ML Engineer
Job Description and Requirements
ASAP start requiring relocation to Sunnyvale, CA. Must have prior internship experience in machine learning.
Synopsys’ Generative AI Center of Excellence defines the technology strategy to advance applications of Generative AI across the company. The GenAI COE pioneers the core technologies – platforms, processes, data, and foundation models – to enable generative AI solutions, and partners with business groups and corporate functions to advance AI-focused roadmaps.
As an ML Engineer in Gen-AI, you will innovate and translate cutting edge research into user experiences on questions like the following:
- How to prompt an LLM like Llama-2, GPT-4, etc. effectively?
- How to build a foundation model for a specific domain like EDA?
- How to blend prompt engineering, retrieval augmentation, and fine-tuning to customize models with the least human time and effort?
Duties
- Design and implement machine learning models and algorithms for Generative AI and Deep Learning applications.
- Conduct experiments to evaluate model performance, identify areas for improvement, and implement optimizations.
- Partner with cross-functional teams to design and develop scalable solutions that meet business goals.
- Stay up to date with the latest research and advancements in the field of Generative AI and Deep Learning and apply this knowledge to improve our models and algorithms.
- Communicate complex technical concepts and findings to both technical and non-technical stakeholders.
- Participate in code reviews, testing, and deployment of machine learning models and algorithms.
Required Qualifications
- BS with 5+ years’ experience or MS degree with 1+ years’ experience in computer science, Electrical Engineering, Mathematics, or related field.
- A Ph.D. in machine learning or a related area with good publication history would be a good fit for this position. We would also love to hear from people with similar skill sets acquired through other career paths.
- 2-5 years of experience in machine learning engineering, with a focus on AI/ML and Deep Learning.
- Proven familiarity with python, and excellent background in data structures and algorithms.
- Good expertise of Probability and Statistics concepts, including Probability, Conditional Probability, Bayes Theorem, Normal Distribution, and Central Limit Theorem.
- Sound knowledge of Linear Algebra and Calculus concepts
- Sound knowledge of deep learning architectures like Recurrent Neural Networks (RNNs), Long-Short-Term-Memory models (LSTMs), and Convolutional Neural Networks (CNNs).
- Experience with deep learning frameworks like Tensorflow or PyTorch.
- Experience with LLMs, Encoder-Decoder Models, and other Generative AI techniques.
- Experience with Natural Language Processing (NLP) and Text Generation using Deep Learning.
- Excellent problem-solving skills and ability to work autonomously as well as collaboratively in a team environment.
- Excellent communication and presentation skills, with the ability to communicate complex technical concepts to both technical and non-technical stakeholders.
- Good expertise with hands-on experience in data cleansing and modeling for deep learning models in at least one domain (language, image, graphs, etc.)
- Experience with cloud-based machine learning platforms such as AWS, GCP, or Azure
Preferred Qualifications
- Proven publication record in top-tier conferences and journals in the field of Machine Learning or NLP or Generative AI.
- Experience with standard machine learning frameworks and tools (Huggingface Transformers, NumPy, Scikit-learn, Pandas, PyTorch, TensorFlow, etc.) and machine learning cloud infrastructure and accelerators (AWS, Google Cloud, GPUs and TPUs).
- Familiarity with supervised and unsupervised learning algorithms like linear regression, logistic regression, random forests, and k-means.
- Prior exposure to AI/ML workflows and tools
- Knowledge and/or exposure to cloud computing technologies like containerization platforms (Docker, Kubernetes, microservices)
- Broad expertise and understanding of AI, NLP, LLM, and generative AI trends.
- Proficiency in advanced concepts and techniques like Proximal Policy Optimization (PPO) and RLHF for building generative models is a big plus
- Experience prototyping, experimenting, and testing with large datasets and training models.
Education
- MS in EE or CS with 1+ years of relevant experience.
- PhD in EE or CS with published academic papers and/or relevant research experience.