{"id":553423,"url":"https://alion.io/job/extropic-thermo-ml-resident","title":"Thermo ML Resident","company":{"id":63280,"name":"Extropic","domain":"extropic.ai","url":"https://alion.io/company/extropic","size_band":"51-200","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Ashby","truth_index":{"grade":"C","score":58,"open_postings":10,"ghost_share":0.7,"stale_share":0,"repost_share":0,"time_to_fill_p50_days":null,"computed_at":"2026-10-29T05:49:05Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":"junior","employment_type":"full_time","work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Boston, United States"],"countries":["US"],"hiring_countries":[],"hiring_countries_total":0,"salary":{"min":75000,"max":200000,"currency":"USD","period":"year","gross":null,"usd_annual":200000},"salary_estimate":null,"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Diffusion Models","optional":false},{"name":"JAX","optional":false},{"name":"Keras","optional":false},{"name":"Machine Learning","optional":false},{"name":"Python","optional":false},{"name":"PyTorch","optional":false},{"name":"TensorFlow","optional":false},{"name":"Kubernetes","optional":true},{"name":"Ray","optional":true},{"name":"SLURM","optional":true},{"name":"Weights & Biases","optional":true}],"status":"live","first_seen_at":"2026-09-07T22:46:08Z","employer_posted_date":"2026-09-08","last_verified_at":"2026-10-29T16:35:35Z","board_verified":true,"closed_at":null,"days_open":51,"trust":{"level":"ok","repost_count":0,"flags":["company_stale"],"days_open":51},"description":"Overview\nExtropic is looking for junior ML scientists to join our residency program on either a part-time or full-time basis. This is a flexible program that can be similar to an internship (minimum 3 months) but we give our residents far more autonomy than most internships.\nOur hardware massively accelerates certain kinds of probabilistic inference, and residents will help pioneer the science of training models in the thermodynamic paradigm.\nResponsibilities\nCollaborate with senior researchers to derive the theory of new probabilistic models and their learning rules, including energy-based models and diffusion models\n\nScale up experimentation infrastructure and optimize over the design space of models\n\nImplement, visualize, and evaluate new architectures, training algorithms, and benchmarks\n\nPublish papers, contribute to open source, and communicate design insights to our hardware team\n\nRequired Qualifications\nExperience in scientific Python with JAX or similar deep learning framework (PyTorch, TensorFlow, or Keras)\n\nStrong foundations in probability and linear algebra\n\nProjects or papers demonstrating hands-on experience in applied machine learning and data science\n\nPreferred Qualifications\nFamiliarity with deep learning theory and literature, including theory of over-parameterization and scaling laws\n\nExperience training energy-based models (EBMs) or diffusion models\n\nExperience with graph neural networks (GNNs) or graph message passing algorithms\n\nExperience with infrastructure for deep learning experimentation and training (Slurm, Ray, Kubernetes, Weights & Biases, etc.)\n\nStrong theoretical background in information geometry\n\nStrong grasp of computational Bayesian methods, including MCMC sampling methods and variational inference\n\nPublications in top ML conferences (NeurIPS, ICML, ICLR, CVPR, etc.)\n\nExtropic is an equal opportunity employer\nThis position will require access to information subject to control under U.S. export control laws and regulations, including the Export Administration Regulations (“EAR”). 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