Principal AI/ML Engineer, Time-Series & Sensor Reasoning Models (Lorenz Labs)
Analog Devices14 days ago
Base Salary
$230k - $316k/yr
Responsibilities
- Lead R&D to create intelligent time-series agents and reasoning foundation models for edge applications.
- Develop multimodal models incorporating electrical, audio, motion, physiological, photonic, text, and image data.
- Advance sensor fusion and cross-modal alignment across electrical, acoustic, inertial, and photonic domains.
- Create benchmarking pipelines covering representation robustness, interpretability, and hardware-performance metrics.
- Apply LoRA, Q-LoRA, adapter-tuning, contrastive alignment, DPO, and RLAIF to multimodal sensor datasets and reasoning tasks.
- Research time-series embedding, compression, memory, retrieval-augmented generation, and agentic systems.
- Co-design real-time, energy-efficient sensing architectures with hardware, signal-processing, and embedded-systems teams.
- Design statistical experiments for subject-matter experts to collect sensor data for model development.
- Publish and represent Analog Devices at ML and signal-processing venues including NeurIPS, ICLR, ICML, ICASSP, and KDD.
- Mentor junior researchers and help shape foundation-model strategy for physical systems.
Requirements
- At least 10 years of experience developing software products with an AI/ML focus and at least 6 years developing AI/ML products.
- Deep expertise in time-series machine learning, signal processing, and foundation models, including hands-on training or fine-tuning of time-series foundation models such as Chronos, TimesFM, or TimeGPT.
- Proficiency in representation learning, time-series encoding and compression, and motif discovery in high-dimensional temporal data.
- Knowledge of time-series reasoning models, cross-attention, multimodal embeddings, agentic systems, memory, and RAG.
- Experience with parameter-efficient fine-tuning and reward-based optimization methods including LoRA, Q-LoRA, DPO, PPO, and RLAIF.
- Strong knowledge of statistical hypothesis testing, experimental design, and causal discovery.
- Fluency in Python, PyTorch, and large-scale training pipelines using cloud or distributed systems.
- Ability to collaborate across ML, hardware, and embedded-systems disciplines and translate research into deployable physical-intelligence systems.
- Preferred: Ph.D. in Electrical Engineering, Computer Science, or Applied Physics.
- Preferred: leadership combining technical solutions with business needs, especially for embedded systems.
- Preferred: patents, publications, or open-source contributions demonstrating innovation.
Benefits
- Medical, vision, and dental coverage.
- 401(k), paid vacation, holidays, and sick time.
- Discretionary performance-based bonus eligibility.
- Required travel is 10% of the time.
- 1st Shift/Days schedule.
Tech Stack
Categories
AI ResearchML Engineering
About Analog Devices
Analog Devices, Inc. designs and sells analog, mixed-signal, RF, power, sensor, and digital signal processing ICs to OEMs across industrial, automotive, communications, consumer, and healthcare markets. Founded in 1965 and headquartered in Wilmington, Massachusetts, the NASDAQ-listed company earns revenue from catalog and application-specific semiconductors and supporting software used in applications from factory automation and vehicles to data centers and medical devices.