{"id":558939,"url":"https://alion.io/job/air-senior-machine-learning-engineer-2","title":"Senior Machine Learning Engineer","company":{"id":22985,"name":"Air","domain":"air.ai","url":"https://alion.io/company/air-ai","size_band":"51-200","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Greenhouse","truth_index":{"grade":"A","score":95,"open_postings":33,"ghost_share":0,"stale_share":0,"repost_share":0,"time_to_fill_p50_days":253,"computed_at":"2026-10-26T05:57:25Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":"senior","employment_type":"full_time","work_mode":"remote","remote_scope":"stated_countries","remote_scope_basis":"posting_text","remote_working_hours":null,"hiring_geo_confidence":"inferred","locations":["Arlington, United States","Pittsburgh, United States"],"countries":["US"],"hiring_countries":["US"],"hiring_countries_total":1,"salary":null,"salary_estimate":{"min_usd":135000,"max_usd":257000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":370},"experience_years_min":5,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"AI Agents","optional":false},{"name":"AWS","optional":false},{"name":"Azure","optional":false},{"name":"Fine-tuning","optional":false},{"name":"GCP","optional":false},{"name":"Kubernetes","optional":false},{"name":"LLM","optional":false},{"name":"LLMOps","optional":false},{"name":"LoRA","optional":false},{"name":"Machine Learning","optional":false},{"name":"Post-training","optional":false},{"name":"Python","optional":false},{"name":"QLoRA","optional":false},{"name":"SFT","optional":false},{"name":"Synthetic Data","optional":false},{"name":"Multi-Agent Systems","optional":true},{"name":"PEFT","optional":true},{"name":"Reinforcement Learning","optional":true}],"status":"live","first_seen_at":"2026-09-08T19:43:10Z","employer_posted_date":"2026-10-20","last_verified_at":"2026-10-26T20:01:36Z","board_verified":true,"closed_at":null,"days_open":48,"trust":{"level":"ok","repost_count":0,"flags":["company_stale"],"days_open":47},"description":"Company Description\nAir is the leader in Enterprise Readiness. Our mission is to establish readiness as a real-time condition that is continuously achieved. Today, a dangerous Readiness Gap exists between what the front line needs and what is delivered. Our AI-native platform, Air Enterprise Readiness, aligns development, production, delivery, and sustainment into one coordinated execution system for government agencies and industrial suppliers. By revealing true capacity, exposing real constraints, coordinating resources, and executing at the speed of operational demands, the front line gets what it needs to succeed.\nJob Description\nWe are seeking an experienced Senior Machine Learning Engineer to join our AI/ML team and build the infrastructure that powers the development, evaluation, deployment, and continuous improvement of our language models and AI systems.\nAs our AI capabilities expand, we need robust infrastructure for moving models from experimentation into production. This role will own critical parts of that lifecycle, including LLMOps, fine-tuning infrastructure, model evaluation, dataset pipelines, experiment management, model serving, and production observability.\nIn order to do this job well: This is an engineering-heavy ML role. You will build platforms and infrastructure that allow AI engineers and researchers to rapidly experiment with models, datasets, and training techniques while maintaining the reproducibility, scalability, and reliability required for production systems.\nYou will work across the full model lifecycle - from dataset creation and experimentation through training, evaluation, deployment, monitoring, and iteration.\nThis role is a full-time position based in our Pittsburgh, PA office or open to Remote Opportunities.\nThis role may require up to 25% travel, including periodic travel to our Pittsburgh, PA and Arlington, VA offices for team collaboration, planning activities, and in-person meetings.\nScope of Responsibilities\nDesign and build LLMOps infrastructure supporting the development, evaluation, deployment, and continuous improvement of production language models.\nBuild scalable training and fine-tuning infrastructure for commercial and open-weight language models.\nDevelop pipelines supporting supervised fine-tuning, parameter-efficient fine-tuning, preference optimization, and other post-training techniques.\nBuild infrastructure for distributed training and GPU-accelerated ML workloads.\nDevelop data pipelines for training, fine-tuning, evaluation, and synthetic data generation.\nBuild systems for dataset versioning, lineage, quality validation, transformation, and reproducible experimentation.\nDevelop experiment management infrastructure that enables engineers to compare models, datasets, hyperparameters, prompts, and training techniques.\nBuild automated evaluation pipelines that determine whether new models or model versions are ready for production deployment.\nDesign model registries, artifact management, versioning, and promotion workflows across development and production environments.\nBuild and operate scalable model-serving and inference infrastructure for open-weight and fine-tuned models.\nDevelop abstractions that allow product and AI engineering teams to use multiple models and inference providers without tightly coupling applications to a single model or vendor.\nBuild observability for model training and inference, including metrics, tracing, logging, resource utilization, model quality, latency, throughput, and cost.\nOptimize training and inference workloads for GPU utilization, throughput, latency, reliability, and infrastructure cost.\nBuild automated workflows for model deployment, rollback, canarying, and production validation.\nInvestigate model and infrastructure failures across data pipelines, training jobs, inference services, distributed systems, and production environments.\nEvaluate emerging models, training techniques, inference frameworks, and ML infrastructure and determine where they can improve our production systems.\nPartner closely with AI engineers building agentic systems to provide the model, evaluation, and training infrastructure required to continuously improve those systems.\nQualifications\nU.S. Citizenship is required\nRequired Skills: \n5+ years of experience building production machine learning systems, ML infrastructure, distributed systems, or similar technical systems.