• Work with large scale structured and unstructured data, build and continuously improve cutting edge Machine Learning models for Airbnb product, business and operational use cases. • Work collaboratively with cross-functional partners including software engineers, product managers, operations and data scientists, identify opportunities for business impact, understand, refine, and prioritize requirements for machine learning models, drive engineering decisions, and quantify impact. • Hands-on develop, productionize, and operate Machine Learning models and pipelines at scale, including both batch and real-time use cases. • Leverage third-party and in-house Machine Learning tools & infrastructure to develop reusable, highly differentiating and high-performing Machine Learning systems, enable fast model development, low-latency serving and ease of model quality upkeep.

• Design, train, and deploy machine learning models spanning classical ML (classification, regression, clustering, time-series) and generative AI use cases including LLM-based and agentic applications. • Build and maintain cloud-native solutions on AWS using containerized architectures (Docker, Kubernetes) to support scalable model serving and data pipelines. • Own and contribute to the full Model Development Lifecycle (MDLC), including dataset versioning, model versioning, model registry management, and model evaluation frameworks. • Develop and integrate Python-based ML components that work seamlessly with existing product platforms across multiple business units. • Collaborate with AI Product Managers across the Insights BU and partner business units (Provider, Payer, Pharmacy) to translate business needs into AI solutions. • Apply neural networks and deep learning techniques using PyTorch for appropriate use cases alongside scikit-learn-based classical approaches. • Write robust, production-ready code following engineering best practices; participate in code and design reviews. • Leverage AI coding tools (such as Claude Code or equivalent) as part of your daily development workflow to improve velocity and code quality. • Mentor junior engineers and contribute to team knowledge-sharing around ML best practices, tooling, and architecture decisions. • Support integration of frontend components into ML-powered features where applicable. • Contribute to retrospectives and team process improvements; actively participate in sprint planning and end-of-iteration demos. • Adhere to all HIPAA, data governance, confidentiality, and regulatory requirements in all aspects of work. • Maintain compliance with Inovalon’s policies, procedures, and mission statement, fulfilling responsibilities that support operational and financial success.

• Analyze detection data including customer reports to determine which aspects of the machine learning models can be improved • Perform tasks to enable better-management of false positive detections • Analyze binary files to determine their legitimacy • Review current product detections to ensure they are performing to the company standard • Address internal questions and concerns regarding customer threat detections

Role Description Unity is looking for a Senior Machine Learning Infrastructure Engineer to join our Vector Ads team, where we build the real-time systems that power Unity's global advertising platform. This is a high-scale, low-latency environment — processing billions of requests daily to deliver fast, relevant ads to players around the world. You'll build and operate the infrastructure that brings ML models from training into production, ensuring our ranking, bidding, and targeting systems run reliably at scale. This is a great opportunity for an engineer who's excited to work at the intersection of ML systems and distributed infrastructure, collaborate across teams, and have direct impact on how machine learning shapes the player and advertiser experience. What you'll be doing - Design, build, and maintain the infrastructure that serves ML models in real-time across Unity's ads ecosystem - Build and operate scalable model serving pipelines — owning latency, throughput, and reliability in a high-QPS production environment - Partner with ML engineers to productionize models, manage model deployments, and improve iteration speed - Improve observability, performance, and cost-efficiency of ML serving infrastructure - Contribute to architectural decisions around feature serving, model versioning, and inference optimization Qualifications - Experience building and operating ML infrastructure or model serving systems in production - Proficiency in Golang or Python, with strong systems engineering fundamentals - Hands-on experience with Kubernetes and container orchestration at scale - Familiarity with ML serving frameworks such as Ray Serve, Triton, TorchServe, or similar - Understanding of distributed systems, API design, and system reliability - Strong collaboration and communication skills in a remote-first environment Requirements - Experience with feature stores, feature pipelines, or online/offline feature serving - Background in ad tech, real-time bidding, or programmatic advertising systems - Familiarity with infrastructure-as-code such as Terraform - Experience with observability tooling — Prometheus, Grafana, OpenTelemetry - Background with real-time data pipelines, caching layers, or low-latency serving systems Benefits - Comprehensive health, life, and disability insurance - Commute subsidy - Employee stock ownership - Competitive retirement/pension plans - Generous vacation and personal days - Support for new parents through leave and family-care programs - Office food snacks - Mental Health and Wellbeing programs and support - Employee Resource Groups - Global Employee Assistance Program - Training and development programs - Volunteering and donation matching program