Themis AI is a MIT CSAIL spinoff originating from Prof. Daniela Rus' lab. We have implemented more than 5 years of foundational research into a software framework that automatically estimates uncertainty for any Machine Learning model. Our team is deeply technical and AI-centric; our headquarters are located a block walk away from the MIT campus.
We are post-revenue with Fortune 500 paying customers, VC-backed, and are rapidly growing to hire team members passionate about AI reliability and model deployment.
Position: Machine Learning Engineer (Full-time)
**Location:**Cambridge, MA (or remote)
Role Overview:
As a Machine Learning Engineer, you will play a crucial role in developing our Machine Learning software framework. You will collaborate closely with cross-functional teams to research, develop, and ship software solutions.
Responsibilities:
. Develop robust, scalable, and production-ready code for our Machine Learning software framework.
. Develop and implement state-of-the-art Machine Learning algorithms.
. Collaborate with customers and cross-functional teams to understand business requirements and translate them into technical specifications.
. Conduct thorough testing and evaluation of our software stack.
Minimum Qualifications:
. BS in Machine Learning, Computer Science, Software Engineering, Data Science or related fields.
. Experience in Machine Learning.
. Python proficiency.
. Experience with Machine Learning frameworks like PyTorch, TensorFlow, and JAX.
. Ability to write and test high quality code.
Preferred Qualification:
. Masters or PhD in Machine Learning, Computer Science, Software Engineering, Data Science or related fields.
. 2+ years of industry experience.
. First-author publications at NeurIPS, ICML, ICLR or other top-tier conferences or journals.
. Experience developing Machine Learning software.
. Experience in uncertainty estimation/quantification, uncertainty calibration, Bayesian machine learning, out-of-distribution detection, uncertainty-aware/risk-aware modeling.
. Experience with model evaluation metrics and techniques such as expected calibration error, temperature scaling, Dirichlet calibration, negative log likelihood minimization, Brier scoring, loss-calibrated approximate inference, etc.
. Proven research or practical experience developing Machine Learning algorithms.
. Familiar with version control systems such as Git.
. Strong optimization and debugging skills.
Apply:
More Information:
themisai.org
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