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Staff AI/ML Engineer, Time-Series & Sensor Reasoning Models
Analog Devices. Lead R&D on intelligent time-series agents for edge by combining anomaly detection, reasoning, and forecasting foundation models .
Core Competencies
Role fitCore Competencies
Use this summary to align your resume positioning with the role.
Demonstrates deep expertise in time-series machine learning, signal processing, and foundation models, with proficiency in Python and PyTorch. Capable of leading research and development efforts while collaborating across disciplines to create innovative, deployable solutions in embedded systems.
Highest-signal resume keywords
Time-Series Machine LearningSignal ProcessingFoundation ModelsPython ProgrammingPyTorch Framework
ATS Keywords
Tailor your resumeApplicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard Skills
Anomaly DetectionMultimodal ModelsTime-Series EncodingTime-Series CompressionLoRA/Q-LoRADPOPPOStatistical Hypothesis TestingExperimental DesignCausal Discovery
Soft Skills
LeadershipCollaborationMentoringAgilityInnovation
Tools & Technologies
AWSGCPCloud SystemsDistributed Systems
Industry Keywords
Time-Series Reasoning ModelsCross-AttentionMultimodal EmbeddingTime-Series Agentic SystemsTime-Series MemoryRAGBenchmarking PipelinesSensor FusionCross-Modal AlignmentPhysical Intelligence Systems
Tech Stack
Tools & technologiesAWSCloudDistributed SystemsGoogle Cloud PlatformPythonPyTorch
About the role
Key responsibilities & impact- Lead R&D on intelligent time-series agents for edge by combining anomaly detection, reasoning, and forecasting foundation models
- Develop multimodal models using electrical, audio, motion, physiological, text, and image data
- Advance sensor fusion and cross-modal alignment across electrical, acoustic, inertial, and photonic domains
- Create benchmarking pipelines for cross-domain time-series foundation models, including representation robustness, interpretability, and hardware performance metrics
- Apply LoRA, Q-LoRA, adapter-tuning, and contrastive alignment methods to multimodal sensor datasets
- Lead state-of-the-art research in time-series embedding and compression for edge reasoning models
- Investigate DPO and RLAIF foundation alignment techniques for physical and sensory reasoning tasks
- Partner with hardware, signal processing, and systems teams to co-design real-time, energy-efficient sensing architectures
- Design statistical experiments for subject-matter experts to collect sensor data for model development
- Publish research and represent ADI at major ML and signal-processing venues
- Mentor junior researchers and help shape Lorenz Labs’ foundation-model strategy
Requirements
What you’ll need- 6+ years of experience developing software products with an AI/ML focus
- 4+ years of experience developing AI/ML products
- Deep expertise in time-series ML, signal processing, and foundation models, including Chronos, TimesFM, and TimeGPT
- Hands-on experience training or fine-tuning one or more time-series foundation models
- Proficiency in representation learning, time-series encoding, time-series compression, and motif discovery in high-dimensional temporal data
- Knowledge of state-of-the-art time-series reasoning models, cross-attention, multimodal embedding, time-series agentic systems, time-series memory, and RAG
- Experience with parameter-efficient fine-tuning, LoRA/Q-LoRA, DPO, PPO, and RLAIF
- Strong knowledge of statistical hypothesis testing, experimental design, and causal discovery
- Fluency in Python and PyTorch
- Experience with large-scale training pipelines using cloud or distributed systems such as AWS and GCP
- Ability to collaborate across ML, hardware, and embedded systems disciplines and translate research into deployable physical intelligence systems
- Demonstrated leadership and agility in combining technical solutions to business problems, preferably for embedded systems
- Record of innovation through patents, publications, or open-source contributions
- Applicants other than US Citizens, US Permanent Residents, and protected individuals may have to undergo export licensing review
Benefits
Comp & perks- Discretionary performance-based bonus based on personal and company factors
- Medical coverage
- Vision coverage
- Dental coverage
- 401k
- Paid vacation
- Paid holidays
- Sick time
- Other benefits