Applied Machine Learning Scientist Computer Vision NLP Large Language Models LLM
Applied Machine Learning Scientist Computer Vision NLP Large Language Models LLM
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4-7years
Not Disclosed
Salary Not Disclosed
1 Vacancy
Job Description
We are seeking a highly skilled Applied Machine Learning Scientist with a strong focus on advanced computer vision natural language processing (NLP) and large language models (LLM). This role is ideal for an ML Scientist ready to develop and deploy cuttingedge solutions that address complex realworld challenges bridging the gap between technical innovation and client needs.
Key Responsibilities:
- Advanced Model Development: Design implement and optimize computer vision NLP and large language models applying advanced algorithms to solve complex client problems. Evaluate model suitability based on requirements and constraints ensuring the selection of the most effective approach.
- Data Preprocessing and Feature Engineering: Perform thorough data preprocessing feature engineering and transformation tasks for image text and multimodal data to maximize model performance and accuracy.
- Custom Model Tuning and FineTuning LLMs: Leverage and finetune stateoftheart NLP models such as BERT GPT and other transformerbased architectures adapting them to specific domain requirements. Apply transfer learning domain adaptation and fewshot techniques for customized model outputs.
- Computer Vision Solutions: Implement and optimize computer vision techniques including image segmentation object detection classification and image generation using stateoftheart architectures such as CNNs YOLO and vision transformers.
- Solution Strategy and Client Collaboration: Collaborate with clients to understand their unique needs recommend solution strategies and develop tailored machine learning and deep learning applications for NLP and computer vision tasks.
- ProductionReady Code and Pipeline Deployment: Write clean efficient productiongrade Python code for deploying machine learning models as scalable cloudbased pipelines. Adhere to software engineering best practices including version control modular design and testing.
- Cloud Platform and Model Deployment: Design and manage machine learning pipelines on cloud platforms (e.g. AWS Azure GCP) leveraging their services for model deployment versioning monitoring and scaling.
- Result Analysis and Communication: Analyze and interpret model results deriving actionable insights and translating complex findings for diverse audiences from technical stakeholders to business users.
Requirements
Required Skills and Qualifications:
- Computer Vision Expertise: Proven experience with computer vision techniques such as CNNs object detection image segmentation and vision transformers. Proficiency in applying stateoftheart models (e.g. YOLO EfficientNet) for imagebased tasks.
- NLP and Large Language Models: Advanced knowledge of NLP techniques and experience with transformerbased models like BERT GPT and T5. Skilled in finetuning and optimizing large language models (LLMs) for specific tasks and domains.
- Python and Software Engineering: Proficiency in Python programming with a strong understanding of objectoriented programming (OOP) principles and best practices for writing scalable maintainable code.
- Cloud Platforms and Model Deployment: Handson experience with deploying machine learning solutions on cloud platforms (AWS Azure GCP) particularly for NLP and computer vision applications. Familiarity with MLOps tools for automated deployment and monitoring.
- Debugging Optimization and Best Practices: Excellent debugging skills with experience in code optimization memory management and troubleshooting in production environments.
- Client Engagement and Solution Design: Demonstrated ability to communicate effectively with clients understand their needs and provide clear technical solutions aligned with business goals.
Preferred Qualifications:
- MultiModal Modeling: Experience with multimodal models that combine vision and NLP tasks such as CLIP and other transformerbased architectures will be an added advantage.
- Experience with Distributed Computing and Big Data: Familiarity with distributed computing frameworks and big data tools such as Spark and Databricks for handling large datasets in training pipelines.
- Innovation and ProblemSolving: Proactive solutionfocused mindset with a passion for staying at the forefront of ML advancements in computer vision and NLP.
This role is perfect for an ML Scientist with a passion for NLP computer vision and deploying innovative solutions using LLMs. If youre ready to leverage these advanced tools to solve complex challenges and deliver clientfocused outcomes we encourage you to apply.
Employment Type
Full Time
Company Industry
Key Skills
- Laboratory Experience
- Immunoassays
- Machine Learning
- Biochemistry
- Assays
- Research Experience
- Spectroscopy
- Research & Development
- cGMP
- Cell Culture
- Molecular Biology
- Data Analysis Skills
