Sr. Data Scientist

As a Senior Data Scientist, you will lead machine learning research, develop, and optimize large-scale AI models. You will collaborate closely with ML engineers and data engineers to scale AI models, integrate them into cloud platforms, and ensure optimal performance following MLOps best practices.

Essential Duties/Responsibilities

• Deep Learning & AI Frameworks: Develop and optimize models using PyTorch, ONNX, CUDA, and TensorRT.
• Neural Network Architectures: Design, implement, and fine-tune advanced architectures, including Transformers, Diffusion Models, and Mixture-of-Experts (MoE) for scalable AI solutions.
• Multimodal LLMs & Vision-Question Understanding (VQU): Apply and fine-tune Multimodal LLMs (e.g., DeepSeek-Vision, Janus Pro, LLaMA-3.2 Vision, CLIP, LLaVA, BLIP, Flamingo) for image-based reasoning and question understanding.
• Object Detection & Computer Vision: Implement YOLO, Faster R-CNN, DETR, and similar models for real-world applications, including risk analysis.
• Large-Scale Distributed Training: Develop and deploy training pipelines on cloud and GPU clusters.
• Model Optimization & Risk Analysis: Improve model performance, detect drift, conduct A/B testing, and assess risks in AI-driven solutions.
• MLOps & Cloud Deployment: Utilize Databricks MLflow, Unity Catalog, or AWS SageMaker for model tracking and deployment. Implement CI/CD pipelines, workflow automation, monitoring, and automated testing for robust AI deployment.
• Cross-Functional Collaboration: Work closely with engineering teams and stakeholders to effectively integrate AI solutions.
  
  

Minimum Qualifications

• 6 years of experience in Data Science or Machine Learning Engineering.
• Expertise in deep learning frameworks (PyTorch, TensorRT, ONNX, CUDA).
• Experience with Multimodal LLMs & Vision-Language Models.
• Proficiency in Object Detection & Computer Vision models.
• Experience with large-scale distributed training on cloud/GPU clusters.
• Strong background in model optimization, A/B testing, and drift detection.
• Bachelor’s or Master’s degree in a STEM field (e.g., Computer Science, Machine Learning, Data Science).
  
  

Note: The Company reserves exclusive right in its sole discretion to modify, adjust, delete, add or otherwise change the above at any time.

Hybrid Workplace

SoundThinking follows a hybrid schedule for employees who live equal to or less than 50 miles from one of our office locations, which include Fremont, CA, Tucson, AZ, Washington, D.C., or Iselin, NJ. Employees are expected to work onsite 3 days per week – the specific days are dependent on the office location.

SoundThinking provides equal employment opportunities (EEO) to all employees and applicants for employment without regard to race, color, religion, sex, national origin, age, disability or genetics. In addition to federal law requirements, SoundThinking complies with applicable state and local laws governing nondiscrimination in employment in every location in which the company has facilities. This policy applies to all terms and conditions of employment, including recruiting, hiring, placement, promotion, termination, layoff, recall, transfer, leaves of absence, compensation and training. SoundThinking maintains a drug-free workplace policy.

SoundThinking expressly prohibits any form of workplace harassment based on race, color, religion, gender, sexual orientation, gender identity or expression, national origin, age, genetic information, disability, or veteran status. Improper interference with the ability of SoundThinking’s employees to perform their job duties may result in discipline up to and including discharge. If you are an individual with a disability and require a reasonable accommodation to complete any part of the application process, or are limited in the ability or unable to access or use this online application process and need an alternative method for applying, you may contact SoundThinking at careers@soundthinking.com for assistance.