Join Pika as a Research Engineer (Foundation Model) in Palo Alto, CA. Contribute to cutting-edge research in multimodal foundation models and generative AI. Apply today!
About Pika – Driving the Future of AI
Pika is at the forefront of Generative AI and multimodal foundation models, developing state-of-the-art technologies that will define the future of artificial intelligence. We are seeking a Research Engineer (Foundation Model) to contribute to our groundbreaking research and push the boundaries of AI innovation.
Research Engineer (Foundation Model) Role at Pika
As a Research Engineer (Foundation Model), you will play a pivotal role in the research and development of next-generation multimodal foundation models. You’ll work with a talented team to design, optimize, and scale generative AI models, ensuring they perform efficiently on large distributed systems.
Key Details of the Research Engineer (Foundation Model) Position
| Company Name | Pika |
|---|---|
| Job Title | Research Engineer (Foundation Model) |
| Location | Palo Alto, California, USA |
| Salary | $160,000 – $200,000 per year |
| Employment Type | Full-time |
| Compensation | Competitive salary + equity packages |
Key Responsibilities of the Research Engineer (Foundation Model)
- Lead groundbreaking research in multimodal foundation models.
- Design and develop algorithms and architectures to enhance model performance and scalability.
- Optimize models for production environments, ensuring computational efficiency, throughput, and low latency.
- Analyze and manage large-scale data clusters, identifying inefficiencies and optimizing data loading processes.
- Collaborate with cross-functional teams to drive impactful AI research projects.
Required Qualifications for the Research Engineer (Foundation Model) Role
- Proficiency in Python and PyTorch, with hands-on experience building models from scratch using PyTorch.
- Experience with generative multimodal models like Diffusion Models and GANs.
- Deep understanding of foundational deep learning concepts, including Transformers.
- Strong problem-solving and analytical skills.
Preferred Experience for the Research Engineer (Foundation Model) Role
- 1+ year of industrial or academic lab experience in AI research.
- Experience with large distributed systems (100+ GPUs).
- Familiarity with Linux clusters, systems, and scripting.
Compensation and Benefits for the Research Engineer (Foundation Model)
- Base salary: $160,000 – $200,000 annually, based on experience and location.
- Equity packages: Competitive stock options.
- Comprehensive benefits: Health, dental, vision, life, and disability insurance.
- Retirement plan with employer match.
- Paid time off and other wellness perks.
FAQs About the Research Engineer (Foundation Model) Role
- Where is this role based?
The position is located in Palo Alto, California. - What is the salary range for this position?
The salary range for this role is $160,000 – $200,000 annually, depending on experience and location. - Do I need prior experience in AI research?
While experience is preferred, this role is open to recent graduates with a strong academic background in AI and machine learning.
How Your Profile Fits the Research Engineer (Foundation Model) Role
To optimize your resume for this position:
- Highlight hands-on experience with PyTorch, generative AI models, and large-scale distributed systems.
- Demonstrate your deep learning knowledge, particularly with Transformers, Diffusion Models, and GANs.
- Showcase any academic projects or research experience related to multimodal foundation models.
How Can You Best Position Yourself for the Research Engineer (Foundation Model) Role?
- Tailor your resume to emphasize your experience with large-scale AI models and distributed systems.
- Include any relevant research projects, internships, or academic achievements in deep learning and generative AI.
- Show your enthusiasm for multimodal AI and your desire to work with cutting-edge technologies in a fast-paced environment.
Step 1: Understand the Role
This position focuses on advancing multimodal foundation models, with responsibilities ranging from developing new architectures to optimizing training efficiency. The role requires expertise in generative AI models, large-scale training, and distributed systems.
Key responsibilities:
- Lead and contribute to research on multimodal foundation models.
- Design and optimize algorithms for performance and scalability.
- Work with large-scale data and training clusters (100+ GPUs).
- Collaborate with applied research, data, and infrastructure teams.
