Multimodal AI Scientist

L&T Technology Services; Santa Clara (CA); On-site; USD 180,000 - 240,000; Full time

Job summary

L&T Technology Services is seeking a Multimodal AI Scientist in Santa Clara to bridge foundation-model research and production engineering across vision, language, and sensor data.

You will design, train, and deploy multimodal models for engineering drawings, inspection imagery, and IoT telemetry, collaborating with Robotics, CV, and LLM teams to build reusable assets and pipelines. 8–15 years of ML/AI experience, PhD preferred, with publications a plus.

Qualifications

• Master's or PhD in CS/AI/ML/EE; PhD preferred.
• 8–15 years in ML/AI with at least 4 years in multimodal, vision-language, or foundation-model work.
• Deep expertise in transformers, diffusion models, contrastive learning, and cross-attention fusion.
• Strong Python with PyTorch, JAX, or Hugging Face; distributed GPU training on Azure, AWS, or GCP; familiarity with RAG and multimodal retrieval.
• Publications at NeurIPS, ICML, ICLR, CVPR, or ACL are a plus.

Responsibilities

• Design, train, and adapt vision-language and sensor-fusion models for engineering drawings, inspection imagery, P&IDs, video, and IoT telemetry.
• Partner with business units on high-value use cases, such as visual inspection with natural-language reporting, drawing understanding, and digital twin enrichment.
• Take proofs of concept through pilot to production, with defined success metrics and quantified business impact.
• Define data collection and annotation strategies, and build scalable pipelines for video, point clouds, and sensor streams.
• Set benchmarks for accuracy, robustness, hallucination, and safety, and optimize models for latency and edge deployment.
• Mentor engineers, contribute to the CoE repository, and publish papers or file patents.

Skills

Python

PyTorch

JAX

Hugging Face

GPU training

Multimodal ML

Vision-Language

Transformers

Diffusion models

Cross-attention fusion

Education

Master's or PhD in CS/AI/ML/EE

Tools

Azure

AWS

GCP

Hugging Face

PyTorch

Job description

Multimodal AI Scientist – L&T Group AI Center of Excellence

Santa Clara, California |L&T Group-wide (US and India)| 8–15 years |Reports to: Head, AI CoE

About the Role

Build multimodal AI systems that reason jointly over vision, language, audio, sensor, time-series, and 3D data to solve real problems across L&T's engineering, manufacturing, infrastructure, energy, and defence businesses. You will bridge foundation-model research and production engineering, working closely with Robotics, Computer Vision, and Language Model teams to build reusable models and pipelines.

What You'll Do

• Design, train, and adapt vision-language and sensor-fusion models for engineering drawings, inspection imagery, P&IDs, video, and IoT telemetry.
• Partner with business units on high-value use cases, such as visual inspection with natural-language reporting, drawing understanding, and digital twin enrichment.
• Take proofs of concept through pilot to production, with defined success metrics and quantified business impact.
• Define data collection and annotation strategies, and build scalable pipelines for video, point clouds, and sensor streams.
• Set benchmarks for accuracy, robustness, hallucination, and safety, and optimize models for latency and edge deployment.
• Mentor engineers, contribute to the CoE repository, and publish papers or file patents.

First 12 Months

• Define the multimodal AI technical roadmap and reference architecture for the CoE.
• Deliver 3–5 multimodal proofs of concept, at least two scaled to pilot or production.
• Publish reusable model and pipeline assets, plus an evaluation and benchmarking framework.

What We're Looking For

• Master's or PhD in Computer Science, AI, Machine Learning, Electrical Engineering, or a related field (PhD preferred).
• 8–15 years in machine learning or AI, including at least 4 years in multimodal, vision-language, or foundation-model work and a record of taking models to production.
• Deep expertise in transformers, diffusion models, contrastive learning, and cross-attention fusion.
• Strong Python with PyTorch, JAX, or Hugging Face; distributed GPU training experience on Azure, AWS, or GCP; familiarity with RAG and multimodal retrieval.
• Experience with industrial, engineering, or physical-world data is strongly preferred. Publications at NeurIPS, ICML, ICLR, CVPR, or ACL are a plus.