{"id":2327591,"url":"https://alion.io/job/knowtex-ml-engineer-llms","title":"ML Engineer, LLM's","company":{"id":1209,"name":"Knowtex","domain":"knowtex.ai","url":"https://alion.io/company/knowtex","size_band":"11-50","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Work at a Startup","truth_index":null},"role":"AI/ML","role_family":"AI/ML","seniority":"middle","employment_type":"full_time","work_mode":"hybrid","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"explicit","locations":["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":3,"visa_sponsorship":false,"relocation_package":false,"has_equity":true,"technologies":[{"name":"AI Agents","optional":false},{"name":"Fine-tuning","optional":false},{"name":"Function Calling","optional":false},{"name":"Knowledge Distillation","optional":false},{"name":"LLM","optional":false},{"name":"Machine Learning","optional":false},{"name":"Model Distillation","optional":false},{"name":"Post-training","optional":false},{"name":"Python","optional":false},{"name":"PyTorch","optional":false},{"name":"Reinforcement Learning","optional":false},{"name":"SFT","optional":false},{"name":"SOAP","optional":false},{"name":"Tool Use","optional":false},{"name":"AWS","optional":true},{"name":"HIPAA","optional":true},{"name":"NLP","optional":true},{"name":"Quantization","optional":true},{"name":"Speculative Decoding","optional":true},{"name":"TensorRT","optional":true},{"name":"TensorRT-LLM","optional":true},{"name":"vLLM","optional":true}],"status":"live","first_seen_at":"2026-10-12T07:53:44Z","employer_posted_date":"2026-10-12","last_verified_at":"2026-10-12T23:17:12Z","board_verified":true,"closed_at":null,"days_open":0,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":0},"description":"Job title: ML Engineer / Researcher, Large Language Models\nWORK AUTHORIZATION\nMust be authorized to work in the U.S.; we are unable to sponsor visas for this role.\nAbout Knowtex\nKnowtex is building the future of voice AI operating systems for clinicians, transforming how healthcare documentation happens at the point of care. We are growing fast across both commercial health systems and federal healthcare, and our ambient documentation platform is scaling to thousands of clinicians across hundreds of specialties.\nWe are at an inflection point where advances in language models and clinical AI can fundamentally change how clinicians interact with technology. That gives them more time to focus on what matters most: their patients.\nPosition Overview\nWe are hiring an ML Engineer / Researcher focused on Large Language Models to help build the next generation of Knowtex's AI stack.\nYou will develop and optimize the models behind our clinical documentation and structured clinical reasoning, improving quality, cost, latency, and control. Your work will draw on Knowtex's large proprietary clinical dataset, built from real-world encounters across hundreds of specialties.\nThis is a research-heavy role with a direct path to production. You will design experiments, build datasets and evaluation systems, and fine-tune and post-train models. You will also work closely with engineering and clinical teams to deploy successful approaches at scale. The role plays a central part in defining Knowtex's long-term LLM strategy.\nKey Responsibilities\nClinical Documentation & Reasoning\nDevelop and optimize models that generate high-quality clinical documentation, including SOAP notes and specialty-specific note formats\nBuild models for downstream clinical tasks such as medication extraction, orders, ICD-10 coding, E&M coding, patient visit summaries, and other structured clinical artifacts\nApply structured generation, tool use, and agentic approaches to produce reliable, controllable clinical outputs\nModel Strategy & Training\nEvaluate open-weight and proprietary models, and determine where fine-tuning, distillation, structured generation, or task-specific models can beat general-purpose API-based approaches\nFine-tune and post-train open-weight LLMs on Knowtex's proprietary clinical datasets, using SFT, distillation, preference optimization, and reinforcement learning\nResearch ways to reduce inference cost and latency while maintaining or improving clinical quality\nServe and optimize open-weight models in production at scale\nEvaluation\nBuild rigorous evaluation frameworks for clinical accuracy, hallucinations, completeness, formatting, and clinician preferences\nBuild datasets, benchmarks, and evaluation infrastructure that make model improvements measurable and reproducible\nDesign experiments that clearly show whether an approach improves real-world clinical outcomes\nResearch to Production\nMove quickly from idea → dataset → experiment → evaluation → production\nTake successful research beyond prototypes and help deploy models into production\nBalance model quality with latency, inference cost, reliability, and scalability\nCollaborate closely with clinicians, speech and applied ML engineers, and platform engineers\nRequired Qualifications\n2+ years of experience in machine learning research or ML engineering, with deep expertise in large language models\nHands-on experience fine-tuning or post-training open-weight LLMs (e.g., SFT, distillation, preference optimization, RL)\nStrong Python and PyTorch skills\nDeep understanding of modern transformer architectures and LLM training techniques\nStrong experimental methodology and the ability to independently design and execute research projects\nExperience working with large-scale datasets and distributed training environments\nStrong understanding of LLM evaluation and benchmarking\nAbility to translate research results into production systems\nBachelor's, Master's, or PhD in Computer Science, Machine Learning, or a related technical field, or equivalent research experience\nPreferred Qualifications\nExperience building LLM evaluation systems, including LLM-as-judge, human preference, and task-specific benchmarks\nExperience serving and optimizing open-weight models at scale (e.g., vLLM, TensorRT-LLM, quantization, speculative decoding)\nExperience with structured generation, tool use, or agentic systems\nExperience in healthcare AI or clinical NLP\nFamiliarity with clinical documentation workflows and medical terminology\nKnowledge of coding systems such as ICD-10, CPT, E&M, or SNOMED\nPublications at leading ML or NLP conferences\nExperience deploying ML systems in HIPAA-compliant or regulated environments\nExperience in fast-moving startups where researchers own projects from experimentation through production\nTechnical Environment\nAWS\nPython, PyTorch\nOpen-weight and frontier language models\nLarge-scale clinical text and transcript datasets\nDistributed model training and inference\nGPU-based model serving and optimization\nReal-time clinical AI pipelines\nStructured clinical evaluation and benchmarking infrastructure\nCompensation & Benefits\nCompetitive salary\nMeaningful equity compensation\nUnlimited PTO\nPremium health, dental, and vision coverage\n401(k) plan\nWork model: Hybrid, in 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