Design, build, and deploy AI safety and security solutions directly inside customer environments.
Apply state-of-the-art deep learning methods to turn ambiguous, high-stakes problems into production-grade models and systems.
Implement data pipelines, model serving, and evaluation frameworks.
Adapt and fine-tune models to domain-specific data and performance requirements.
Work with customer teams to understand data constraints, and operational realities.
Translate real-world problems into well-scoped proofs-of-concept and deployment architectures.
Act as the primary AI/ML technical owner for customer projects, rapidly identifying, scoping, and solving technical issues.
Develop proofs of concept quickly to validate technical feasibility and customer value.
Iterate based on user feedback, empirical results, and changing requirements.
Balance speed with long-term maintainability.
Partner with sales, product, research, and platform teams.
Communicate findings, tradeoffs, and recommendations.
Contribute learnings from deployments back into core product and platform development.

Hands-on experience in collaborative environments, working closely with cross-functional teams like data scientists, software engineers, and product managers.
Demonstrated ability to work independently in ambiguous environments and take full ownership of outcomes.
Hands-on experience in building and deploying machine learning models and systems.
Demonstrated expertise in designing, training, and deploying deep learning models with frameworks like PyTorch.
Demonstrated experience programming in Python and C++.
Practical experience in developing scalable machine learning pipelines and integrating them with cloud infrastructure (e.g., AWS, GCP, Azure).
In-depth knowledge of neural network architectures, such as sequence models, transformers, and other state-of-the-art approaches.
Strong algorithmic problem-solving skills and comprehensive knowledge of ML theory and optimization techniques.
Proficiency in data preprocessing and transformation, and handling large-scale datasets with multiple modalities.
Bachelor’s degree in Computer Science, Machine Learning, Engineering, or a related technical field is required.

Forward Deployed Engineer – Machine Learning

Key skills