and a thriving AI community. Live minutes from Levi's Stadium, yet immersed in diverse neighborhoods from Japantown to Willow Glen. The region's Mediterranean climate—mild winters, sunny summers—perfects outdoor pursuits like hiking in nearby Santa Cruz Mountains or biking along Guadalupe River Trail.
Rio Robles, California complements with its engineering legacy, offering a quieter pace while remaining connected to San Jose's buzz. Both locations boast robust public transit, international airports, and cultural richness—from San Jose Museum of Art to wineries in nearby Santa Clara Valley. With median home prices reflecting high demand for talent, ADI's competitive compensation ensures financial comfort amid California's premium lifestyle.
Career Growth
At Lorenz Labs, growth accelerates through leadership in Physical Intelligence. Principal Engineers mentor teams, influence PhysGPT roadmap, and lead multi-year R&D initiatives. Expect rapid advancement to Distinguished Engineer or Lab Director roles, backed by ADI's $9B+ R&D investment. Publish prolifically, secure patents, and collaborate with partners like leading AI labs—your work shapes industry standards.
ADI's global mobility programs enable rotations across Ireland, Thailand, and Massachusetts sites. Internal academies offer executive training, while Lorenz Labs' flat structure fosters direct impact on C-suite strategy. With 24,000 employees, networking spans digitized factories to digital healthcare, positioning you as a physical AI luminary.
Rewards and Benefits
Compensation leads industry: $170,775–$256,163 base, plus performance bonuses tied to PhysGPT milestones. Full medical, dental, vision coverage; 401k matching; generous PTO, holidays, sick leave. Equity grants align with ADI's NASDAQ: ADI growth. Unique perks include conference travel budgets, hardware prototyping labs, and Lorenz Labs' innovation sabbaticals.
Our Culture
Lorenz Labs embodies ADI's Ahead of What's Possible™ ethos—curiosity-driven, collaborative, inclusive. Diverse teams (race, gender, veteran, neurodiverse) thrive in open-plan labs fostering serendipitous breakthroughs. Weekly Physical AI seminars, hackathons, and offsites build camaraderie. As an equal opportunity employer, ADI champions EEO, supporting parental leave, accessibility, and belonging for all.
How to Apply
Join the Physical Intelligence revolution. Submit your resume, PhD transcript, publication list, and GitHub/portfolio via ADI's careers portal. Highlight time-series projects, sensor fusion experience, and alignment technique implementations. Selected candidates interview with Lorenz Labs directors, hardware leads, and peers—technical deep-dive plus cultural fit. U.S. export compliance applies; non-citizens may require licensing review. Apply now—shape the Artificial Engineer at Analog Devices in San Jose, California.
FAQ
Locations
- San Jose, California, United States
- Rio Robles, California, United States
Salary
170,775 - 256,163 USD / yearly
170,775 - 256,163 USD / yearly
Source: ai estimated
* This is an estimated range based on market data and may vary based on experience and qualifications.
Skills Required
- Time-series machine learningintermediate
- Signal processing expertiseintermediate
- Foundation models (Chronos, TimesFM, TimeGPT)intermediate
- Sensor modeling and fusionintermediate
- Multimodal reasoningintermediate
- Audio perception and reasoningintermediate
- Biosignals analysis (PPG, IMU)intermediate
- Representation learningintermediate
- Causal inference in temporal dataintermediate
- Motif discovery in high-dimensional dataintermediate
- Model benchmarking and evaluationintermediate
- LoRA/Q-LoRA fine-tuningintermediate
- DPO (Direct Preference Optimization)intermediate
- RLAIF (Reinforcement Learning from AI Feedback)intermediate
- Python and PyTorch proficiencyintermediate
- Large-scale training pipelinesintermediate
- AWS/GCP distributed systemsintermediate
- Edge hardware optimizationintermediate
- Tiny Recursive Modelsintermediate
- Liquid Neural Networksintermediate
- State-Space Transformersintermediate
- Sensor fusion architecturesintermediate
- Physics-aligned AI modelsintermediate
- Cross-modal alignmentintermediate
Required Qualifications
- Ph.D. in Electrical Engineering, Computer Science, or Applied Physics (experience)
- 10+ years research and industrial experience in ML/signal processing (experience)
- Deep expertise in time-series foundation models (experience)
- Strong background in sensor modeling (PPG, IMU, audio, photonics) (experience)
- Experience in context-aware multimodal reasoning (experience)
- Proficiency in representation learning for temporal data (experience)
- Hands-on with parameter-efficient fine-tuning (LoRA, Q-LoRA) (experience)
- Expertise in reward-based optimization (DPO, PPO, RLAIF) (experience)
- Fluency in Python, PyTorch, and distributed training (experience)
- Proven track record in ML publications (NeurIPS, ICLR, ICML) (experience)
- Leadership in bridging sensing hardware with AI models (experience)
- Experience with embedded sensing and edge deployment (experience)
- Record of patents or open-source contributions (experience)
- Ability to collaborate across ML, hardware, systems teams (experience)
- Demonstrated innovation in physical intelligence systems (experience)
Responsibilities
- Lead R&D on time-series foundation models for multi-sensor streams
- Develop compact recursive models for edge hardware deployment
- Advance sensor fusion across acoustic, inertial, photonic domains
- Create audio reasoning models for context and intent interpretation
- Build benchmarking pipelines for time-series model evaluation
- Apply LoRA, Q-LoRA, adapter-tuning for sensor datasets
- Implement DPO and RLAIF for physical reasoning tasks
- Partner with hardware teams for energy-efficient sensing
- Publish research at top venues (NeurIPS, ICLR, ICASSP)
- Mentor junior researchers in Lorenz Labs
- Shape strategy for PhysGPT and Artificial Engineer vision
- Explore Tiny Recursive Models and Liquid Neural Networks
- Investigate cross-domain temporal modeling robustness
- Co-design architectures for real-time edge inference
- Develop tools for physics-aligned model optimization
- Conduct anomaly detection and forecasting research
Benefits
- general: Competitive salary $170,775 - $256,163 USD
- general: Discretionary performance-based bonus
- general: Comprehensive medical coverage
- general: Vision and dental insurance
- general: 401k retirement savings plan
- general: Paid vacation time
- general: Paid holidays
- general: Paid sick time
- general: Work at the frontier of Physical Intelligence
- general: Access to ADI's global hardware ecosystem
- general: Collaboration with world-class AI researchers
- general: Opportunities for publications at top conferences
- general: Mentorship and leadership development
- general: 10% travel for industry partnerships
- general: Cutting-edge tools and cloud infrastructure
- general: Influence on PhysGPT foundation models
Tags & Categories
Answer 10 quick questions to check your fit for Principal Engineer – Time-Series & Sensor Reasoning Models Careers at Analog Devices in San Jose, California | Apply Now @ Analog Devices.
Related Books and Jobs