Highlights
Work on one of the fastest AI supercomputers in the world. Transform real-time AI applications through speculative decoding techniques.
Description
Job Summary
pJoin the elite team at Cerebras Systems, a pioneer in AI chip technology. As a Research Engineer on our Inference ML team, you will work on transforming cutting-edge language and vision models to run efficiently on our innovative hardware.
Responsibilities
- Adapt transformer-based models (NLP and/or vision) to Cerebras hardware
- Optimize models for inference performance (latency, throughput)
- Run experiments, analyze results, and support model improvements
- Validate and debug models on the Cerebras system with guidance from senior team members
- Collaborate with cross-functional teams to integrate models into our architecture
Required Skills
- Python programming expertise
- Experience with machine learning frameworks (PyTorch, Transformers)
- Familiarity with deep learning concepts and neural networks
- Knowledge of speculative decoding techniques
- Strong problem-solving skills in Linux environments
Required Skills Explained
{'html': '
- Python: Essential for model implementation and experimentation.
- ML Frameworks: Proficiency with at least one framework such as PyTorch or Hugging Face Transformers is crucial.
- Transformer-Based Models: Understanding of NLP and vision models, including transformers and their applications.
- Speculative Decoding and Pruning: Knowledge in advanced techniques for improving inference performance.
- C++ Programming: Useful for system-level optimizations and debugging.
'}
Who is this for
pThis role is ideal for software engineers and machine learning enthusiasts with a passion for pushing the boundaries of AI technology. Applicants should have hands-on experience with model optimization, inference performance tuning, and collaborative team work.
Why This Job is a Good Opportunity
{'html': 'ulliWork on the leading AI chip, offering unmatched training and inference speeds.liJoin a team with access to cutting-edge research and state-of-the-art hardware.liPotential for career growth in a rapidly expanding field of AI technology.liOpportunities to collaborate across multiple disciplines within Cerebras Systems.'}
Interview Preparation Tips
{'html': '
- Review deep learning concepts and transformer models thoroughly.
- Practice coding challenges using Python and your preferred ML framework.
- Familiarize yourself with speculative decoding, pruning techniques, and sparse attention mechanisms.
- Prepare to discuss real-world applications of your skills in AI systems.
'}
Career Growth in This Role
{'html': 'pThe role offers a unique blend of technical challenges and opportunities for innovation. With experience, you can specialize in speculative decoding or become an expert in model optimization techniques. Additionally, the fast-paced nature of the job allows for rapid skill development and exposure to cutting-edge research.pLong-term career growth may include taking on leadership roles within Cerebras Systems or transitioning into senior engineering positions where you could influence product direction and strategic decisions.'}
Explore More Opportunities
Skills
Frequently Asked Questions
What kind of models will I be working with?You will work on transformer-based models, specifically in natural language processing (NLP) and vision tasks.
What is speculative decoding, and how does it fit into this role?Speculative decoding involves predicting the next steps in a model's computation to reduce latency. You will apply this technique to optimize inference performance on Cerebras hardware.
How can I improve my chances of being selected for this position?Highlight your experience with machine learning frameworks, deep learning concepts, and hands-on optimization techniques in your application.
