{"id":3369476,"url":"https://alion.io/job/ardent-principles-machine-learning-engineer-2","title":"Machine Learning Engineer","company":{"id":668946,"name":"Ardent Principles","domain":"ardentprinciples.com","url":"https://alion.io/company/ardentprinciples","size_band":"51-200","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Pinpoint","truth_index":{"grade":"A","score":99,"open_postings":8,"ghost_share":0,"stale_share":0,"repost_share":0.375,"time_to_fill_p50_days":11,"computed_at":"2026-10-29T05:49:05Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":null,"employment_type":"full_time","work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["McLean, United States"],"countries":["US"],"hiring_countries":[],"hiring_countries_total":0,"salary":{"min":130000,"max":260000,"currency":"USD","period":"year","gross":null,"usd_annual":260000},"salary_estimate":null,"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Explainable AI","optional":false},{"name":"Flask","optional":false},{"name":"GitHub","optional":false},{"name":"Hugging Face","optional":false},{"name":"Jenkins","optional":false},{"name":"Keras","optional":false},{"name":"LLM","optional":false},{"name":"Machine Learning","optional":false},{"name":"ONNX","optional":false},{"name":"Python","optional":false},{"name":"PyTorch","optional":false},{"name":"RAG","optional":false},{"name":"Scikit-learn","optional":false},{"name":"Semantic Search","optional":false},{"name":"SQL","optional":false},{"name":"Tableau","optional":false},{"name":"TensorFlow","optional":false},{"name":"Transformers","optional":false},{"name":"Verification & Validation","optional":false}],"status":"live","first_seen_at":"2026-10-27T00:23:50Z","employer_posted_date":"2026-10-27","last_verified_at":"2026-10-29T20:48:44Z","board_verified":true,"closed_at":null,"days_open":2,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":2},"description":"REQUIRED: Active TS/SCI with Full Scope Polygraph\nLOCATION: Full-time onsite in Northern Virginia\nLEVEL: SME\nWe are seeking an experienced Machine Learning Engineer to support the development, tuning, validation, and deployment of advanced machine learning and Large Language Model (LLM) solutions. The ideal candidate will possess hands-on experience with deep learning frameworks such as PyTorch, TensorFlow, or Keras, expertise in experimental design and model evaluation, and the ability to leverage GPU-based computing environments to optimize performance. Must have RAG testing experience.\nWho We Are:\n We offer advanced services in data science, data engineering, software engineering, AI solutions, cybersecurity, staff augmentation, and IT program management.\nPassionate Integrity, Driven by Excellence\nWe provide more than a position-we offer a workplace committed to excellence, integrity, and mission-focused impact. Our mission is to act as a bridge between satisfied clients and fulfilled employees, ensuring that your job and well-being are our top priorities because your satisfaction leads to the success of our clients.\nJoin us as we continue building the future of secure, high-impact solutions.\nKey Responsibilities\nIn this challenging yet rewarding role, you are an integral part of what brings our Company's mission to life. You must have the following required skills, certifications and demonstrated experience in and/or with:\nTuning neural networks, such as LLMs, on custom data sets and applying results to specific use cases.\nProfessional or academic experience developing models and ensembles in the AI/ML space, including selecting the best Python libraries for a given task, choosing appropriate pre-processing actions, performing analysis, and assessing model performance.\nProfessional or academic experience using Python or R.\nProfessional or academic experience with deep learning frameworks such as PyTorch, Tensorflow, or Keras. (Key experience)\nProfessional or academic experience and proficiency with SQL to include using common table expressions, set operations, aggregated functions and nested subqueries.\nProfessional or academic experience with version control systems such as Github and Jenkins.\nLeveraging GPUs for accelerated computing. (Key experience)\nProfessional experience or academic experience in testing, evaluating, or validating the performance of AI systems (with a focus on LLM-based systems. (Key experience)\nProfessional or academic experience in experiment design and execution to test the performance of LLMs against a benchmark. (Key experience)\nMust have RAG testing experience\nHighly Desired Qualifications\nOther skills, demonstrated experiences and certifications which are highly desired but not mandatory to perform the work, include:\nProfessional or academic experience with the HuggingFace Transformers library and hub.\nProfessional or academic experience in the development of benchmarks to measure LLM performance. (Key experience)\nCloud computing development and architecture.\nWorking with front-end web development frameworks such as Flask.\nCreating machine learning models that conduct text classification and topic modeling in Python using standard machine learning (SciKit Learn-) or deep learning models. \nDeveloping applications for semantic search.\nProfessional or academic experience and proficiency with Tableau to produce visualizations and dashboards. \nAcademic or professional experience communicating methodological choices and model results\nWorking with verification and validation test benches.\nWorking with Explainable AI (XAI) techniques.\nWorking with ONNX (Open Neural Net Exchange).\nWhat We Offer You\n SCHEDULE:\nThis is a full-time position with consultants expected to be available and working onsite in Northern Virginia during core hours.\nReady to take the next step in your career? Join us and be part of a team dedicated to driving positive change through technology. Apply now with our easy 3-minute, mobile-friendly initial application process. 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