{"id":697457,"url":"https://alion.io/job/doctronic-senior-ai-engineer","title":"Senior AI Engineer","company":{"id":673395,"name":"Doctronic","domain":"doctronic.ai","url":"https://alion.io/company/doctronic","size_band":"201-500","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Ashby","truth_index":{"grade":"A","score":95,"open_postings":11,"ghost_share":0,"stale_share":0,"repost_share":0,"time_to_fill_p50_days":131,"computed_at":"2026-11-05T05:54:30Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":"senior","employment_type":"full_time","work_mode":"hybrid","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["New York, United States","San Francisco, United States"],"countries":["US"],"hiring_countries":[],"hiring_countries_total":0,"salary":{"min":200000,"max":300000,"currency":"USD","period":"year","gross":null,"usd_annual":300000},"salary_estimate":null,"experience_years_min":7,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"AI Agents","optional":false},{"name":"Fine-tuning","optional":false},{"name":"Function Calling","optional":false},{"name":"Knowledge Distillation","optional":false},{"name":"Model Distillation","optional":false},{"name":"RAG","optional":false},{"name":"Reinforcement Learning","optional":false},{"name":"FHIR","optional":true},{"name":"HIPAA","optional":true},{"name":"HL7","optional":true},{"name":"RLHF","optional":true},{"name":"Synthetic Data","optional":true}],"status":"live","first_seen_at":"2026-07-17T23:44:09Z","employer_posted_date":"2026-07-17","last_verified_at":"2026-11-06T01:56:41Z","board_verified":true,"closed_at":null,"days_open":111,"trust":{"level":"ok","repost_count":0,"flags":[],"days_open":110},"description":"Senior AI Engineer\nNew York City or San Francisco | Hybrid Onsite | Full Time\nMission\nLet's make high-quality healthcare free and accessible to everyone through AI. Doctronic's AI doctor already handles millions of consultations; our goal is to scale to billions while continuously improving clinical safety and accuracy. That means advancing how AI systems reason, learn, retrieve evidence, and earn trust in real clinical care.\nAbout the Role\nDoctronic runs a real clinical practice, with patients consulting our AI doctor every day. That gives us a dataset no one else has to test ideas against. We're committed to publishing and open sourcing as we go.\nYou'll build the reasoning systems, learning methods, retrieval algorithms, and evaluation infrastructure that allow every component of Doctronic's clinical AI to improve with each iteration and earn greater autonomy over time.\nThis role blends research and engineering. What matters is real experimental or modeling work you can also ship, whether you come from applied ML or data science with strong engineering skills, or engineering with a research bent.\nWhat You'll Build\nAgentic Clinical Reasoning\nDesign and implement the next generation of the architecture behind Doctronic's AI doctor, including reasoning, reflection, verification, tool use, routing, uncertainty handling, and escalation.\n\nBuild systems in which specialized agents and models work together to make clinical decisions safely, reliably, and efficiently, with an architecture that supports self-improvement over time.\n\nEvaluation and Measurement\nBuild the evaluation platform, rubrics, simulations, and experiments that measure how the AI doctor performs and know when a benchmark score rewards the wrong behavior.\n\nIdentify what should be improved, whether reasoning, retrieval, model behavior, data, or engineering, and determine whether each intervention genuinely made the system better.\n\nModels and Learning\nApply methods such as fine-tuning, distillation, reinforcement learning, preference optimization, and prompt and system optimization to improve specific components of the AI doctor.\n\nBuild the training data, feedback, reward, and experimentation pipelines that turn evaluation results and clinical expertise into system improvements.\n\nSearch, Retrieval, and Grounding\nBuild search, ranking, retrieval, and grounding algorithms that connect clinical reasoning to trusted medical evidence, partner-specific content, and patient context.\n\nImprove and evaluate retrieval based on its effect on downstream clinical decisions, not only on document-relevance metrics.\n\nWherever you're working, you'll own the problem end-to-end, from framing it to measuring whether it worked.\nHow We Work\nBuilder-first org: senior engineers who take ownership of a problem and run with it\n\nAutonomy here means moving fast on your own judgment and bringing in clinical, product, or engineering partners early when the problem calls for it, not after.\n\nWho You Are\nYou are a research-minded engineer who wants to build intelligent systems and is equally serious about understanding and demonstrating their effectiveness.\n\nYou have deep experience in at least two of the following:\nAgentic architectures, reasoning systems, and tool use\n\nModel evaluation, experimentation, and rubric design\n\nModel training, fine-tuning, distillation, or reinforcement learning\n\nSearch, ranking, retrieval, or RAG\n\nYou have strong ML fundamentals, including training data, objectives, metrics, failure analysis, calibration, and validation.\n\nYou're comfortable working in domains where ground truth is incomplete, and experts disagree, and where the right move is sometimes to question what \"correct\" even means before answering.\n\nYou think through the business impact of your work and scope and prioritize solutions accordingly.\n\nYou have strong engineering fundamentals, you build real systems, not just notebooks or one-off experiments.\n\nYou communicate clearly and collaborate well across engineering, clinical, and product teams.\n\nTypically, candidates will have an advanced degree in a quantitative, computational, or scientific discipline and 3+ years of highly relevant applied or research experience in AI/ML; or 7+ years of relevant experience building and researching ML systems, including recent hands-on work with LLMs and generative AI.\nWe care more about the depth of your work than a specific credential or career path.\nBonus Points\nPublished research, patents, meaningful open-source contributions, or novel production ML systems\n\nExperience building AI systems at an early-stage or high-growth company\n\nExperience in healthcare, clinical AI, or another regulated or safety-critical domain, including familiarity with clinical workflows, healthcare data, or standards such as HIPAA, FHIR, EHR, and HL7\n\nExperience with human-feedback systems, RLHF, simulation, or synthetic-data generation\n\nWork in AI safety, bias detection, calibration, or fairness\n\nWhat We Offer\nCompetitive salary plus meaningful equity with real upside as we grow\n\nBuild AI systems transforming healthcare for millions\n\nHigh autonomy and end-to-end ownership on problems that matter\n\nWork side by side with physician-scientists\n\nHealth benefits\n\nParental Leave: 12 weeks fully paid parental leave for all parents, regardless of gender or path to parenthood - no distinction between birthing and non-birthing parent\n\nFunding and Momentum\nDoctronic is backed by Union Square Ventures, Lightspeed Venture Partners, and Abstract Ventures, with three rounds of financing completed between February 2025 and January 2026.\nDoctronic is an equal opportunity employer. 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