Senior Machine Learning Engineer
Senior Machine Learning Engineer on Atlassian's DevAI team, building AI features for developer products: integrating LLMs, prompt engineering, data preprocessing/feature engineering, running experiments, and implementing and monitoring ML pipelines. Remote-friendly with US geographic pay zones.
- Compensation
- $206.1K–$269.1K / yr
- Location
- San Francisco, CA, US
- Work setup
- Remote
- Seniority
- Senior
- Employment
- Not specified
- Experience
- Not specified
- Category
- Ml / AI
- Posted
Skills
Must have
- LLM integration and prompt engineering
- Develop end-to-end AI features
- Data preprocessing and feature engineering
- Design LLM-based methodologies and algorithms for product needs
- Run experiments and evaluate solutions with metrics and testing methods
- Implement and optimize machine learning pipelines
- Monitor and maintain deployed ML/intelligence solutions
- Collaborate with cross-functional teams (data scientists, software engineers)
Nice to have
- Stay current with ML/AI research and apply cutting-edge techniques
Full description
Overview
Working at Atlassian
Atlassians can choose where they work – whether in an office, from home, or a combination of the two. That way, Atlassians have more control over supporting their family, personal goals, and other priorities. We can hire people in any country where we have a legal entity.
Responsibilities
As part of our team, you will play a crucial role in innovating the core intelligence solutions within our DevAI products. Your responsibilities will include (but not limited to) LLM integration, prompt engineering, and developing end-to-end AI features using cutting-edge technologies. You will collaborate closely with a diverse and highly skilled group of scientists and software engineers within our team, as well as with multiple partner teams across the company. Together, we are committed to delivering Atlassian’s world-class developer AI products that will shape the future of software development.
Responsibility
General Responsibilities
These are ongoing or overarching tasks that guide the strategic and collaborative aspects of the role:
- Problem Solving: Identify complex challenges and provide creative solutions to advance the capabilities and functionalities of the products.
- Collaboration: Work with cross-functional teams, including data scientists and software engineers. Build and maintain sustainable partnerships with team members from various technical backgrounds.
- Communication: Communicate technical details effectively to the team and engage in technical discussions to contribute ideas and solutions.
- Continuous Learning: Stay updated with the latest research and advancements in machine learning, AI, and relevant technology applications. Apply cutting-edge knowledge and techniques to enhance product innovation and effectiveness.
Daily Responsibilities
These tasks are more routine or technical, involving hands-on activities that need to be performed regularly:
- Design and Development: Design LLM based methodology and algorithms tailored to specific product needs.
- Data Handling: Perform data preprocessing and feature engineering to improve product inputs and performance.
- Evaluation: Conduct experiments to evaluate the performance of products and use a variety of metrics and testing methods to ensure the solutions meet design specifications.
- Implementation and Optimization: Implement and optimize machine learning pipelines.
- Monitoring and Maintenance the deployed intelligence solutions to ensure they continue to perform accurately and efficiently.