Early-Career Applied ML Engineer
| Company | Kognitos |
|---|---|
| Location | San Jose, CA, USA |
| Salary | $Not Provided – $Not Provided |
| Type | Full-Time |
| Degrees | Master’s, PhD |
| Experience Level | Entry Level/New Grad, Junior |
Requirements
- Master’s or Ph.D. in Computer Science, Machine Learning, or a related field.
- Demonstrated experience developing and deploying machine learning models (via research, internships, or prior roles).
- Familiarity with strategies to optimize large language models (LLMs) and manage resource usage efficiently.
- Proficiency in Python and experience with one or more ML frameworks (e.g., TensorFlow, PyTorch).
- Ability to address complex challenges in a fast-paced, dynamic environment.
Responsibilities
- Design, implement, and deploy machine learning models with an emphasis on deterministic task execution and agentic workflows.
- Work on integrating diverse data formats (e.g., text, images) into AI models tailored to real-world enterprise use cases.
- Explore and refine fine-tuning techniques to optimize resource usage and improve overall model performance.
- Ensure AI systems adhere to regulatory policies, maintain reliability, and deliver measurable business value.
- Partner with product, engineering, and business teams to align AI solutions with strategic objectives.
- Stay current with AI advancements and apply the latest research insights to enhance Kognitos’ platform.
Preferred Qualifications
- Exposure to or interest in advanced AI topics (e.g., multi-agent systems, reinforcement learning).
- Understanding of automation pain points in large-scale enterprise contexts.
- Familiarity with cloud platforms (AWS, GCP, Azure) and distributed ML frameworks like Spark, Ray, or Kubernetes.