VLM Data Science Expert with self-driving cars OR self-navigating robot Experience

Job title: VLM Data Science Expert with self-driving cars OR self-navigating robot Experience
Location:San Jose, CA or Waukesha, WI (Onsite)

JD: As a Senior Data Scientist with expertise in Vision-Language Models (VLMs) and related technologies to lead the development of efficient, cost-effective multimodal AI solutions. The ideal candidate will have experience with advanced VLM frameworks such as VILA, Isaac, and VSS, and a proven track record of implementing production-grade VLMs for training and testing in real-world environments. A background in healthcare, particularly medical devices, is highly desirable. This role will focus on exploring and deploying state-of-the-art VLM methodologies on cloud platforms like AWS or Azure.

Responsibilities: -

VLM Development & Deployment:

• Design, train, and deploy efficient Vision-Language Models (e.g., VILA, Isaac Sim) for multimodal applications.
• The outstanding concern is that we don't yet have a candidate that has successfully implemented a video-based VLM in an autonomous use case (either robotic industrial or car navigation for example). There must be developers out there with this experience as everyone in the autonomous robotic and self driving cars is working on this.
• Explore cost-effective methods such as knowledge distillation, modal-adaptive pruning, and LoRA fine-tuning to optimize training and inference.
• Implement scalable pipelines for training/testing VLMs on cloud platforms (AWS SageMaker, Azure ML).

Multimodal AI Solutions:

• Develop solutions that integrate vision and language capabilities for applications like image-text matching, visual question answering (VQA), and document data extraction.
• Leverage interleaved image-text datasets and advanced techniques (e.g., cross-attention layers) to enhance model performance.

Healthcare Domain Expertise:

• Apply VLMs to healthcare-specific use cases such as medical imaging analysis, position detection, motion detection and measurements.
• Ensure compliance with healthcare standards while handling sensitive data.

Efficiency Optimization:

• Evaluate trade-offs between model size, performance, and cost using techniques like elastic visual encoders or lightweight architectures.
• Benchmark different VLMs (e.g., GPT-4V, Claude 3.5) for accuracy, speed, and cost-effectiveness on specific tasks.

Collaboration & Leadership:

• Collaborate with cross-functional teams including engineers and domain experts to define project requirements.
• Mentor junior team members and provide technical leadership on complex projects.

Mandatory skills

Experience:

• Minimum of 10+ years of experience in machine learning or data science roles with a focus on vision-language models.
• Proven expertise in deploying production-grade multimodal AI solutions.
• Experience in healthcare or medical devices is highly preferred.

Technical Skills:

• Proficiency in Python and ML frameworks (e.g., PyTorch, TensorFlow).
• Hands-on experience with VLMs such as VILA, Isaac Sim, or VSS.
• Familiarity with cloud platforms like AWS SageMaker or Azure ML Studio for scalable AI deployment.

Domain Knowledge:

• Understanding of medical datasets (e.g., imaging data) and healthcare regulations.

Soft Skills:

• Strong problem-solving skills with the ability to optimize models for real-world constraints.
• Excellent communication skills to explain technical concepts to diverse stakeholders

Good to have skills: -

• Vision-Language Models: VILA, Isaac Sim, EfficientVLM
• Cloud Platforms: AWS SageMaker, Azure ML
• Optimization Techniques: LoRA fine-tuning, modal-adaptive pruning
• Multimodal Techniques: Cross-attention layers, interleaved image-text datasets
• MLOps Tools: Docker, MLflow