{"id":1229384,"url":"https://alion.io/job/midjourney-principal-machine-learning-engineer-reconstruction-quantitative-imaging","title":"Principal Machine Learning Engineer (Reconstruction / Quantitative Imaging)","company":{"id":264,"name":"Midjourney","domain":"midjourney.com","url":"https://alion.io/company/midjourney","size_band":null,"is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Ashby","truth_index":null},"role":"AI/ML","role_family":"AI/ML","seniority":"lead","employment_type":"full_time","work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["San Francisco, United 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requirements.\n\nProductionize models: inference performance, reproducibility, monitoring for drift/regressions, and safe fallbacks.\n\nCollaborate on hybrid algorithms, incorporating physics and learned priors, denoisers, learned regularizers, and quality estimation.\n\nHelp build tooling for rapid experimentation as well as rigorous verification of algorithm changes.\n\nWhat we’re looking for\nStrong applied ML experience plus comfort with signal processing / imaging or adjacent domains.\n\nAbility to move fluidly between research prototypes and production-quality systems.\n\nStrong evaluation discipline: metrics, ablations, data leakage avoidance, and reproducibility.\n\nA demonstrated track record of applying ML to physics-based or inverse problems (i.e., shipped projects, a portfolio, or publications.)\n\nUseful experience\nML for imaging/inverse problems (or adjacent) with strong evaluation discipline and comfort with GPU performance constraints.\n\nPragmatic production mindset: reproducible training/inference, regression testing, and safe deployment in high-stakes contexts.\n\nA background in computational physics or scientific computing.\n\nLeverage ML-based methods such as PiNNs and Neural Operators to solve partial differential equations arising in ultrasound simulation and imaging.\n\nExperience in Agentic-SciML is a plus.\n\nHands-on experience with data curation for ML: building datasets from messy, real-world sources, defining ground truth, and managing labeling or simulation pipelines.\n\nBackground in data assimilation: combining observations with physics-based models (Kalman filtering, variational methods, ensemble approaches, or learned 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