\nDeep experience designing, building, and operating production ML infrastructure or ML platforms.\nExperience building infrastructure for training, fine-tuning, evaluating, deploying, and monitoring large language models or other large-scale deep learning models.\nExperience with LLM fine-tuning and post-training workflows, including techniques such as supervised fine-tuning, LoRA/QLoRA or other parameter-efficient approaches, and preference optimization.\nStrong understanding of the modern LLM lifecycle, including data preparation, training, evaluation, model artifacts, deployment, inference, monitoring, and iteration.\nExperience building reproducible ML pipelines involving dataset versioning, experiment tracking, model versioning, and automated evaluation.\nExperience building and operating production GPU infrastructure across AWS, GCP, Azure, or dedicated GPU providers, including training and/or inference workloads.\nStrong understanding of distributed systems and the challenges involved in running computationally intensive ML workloads at scale.\nStrong programming experience in Python and experience building production-quality software.\nDeep experience with containers, Kubernetes, and cloud platforms such as AWS, GCP, or Azure.\nExperience designing scalable APIs, services, asynchronous workloads, and data-processing pipelines.\nStrong understanding of observability and operational reliability for production ML systems.\nComfortable debugging failures across training code, datasets, models, GPUs, distributed systems, and cloud infrastructure.\nAble to move between ML experimentation and infrastructure engineering, understanding the needs of researchers and AI engineers while building systems that make those workflows scalable and reproducible.\nComfortable working in a rapidly evolving field where tooling, models, and best practices change quickly.\nDesired Skills: \nCurrent possession of a U.S. security clearance, or the ability to obtain one with our sponsorship\nExperience in or exposure to the nuances of a startup or other entrepreneurial environment\nExperience building secure code execution environments or sandboxes for AI agents.\nExperience with multi-agent architectures, agent-to-agent communication, or distributed agent execution.\nExperience with fine-tuning, post-training, reinforcement learning, or synthetic data generation.\nExperience building AI observability, tracing, and debugging infrastructure.\nExperience optimizing inference latency, throughput, GPU utilization, or model-serving costs.\nExperience with AI security, adversarial testing, or securing agentic systems.\nExperience working in government, defense, or other mission-critical environments.\nWe firmly believe that past performance is the best indicator of future performance. If you thrive while building solutions to complex problems, are a self-starter, and are passionate about making an impact in global security, we’re eager to hear from you.\nAir is an Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, sexual orientation, gender identity, disability and protected veterans status or any other characteristic protected by law.","description_format":"text","description_chars":7671,"description_truncated":false,"requirements":{"experience_years_min":5,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":{"level":"phd","optional":false},"security_clearance":true,"languages":[]},"benefits":[],"hiring_locations":[{"name":"United States","iso":"US","kind":"country"},{"name":"Virginia","iso":null,"kind":"city"},{"name":"Pennsylvania","iso":null,"kind":"city"}],"hiring_excludes":[],"relocation_offered":false,"industries":["Predictive Analytics","National Security","Military Logistics","Supply Chain AI"],"lifecycle":[{"event":"open","at":"2026-09-09T01:44:25Z"}],"visa":[],"liveness":{"score":90,"band":"hot","label":"Hiring now","p_open":1,"p_active":0.903,"p_room":1,"age_days":47,"expected_fill_days":253,"reasons":["conf:0","velocity","win:early"],"computed_at":"2026-10-26T05:57:25Z"},"pay":null,"html_url":"https://alion.io/job/air-senior-machine-learning-engineer-2","json_url":"https://alion.io/job/air-senior-machine-learning-engineer-2.json","meta":{"generated_at":"2026-10-27T00:03:45Z","cache_seconds":300,"methodology":"https://alion.io/methodology","terms":"https://alion.io/terms","contact":"https://alion.io/contact","api":"https://alion.io/developers","about":"Alion is a live layer of people, companies and AI agents: who they are, whether they are real and active right now, what they do and how to work with them, readable by people and by agents and paid per call.","catalog":"https://alion.io/catalog.json","usage":{"tier":"crawler_verified","counted_by":"address","units_charged":1,"used_today":59,"day_limit":null,"remaining_today":null,"minute_limit":300,"resets_at":"2026-10-28T00:00:00Z"}},"offers":[{"id":"company.slices","title":"One company in depth, by slice","status":"live","price":{"credits":0.02,"usd":0.002,"plus_per_slice":{"credits":0.05,"usd":0.005}},"unit":"per company, plus each slice with data","note":"the employer in depth","call":{"mcp_tool":"get_company","arguments":{"id":22985},"rest":"https://alion.io/mcp/rest/get_company?id=22985"},"human":"https://alion.io/catalog?offer=company.slices&for=job%2Fair-senior-machine-learning-engineer-2"},{"id":"market.stats","title":"A market slice: pay, demand and time to fill","status":"live","price":{"credits":1,"usd":0.1},"unit":"per slice","note":"pay, demand and time to fill for this role and place","call":{"mcp_tool":"market_stats"},"human":"https://alion.io/catalog?offer=market.stats&for=job%2Fair-senior-machine-learning-engineer-2"},{"id":"job.search","title":"Open jobs by role, technology, place, pay and visa","status":"live","price":{"credits":0.02,"usd":0.002},"unit":"per posting in a list","note":"similar open postings","call":{"mcp_tool":"search_jobs"},"human":"https://alion.io/catalog?offer=job.search&for=job%2Fair-senior-machine-learning-engineer-2"},{"id":"company.verify","title":"Is this company real and active right now","status":"pilot","price":null,"unit":"per company","request":{"url":"https://alion.io/catalog/request","method":"POST","body":"{\"offer\": \"company.verify\", \"for\": \"job/air-senior-machine-learning-engineer-2\", \"note\": \"what you need it for\"}"},"human":"https://alion.io/catalog?offer=company.verify&for=job%2Fair-senior-machine-learning-engineer-2"}]}