{"id":2065474,"url":"https://alion.io/job/artera-machine-learning-engineer-model-dev","title":"Machine Learning Engineer (Model Dev)","company":{"id":46377,"name":"Artera","domain":"artera.io","url":"https://alion.io/company/artera","size_band":"11-50","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Lever","truth_index":{"grade":"B","score":83,"open_postings":3,"ghost_share":0,"stale_share":0.667,"repost_share":0,"time_to_fill_p50_days":54,"computed_at":"2026-10-12T06:55:09Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":"junior","employment_type":"full_time","work_mode":"remote","remote_scope":"stated_countries","remote_scope_basis":"board_field","remote_working_hours":null,"hiring_geo_confidence":"structured","locations":[],"countries":[],"hiring_countries":["US"],"hiring_countries_total":1,"salary":{"min":140000,"max":180000,"currency":"USD","period":"year","gross":null,"usd_annual":180000},"salary_estimate":null,"experience_years_min":1,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Interpretability","optional":false},{"name":"Machine Learning","optional":false},{"name":"Multimodal AI","optional":false},{"name":"Python","optional":false},{"name":"PyTorch","optional":false},{"name":"Self-Supervised Learning","optional":false},{"name":"TensorFlow","optional":false}],"status":"live","first_seen_at":"2026-10-08T01:58:57Z","employer_posted_date":"2026-10-08","last_verified_at":"2026-10-12T04:43:05Z","board_verified":true,"closed_at":null,"days_open":4,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":4},"description":"We're looking for a machine learning engineer to help develop AI biomarkers that improve cancer care. You'll work closely with experienced ML scientists and engineers, as well as clinical, biostatistics, product, and regulatory partners, across the model-development lifecycle, from prototyping and experimentation through validation and production deployment. You'll contribute to challenging problems in medical AI, including building models from digital pathology and clinical data, improving robustness across scanners and sites, understanding model behavior, and advancing our pathology foundation models.\nEssential Responsibilities:\nDevelop and evaluate AI-based biomarkers using multimodal data, including whole-slide images, clinical variables, and molecular data, to predict patient outcomes, treatment benefit, and molecular traits.\n\nContribute to the development and evaluation of self-supervised foundation models and downstream machine-learning models, including multiple-instance learning, time-to-event / hazard models, segmentation, and classification.\n\nDevelop and evaluate methods to improve model robustness and reproducibility across scanners, institutions, staining protocols, and patient populations.\n\nExplore and apply interpretability methods to explain model decisions, build clinician trust, and drive actionable model improvements.\n\nBuild and improve tools and workflows that support efficient, reproducible model development, experimentation, validation, and deployment.\n\nPerform rigorous model evaluation and analysis, communicate findings clearly, and document experiments and technical decisions.\n\nCollaborate with ML scientists and engineers as well as product, biostatistics, clinical development, and regulatory/quality partners throughout model development and validation.\n\nSupport regulatory and quality documentation related to AI model development and validation.\n\nContribute to peer-reviewed publications, conference presentations, and external academic or industry collaborations.\n\nExperience Requirements:\n1+ years of experience developing machine-learning or deep-learning models using PyTorch (or TensorFlow), including relevant master's or graduate research experience.\n\nFamiliarity with oncology and biomarker development, including cancer biology and treatment pathways, clinical endpoints, risk stratification, and what makes a biomarker clinically actionable.\n\nExperience working with real-world datasets and evaluating machine-learning models using appropriate metrics and validation approaches.\n\nStrong Python programming skills and familiarity with modern software-development practices, including version control, testing, and code review.\n\nAbility to analyze experimental results, troubleshoot model behavior, and communicate findings clearly.\n\nAbility to collaborate effectively with ML engineers, scientists, and cross-functional partners.\n\nDesired:\nExperience working with complex clinical datasets, such as medical imaging, multi-omics, longitudinal patient records, or data from clinical studies and multi-institutional cohorts.\n\nFamiliarity with weakly supervised learning, multiple-instance learning, survival analysis, or related methods.\n\nExperience with self-supervised representation learning or foundation models.\n\nFamiliarity with dataset shift and variation across sites, devices, scanners, or acquisition protocols.\n\nExposure to machine learning in regulated healthcare environments, including SaMD, FDA 510(k)/De Novo, design controls, or CLIA/LDT validation.\n\nResearch experience through publications, conference presentations, internships, or academic projects.\n\nFamiliarity with cloud-based ML development, including distributed training, workflow orchestration, experiment tracking, or reproducible pipelines.","description_format":"text","description_chars":3782,"description_truncated":false,"requirements":{"experience_years_min":1,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":{"level":"master","optional":false},"security_clearance":false,"languages":[]},"benefits":[],"hiring_locations":[{"name":"United States","iso":"US","kind":"country"}],"hiring_excludes":[],"relocation_offered":false,"industries":["Artificial Intelligence","Health Care","AI Agents","Patient Access & 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