Themis AI is building the next generation of uncertainty-aware AI infrastructure and tools to improve AI safety and reliability. We’re seeking a Machine Learning Engineer to support the deployment, evaluation, and adoption of our flagship product, Capsa, a software platform that enables scalable, real-time uncertainty estimation for deep learning models. This role bridges technical execution and customer success. You’ll co-develop ML solutions alongside external partners, adapting Capsa to novel use cases and helping organizations unlock the full value of uncertainty-aware AI. It’s ideal for engineers who are strong communicators, comfortable working across organizational boundaries, and eager to see their work in production.
Full-Time | Location: Remote (U.S. time zones preferred) or Hybrid (Cambridge, MA)
Responsibilities:
- Design and implement custom ML solutions, including training, evaluation, and model deployment workflows, across diverse and multi-disciplinary use cases
- Effectively communicate complex features and systems in detail while advocating for higher product quality and engineering efficiency
- Build and deliver demos, proof-of-concept systems, and reproducible benchmarks that showcase product value
- Analyze model performance, uncertainty behavior, and calibration outcomes to provide actionable insights to customers
- Collaborate with customers to understand their machine learning pipelines, objectives, and requirements
- Be a team player and a positive influence within the engineering team culture
Qualifications:
- Bachelor's degree in Computer Science, Software Engineering, or a related field
- 3+ years of experience building and evaluating deep learning models in PyTorch and/or TensorFlow
- Solid understanding of ML fundamentals: training, optimization, overfitting, evaluation metrics, model calibration, etc.
- Strong communication skills and prior experience in technical collaboration with cross-functional / multi-disciplinary teams
- Background in statistics, probability theory, and linear algebra
Nice to Have
- Prior experience working in applied domains (e.g., computer vision, time series, or multi-modal models, etc.)
- Professional software development experience (e.g., code reviews, version control, CI workflows)
- Experience in AI safety, reliability, calibration and/or uncertainty estimation (e.g., MC dropout, ensembles, variational methods)
