{"id":1405800,"url":"https://alion.io/job/relativity-advanced-ai-engineer-2","title":"Advanced AI Engineer","company":{"id":2576,"name":"Relativity","domain":"relativity.com","url":"https://alion.io/company/relativity","size_band":"201-500","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Workday","truth_index":{"grade":"B","score":75,"open_postings":3,"ghost_share":0,"stale_share":1,"repost_share":0,"time_to_fill_p50_days":40,"computed_at":"2026-09-30T05:45:00Z"}},"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":"structured","locations":["United States"],"countries":["US"],"hiring_countries":[],"hiring_countries_total":0,"salary":{"min":103000,"max":155000,"currency":"USD","period":"year","gross":null,"usd_annual":155000},"salary_estimate":null,"experience_years_min":3,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Apache Kafka","optional":false},{"name":"AWS","optional":false},{"name":"Azure","optional":false},{"name":"C#","optional":false},{"name":"CI/CD","optional":false},{"name":"Docker","optional":false},{"name":"GCP","optional":false},{"name":"Helm","optional":false},{"name":"Java","optional":false},{"name":"Kubeflow","optional":false},{"name":"Kubernetes","optional":false},{"name":"Machine Learning","optional":false},{"name":"MLFlow","optional":false},{"name":"Prefect","optional":false},{"name":"Pulumi","optional":false},{"name":"Python","optional":false},{"name":"PyTorch","optional":false},{"name":"Quantization","optional":false},{"name":"Spark","optional":false},{"name":"TensorFlow","optional":false},{"name":"Terraform","optional":false}],"status":"live","first_seen_at":"2026-09-28T17:46:13Z","employer_posted_date":"2026-09-28","last_verified_at":"2026-10-01T02:41:56Z","board_verified":true,"closed_at":null,"days_open":2,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":2},"description":"Posting Type\nHybrid/Remote\nJob Overview\nWHO WE ARERelativity is a leading legal data intelligence company building technology that helps users organize data, discover the truth, and act on it with confidence. Our AI-powered, cloud platform, RelativityOne, transforms massive volumes of complex information into actionable insights for litigation, investigations, regulatory inquiries, data breach responses, and other high-stakes legal work where accuracy and trust are crucial.\nThe world’s largest law firms, corporations, and government agencies rely on Relativity’s legal AI software to securely surface and manage the most relevant and impactful information in their matters. Beyond our commercial impact, we are committed to expanding access to technology and supporting pro bono legal work.\nWHAT WE DO\nOur AI team is focused on helping users discover the truth more quickly and act on data with confidence through AI-powered innovation. We are committed to algorithm excellence, building trusted and scalable AI solutions that improve user experiences, products, investigations, and operational efficiency.\nRelativity’s AI and Data Science teams leverage large-scale datasets, modern data infrastructure, and advanced machine learning technologies to deliver insights at scale. Our engineers and data scientists collaborate to build secure, high-performing platforms that support experimentation, innovation, and continuous improvement across the AI lifecycle.\nJob Description and Requirements\nABOUT THE ROLE\nAs an Advanced AI Engineer, you will bridge the gap between core engineering and data science teams, building and evolving the platforms, pipelines, and practices that transform research prototypes into reliable, scalable production solutions.\nYou will own critical components of the machine learning lifecycle, including automated training pipelines, secure deployments, monitoring, and operational excellence. Working closely with Senior and Lead Engineers, you will help drive continuous improvement across Relativity’s AI platform while contributing to technical strategy, implementation, and innovation.\nWHAT YOU’LL DO\nContribute to the design and implementation of ML/AI platforms with a focus on scalability, reliability, security, and standardized GenAI workflows.\nPartner with data scientists, product managers, security teams, and data engineers to deliver high-impact machine learning solutions.\nImplement and improve CI/CD pipelines for machine learning models and data workflows using containerization, infrastructure-as-code, and orchestration technologies.\nBuild and enhance automated model training, deployment, and lifecycle management processes.\nPrototype and evaluate emerging MLOps technologies to improve efficiency, optimize costs, and enable new product capabilities.\nDeploy, monitor, tune, and troubleshoot production machine learning models.