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Senior Machine Learning Engineer
3 months ago
TITLE: Senior Machine Learning Engineer - Knox Cloud
POSITION TYPE: Full Time (W2)
LOCATION: Mountain View, CA
ABOUT WorldLink:
WorldLink is a rapidly growing information technology company at the forefront of the tech transformation. From custom software development to cloud hosting, from big data to cognitive computing, we help companies harness and leverage today's most cutting-edge digital technologies to create value and grow.
Collaborative. Respectful. Work hard Play hard. A place to dream and do. These are just a few words that describe what life is like at WorldLink. We embrace a culture of experimentation and constantly strive for improvement and learning.
We take pride in our employees and their future with continued growth and career advancement. We put TEAM first. We are a competitive group that like to win. We're grounded by humility and driven by ambition. we're passionate, and we love tough problems and new challenges. You don't hear a lot of "I don't know how" or "I can't" at WorldLink. If you are passionate about what you do and having fun while doing it; tired of rigid and strict work environments and would like to work in a non-bureaucratic startup cultural environment, WorldLink may be the place for you.
For more information about our craft, visit .
WHO we're looking for:
We are looking for a Senior Machine Learning Engineer - Knox Cloud to join the Knox Cloud Service team and help us shape future of our role in the machine learning domain.
The Knox Cloud Service team focuses on the development and research of large-scale production-level machine learning algorithms, models, and systems and aims at representing our leadership in large-scale machine learning products. The team productizes a wide range of Machine Learning algorithms from various Research teams and Research papers. This is an exciting and unique opportunity for a talented and hard-working machine learning engineer to get involved in envisioning, designing and implementing cutting-edge products with a growing team.
In this position, you will join a collaborative team of world-class developers/research engineers and have a chance to integrate your innovative solutions into our Security & Intelligence services.
Role and Responsibilities:
- Design, develop, and productize both device and server-side machine learning solutions for our services.
- Productize machine learning algorithms from Research teams or Research Papers.
- Create quick prototypes and proof-of-concepts.
- Design experiments, perform evaluations, and apply enhancements to our products.
- Create state of the art ML based security solutions for devices.
Required Experience and Education:
- 5+ years' industrial experience in building machine learning and deep learning-based software solutions in a production setting.
- Masters or Ph.D. degree in Computer Science, Statistics, Math, Physics, Mechanical Engineering, or other quantitative fields.
- Solid experience in one or more of the following areas - cybersecurity, anomaly detection.
- Experience with various MLs, such as LLM, GNN, VAEs and transformer-based models.
- Hands-on experience in Parameter-Efficient Fine-Tuning (PEFT) including p-tuning, LoRA, IA3, etc.
- Understanding of GenAI Foundation Models, Vector DB and Graph DB: Leveraging foundational AI models and vector/graph database technologies for advanced AI capabilities.
- RAG (Retrieval-Augmented Generation) and Model Fine Tuning: Employing RAG techniques for enhanced AI responses and fine-tuning embedder models for optimal performance.
- Familiar with various quantization techniques including QAT and PTQ
- Use of Orchestration Tools: Utilizing advanced tools like Langchain, Pydantic and others for efficient AI model management.
- Solid theoretical background in deep/machine learning.
- (Preferred) Familiar with Amazon Web Services or Google Cloud Platform.
- (Preferred) Familiar with big data tools, data pipelines, RDBMS and NoSQL databases.
- (Preferred) Experience with security & privacy concepts such as zero trust and differential privacy.
