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Themis AI is building the next generation of uncertainty-aware AI infrastructure and tools to improve AI safety and reliability. We’re looking for a Staff Machine Learning Engineer to lead the development of the high-performance libraries that form the foundation of our product offerings. This role is ideal for engineers who have deep experience building robust Python-based ML libraries and want to shape core systems that enable trustworthy AI deployments for novel and high-impact use cases.

Full-Time | Location: Remote (U.S. time zones preferred) or Hybrid (Cambridge, MA)

Core Responsibilities:

• Support the design, development, optimization and testing of our core ML libraries
• Collaborate closely with AI research teams to translate new innovations into high-performance, easy-to-use APIs and scalable systems
• Contribute to architectural decisions and long-term technical roadmaps
• Drive strong engineering practices: code reviews, automated testing, CI/CD, docs, etc.
• Mentor engineers and help maintain a culture of technical rigor and mission alignment

Requirements:

• 5+ years of software engineering experience in Python and ML model development
• Deep understanding of machine learning fundamentals, particularly neural networks, optimization, probability theory, and linear algebra
• Strong software engineering practices: Git-based workflows, pull request reviews, testing infrastructure, release processes
• Major contributions to production-grade AI/ML frameworks, toolkits, or open-source tools
• Clear communicator with the ability to operate autonomously and prioritize effectively in a startup environment
• Experience working in high-performance, collaborative software teams
• Mission-driven mindset: you care deeply about building systems that matter

Nice to Have:

• Experience in uncertainty quantification, Bayesian neural networks, probabilistic models
• Contributions to major ML or scientific computing libraries (e.g., PyTorch, NumPy, SciPy)
• Exposure to GPU programming, compiler-level optimization, and distributed systems

At Themis, we believe that uncertainty is essential for building trustworthy, adaptive, and resilient AI. If you’re excited about enabling the future of safe and efficient machine learning and building tools that top AI teams rely on, we want to hear from you.

To apply, email us with the button below and include your resume, GitHub portfolio (if relevant), and a brief note explaining your interest in the role.

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)