About the Role
Develop and refine generative machine learning models for practical deployment. Work closely with engineers and domain experts to translate research into scalable solutions that address tangible problems.
Responsibilities
- Design and train generative models tailored to specific application domains
- Iterate on model architectures to improve performance and efficiency
- Collaborate with engineering teams to integrate models into production systems
- Evaluate model outputs against real-world benchmarks and constraints
- Prototype new approaches using both synthetic and real-world datasets
- Optimize inference pipelines for speed and resource usage
- Conduct ablation studies to understand model behavior
- Translate research findings into implementable technical specifications
- Work with domain specialists to define problem scope and success criteria
- Monitor deployed models for performance degradation and data drift
- Document model design decisions and experimental outcomes
- Contribute to version-controlled codebases with clear, maintainable practices
- Participate in peer reviews of model designs and implementations
- Stay current with advancements in generative modeling and related fields
- Assess ethical implications of model outputs and deployment scenarios
- Support the development of safety checks for generative systems
- Help define evaluation metrics aligned with end-user needs
- Troubleshoot model failures in collaboration with engineering staff
- Refine training data pipelines to improve model fidelity
- Balance innovation with practical constraints such as latency and cost
Nice to Have
- PhD in machine learning, artificial intelligence, or related area
- Published work in generative modeling or applied ML venues
- Direct experience with multimodal generative systems
- Familiarity with reinforcement learning from human feedback
- Contributions to open-source machine learning projects
- Experience in scientific or engineering application domains
- Knowledge of safety and alignment techniques in generative AI
- Prior work deploying models in low-latency environments
- Understanding of data curation and labeling pipelines
- Experience mentoring junior researchers or engineers
Compensation
Competitive salary and equity
Work Arrangement
Hybrid
Team
Small, interdisciplinary team focused on rapid experimentation and deployment
Why This Role Matters
Generative models are evolving quickly, but few are effectively deployed in real-world settings. This role focuses on closing the gap between research breakthroughs and practical implementation. You will help shape how organizations adopt generative AI responsibly and effectively, ensuring systems are not only advanced but also reliable, interpretable, and aligned with user needs.
What We Value
We prioritize clear thinking, technical rigor, and a hands-on mindset. Candidates should demonstrate a history of shipping functional systems, not just theoretical work. We value those who ask the right questions, challenge assumptions, and remain focused on real-world impact. Collaboration, adaptability, and ethical awareness are essential traits for success in this position.
Available