\nEstablish and track health, performance, reliability, and cost optimization metrics for AI systems.\nParticipate in code reviews and design reviews while contributing directly to implementation efforts.\nMentor junior engineers and share best practices across the engineering and AI organizations.\nContinuously learn and apply new technologies, tools, and techniques to improve the AI platform.\nWHAT WE’RE LOOKING FOR\nRequired\n3+ years of professional software engineering experience, including at least 1 year working in ML/AI or big data environments.\nProficiency in Python, Java, or C#.\nProduction experience using Docker.\nExperience deploying cloud-based solutions on AWS, Azure, or GCP.\nExperience using infrastructure-as-code tools such as Terraform or Pulumi.\nFamiliarity with workflow orchestration platforms such as Prefect, Airflow, or similar technologies.\nUnderstanding of Kubernetes and Helm fundamentals.\nExperience deploying, monitoring, and troubleshooting machine learning models in production environments.\nAbility to collect and analyze metrics related to model reliability and algorithm health.\nStrong collaboration and communication skills with cross-functional stakeholders.\nPreferred\nBachelor’s degree in Computer Science, Engineering, Mathematics, or a related field.\nMaster’s degree in a relevant discipline.\nExperience with ML lifecycle platforms such as MLflow or Kubeflow.\nExperience with model optimization techniques including quantization, pruning, or compression.\nExposure to distributed data processing technologies such as Spark, EMR, or Kafka.\nFamiliarity with deep learning frameworks such as TensorFlow or PyTorch.\nExperience working in secure and compliant data processing environments.\nWHY WE COULD BE A GREAT FIT\nImpactful Mission\nBuild systems that help customers organize data, discover the truth, and act on it in high-stakes legal matters.\nEngineering at Scale\nWork on distributed, cloud-native systems that process large volumes of data.\nCutting-Edge Technology\nBuild with AI, cloud platforms, and scalable architectures shaping legal tech.\nGrowth and Ownership\nGain experience owning systems end-to-end across cloud and distributed environments.\nCollaborative Culture\nWork in a team focused on knowledge sharing and continuous improvement.\nInclusive Environment\nDiverse perspectives create stronger teams and better outcomes.\nCompensation and Benefits\nCompetitive salary, benefits, DTO, parental leave, and equity program.\nRelativity is committed to competitive, fair, and equitable compensation practices.\nThis position is eligible for total compensation which includes a competitive base salary, an annual performance bonus, and long-term incentives.\nThe expected salary range for this role is betweenfollowing values:\n$103,000 and $155,000The final offered salary will be based on several factors, including but not limited to the candidate's depth of experience, skill set, qualifications, and internal pay equity. Hiring at the top end of the range would not be typical, to allow for future meaningful salary growth in this position.\nRequired Skills:\nEngineering Principle, Hardware Integration, Innovation, Problem Solving, Process Improvements, Quality Assurance (QA), Research and Development, System Designs, Technical Documents, Troubleshooting","description_format":"text","description_chars":6180,"description_truncated":false,"requirements":{"experience_years_min":3,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":{"level":"bachelor","optional":false},"security_clearance":false,"languages":[]},"benefits":["Equity","Parental leave"],"hiring_locations":[{"name":"United States","iso":"US","kind":"country"}],"hiring_excludes":[],"relocation_offered":false,"industries":["Artificial Intelligence","Design & Creative","Document Management","Branding"],"lifecycle":[{"event":"open","at":"2026-09-28T17:46:13Z"}],"liveness":{"score":99,"band":"hot","label":"Hiring now","p_open":1,"p_active":0.993,"p_room":1,"age_days":1,"expected_fill_days":40,"reasons":["conf:30","urgency","velocity","win:early"],"computed_at":"2026-09-30T05:45:00Z"},"pay":{"stated_usd_annual":155000,"is_top_pay":false},"html_url":"https://alion.io/job/relativity-advanced-ai-engineer-2","json_url":"https://alion.io/job/relativity-advanced-ai-engineer-2.json","meta":{"generated_at":"2026-10-01T03:03:31Z","cache_seconds":300,"methodology":"https://alion.io/methodology","terms":"https://alion.io/terms","contact":"https://alion.io/contact","api":"https://alion.io/developers","usage":{"tier":"crawler","counted_by":"address","units_charged":1,"used_today":2527,"day_limit":5000,"remaining_today":2473,"minute_limit":60,"resets_at":"2026-10-02T00:00:00Z"}}}