Step 2: Assess Required Technical Skills
To be considered for the position, you’ll need to demonstrate proficiency in several technical areas:
Core Skills:
- Python: Strong programming skills, especially for data manipulation, model development, and research purposes.
- Key areas to focus on: NumPy, Pandas, Scikit-learn, and PyTorch for deep learning.
- PyTorch: Hands-on experience in designing and training machine learning models, including generative models.
- Focus on model architectures like Transformers, Diffusion Models, and GANs.
- Deep Learning Concepts:
- Transformers: Key to most state-of-the-art models like GPT, BERT, and Vision Transformers (ViTs).
- Generative Models: Experience with Diffusion Models, GANs, and their applications.
- Distributed Systems:
- Experience with GPU clusters (100+ GPUs), parallel computing, and efficient data loading techniques for training large-scale models.
- Familiarity with Linux systems and cluster management tools (e.g., SLURM, Kubernetes).
Step 3: Build and Enhance Relevant Experience
If you lack direct work experience in certain areas (such as handling large-scale systems):
- Participate in open-source projects involving large models or deep learning.
- Build and experiment with multimodal models like GANs or Diffusion Models.
- Optimize models for training efficiency by working with small-scale systems or cloud resources.
- Develop familiarity with distributed training using platforms like Google Cloud or AWS and tools like Horovod or PyTorch Distributed.
Step 4: Research Pika’s Work
1. Company Background:
- Understand Pika’s goals, products, and vision in advancing Generative AI models.
- Research their approach to multimodal foundation models and any recent publications or research contributions.
2. Industry Trends:
- Stay updated on the latest in multimodal AI, GANs, Diffusion Models, and Transformers.
- Familiarize yourself with new papers, frameworks, and tools in the generative AI space.
Step 5: Prepare for the Interview
1. Technical Interviews:
- Expect questions on deep learning architectures, particularly Transformers, GANs, and Diffusion Models.
- Be prepared for coding challenges that test algorithm design and model implementation using PyTorch.
- Distributed Systems: Demonstrate your understanding of how to optimize training pipelines and handle large data clusters efficiently.
2. Behavioral Interviews:
- Problem-solving scenarios: Discuss challenges you’ve faced and how you tackled inefficiencies in training, model performance, or systems.
- Collaboration: Showcase your ability to work with cross-functional teams, particularly in high-performance computing environments.
3. Prepare Research-Related Questions:
- How does Pika approach the optimization of large-scale training for multimodal models?
- What tools does Pika use for managing distributed systems with hundreds of GPUs?
Step 6: Final Preparation Tips
- Review Relevant Papers: Study papers on multimodal models, diffusion models, and GANs.
- Examples include papers like “Improved Techniques for Training GANs” or “Denoising Diffusion Probabilistic Models”.
- Experiment with Models: Build simple versions of GANs or Diffusion Models and run them on small-scale systems to get hands-on experience.
- System Design Knowledge:
- Understand how to scale AI models and optimize training for large GPUs. Study resources like “Distributed Deep Learning with TensorFlow and PyTorch”.
- Linux and GPU Cluster Management:
- Learn how to manage Linux clusters and troubleshoot issues that arise in high-performance systems. Familiarize yourself with slurm, HPC scheduling, and NFS for data storage.
Step 7: Compensation and Negotiation
Pika offers a competitive salary range of $160,000–$200,000 per year, along with equity options and benefits. If you receive an offer:
- Consider discussing not only base salary but also the equity compensation.
- Reflect on long-term career growth and professional development opportunities at Pika.
Resources for Preparation
1. Books and Courses:
- “Deep Learning with Python” by François Chollet.
- “Deep Learning” by Ian Goodfellow, Yoshua Bengio, and Aaron Courville.
- PyTorch tutorials on PyTorch.org.
2. Research Papers:
- “Attention is All You Need” (Transformer Paper).
- “Denoising Diffusion Probabilistic Models” (Diffusion Model Paper).
3. Tools:
- PyTorch (tutorials, forums, and papers on their official website).
- Kubernetes and Horovod for distributed training.
