About the Role
We are seeking skilled research engineers to help build and refine advanced language models. The role involves close collaboration between machine learning research and systems engineering to push the boundaries of model performance and deployment.
Responsibilities
- Design and implement machine learning models for large-scale training
- Optimize training pipelines for efficiency and speed
- Collaborate with researchers to translate theoretical concepts into working systems
- Evaluate model behavior and identify areas for improvement
- Develop tools for monitoring and debugging model training
- Contribute to the design of novel architectures and training methodologies
- Work on distributed computing solutions for model scaling
- Ensure reproducibility and rigor in experimental workflows
- Integrate feedback from evaluation teams into model updates
- Support deployment of models in real-world applications
- Participate in setting technical direction for the research team
- Document experiments and share findings across the team
- Troubleshoot issues in training infrastructure
- Improve data curation and preprocessing pipelines
- Assist in benchmarking against existing state-of-the-art models
- Explore methods to reduce computational costs during training
- Contribute to open-source projects related to model development
- Engage in peer review of code and research proposals
- Help define metrics for model reliability and safety
- Collaborate on efforts to interpret model outputs and internal states
- Support efforts to align models with ethical guidelines
- Work closely with engineering teams to productionize research
- Investigate techniques for model fine-tuning and adaptation
- Participate in long-term planning for model iteration
- Stay current with advancements in machine learning and AI research
Nice to Have
- PhD in computer science, machine learning, or related field
- Prior work on large language models or transformer architectures
- Contributions to open-source machine learning libraries
- Experience with model parallelism and scaling strategies
- Background in natural language processing or AI safety
- Publications in peer-reviewed AI conferences
- Hands-on experience with GPU clusters or TPU infrastructure
- Knowledge of reinforcement learning from human feedback
- Experience mentoring junior researchers or engineers
- Leadership in previous research or engineering initiatives
- Familiarity with formal verification methods for AI systems
- Work on model interpretability or transparency techniques
- Involvement in AI policy or governance discussions
- Track record of shipping research into production systems
- Cross-functional collaboration with product or policy teams
Compensation
Competitive salary with equity for early team members
Work Arrangement
Hybrid work model with flexibility to work remotely or from office
Team
Small, interdisciplinary team focused on advancing large language models
Why This Role Matters
You will help shape the foundation of new language models that aim to be more efficient, reliable, and aligned with human values. Your work will directly influence the direction of the team and the capabilities of future systems.
Growth and Impact
As an early member, you’ll have significant influence on technical decisions and research priorities. The team values initiative, and engineers are expected to lead projects and propose new directions.
Collaboration Style
We operate with transparency and frequent feedback. Engineers and researchers work side by side, iterating quickly on ideas and experiments.
Tech Stack
Our stack includes PyTorch, JAX, Kubernetes, and cloud infrastructure. We build custom tooling for training, evaluation, and deployment.
Application Process
We review applications on a rolling basis. Selected candidates will complete a technical exercise followed by interviews with team members.
Visa sponsorship available for qualified candidates
