DataDasher is seeking an innovative and passionate Artificial Intelligence Engineer to join our growing team. We are a Silicon Valley based and venture-backed enterprise software startup that harnesses artificial intelligence to transform financial and wealth management strategy processes. Using DataDasher, the jobs of financial analysts and associates are streamlined and thus their workflows become more efficient and their end-products more comprehensive.
Responsibilities:
- Design, develop, and deploy machine learning models and algorithms to address the business needs of clients and improve DataDasher.
- Perform data preprocessing, feature engineering, and model training to improve model performance and accuracy.
- Develop and maintain clear and concise documentation of machine learning models, algorithms, and processes.
- Evaluate the product tech stack and suggest improvements using knowledge of state-of-the-art machine learning techniques.
We are looking for:
- Hackers: you've built and shipped products, are comfortable moving at a fast pace, and are keenly aware of the best tools to use and when.
- Scrappiness: You are confident in your ability to find solutions, even when resources are limited.
- Product-oriented: You obsess over improvements that can be made and how we can deliver the best product for customers.
- Leaders: You have led engineering teams before and are confident in making a plan and sticking to it, or correcting course if necessary.
- (Bonus) You have experience working in finance or fintech, especially in a startup environment.
Requirements:
- Minimum of 3-5 years of experience in machine learning, data science, or a related field.
- Strong understanding of machine learning algorithms, techniques, and best practices, especially in natural language processing.
- Strong understanding of AI LLM frameworks/tools (i.e. Langchain, vector databases, transformers).
- Proficient in Python and experience with machine learning libraries such as TensorFlow, Keras, or PyTorch. Proficient in fine-tuning models, particularly RLHF (reinforcement learning with human feedback).
- Experience with big data processing tools and frameworks, such as Hadoop, Spark, or Flink.
- Strong analytical and problem-solving skills, with the ability to break down complex problems into manageable tasks.
- Excellent communication and collaboration skills, with the ability to work effectively in cross-functional teams.
- Knowledge of cloud platforms (such as AWS, GCP, or Azure) and experience deploying machine learning models in a production environment is a plus.
- A strong passion for staying current with the latest advancements in artificial intelligence.