• The Multi-Sport Social Programmer role is responsible for populating Bleacher Report and TNT Sports US social feeds with a focus across Instagram, YouTube, TikTok, X and FB • This individual will be tasked with helping curate and package content from social onto TNT Sports US & Bleacher Report social platforms, including highlights, interviews, studio show content and original and user-generated content • They will also contribute to content and packaging ideation with a focus on social programing optimization • Identify and curate content that will engage with the Bleacher Report Sports Portfolio audience • Edit, package, write titles and captions, and create thumbnails optimized for both vertical and longform content across YouTube, Instagram, TikTok, X and Facebook • Identify and apply latest trends to content • Brainstorm and collaborate with content and programming teams on key initiatives • Cover sports events as they’re happening (weekends/night)

Role Description We are seeking a Software Engineer Instructor to own and evolve our internal technical training program for MLE and SWE engineers. This person will design and deliver curriculum spanning software engineering fundamentals and AI engineering. The role combines live instruction, asynchronous content development, and hands-on coaching to accelerate engineer growth and placement readiness. - Design, build, and maintain the end-to-end technical training curriculum for SWE engineers, covering: - System design - Coding best practices - Software architecture - Testing strategies - AI engineering topics - Deliver live workshops and sessions, and interactive coding labs to groups of engineers at varying seniority levels. - Develop high-quality asynchronous learning content: - Recorded lectures - Written guides - Hands-on exercises - Project-based assessments - Reference materials - Provide 1-on-1 and small-group coaching to engineers, helping them close specific skill gaps and accelerate professional growth. - Track and report on engineer skill progression metrics, using data to identify trends, measure training effectiveness, and prioritize curriculum improvements. - Continuously update training materials to reflect evolving industry standards, new frameworks, emerging AI tools, and client demand patterns. - Collaborate with Engineering Leads and Expertise Group leads to align training content with real-world project needs. - Contribute to onboarding programs to ensure new Factored engineers ramp up quickly on standards, tools, and expectations. Qualifications - 5+ years of professional software engineering experience, with hands-on work in system design, backend/full-stack development, and software architecture. - Demonstrated experience teaching, mentoring, or leading technical training for software engineers (formal or informal). - Experience in deploying and managing Cloud Infrastructure (AWS / GCP / Azure) – AWS Preferred. - Strong proficiency in at least two of: Python, JavaScript/TypeScript, Java, or Go. - Solid understanding of software architecture patterns: microservices, event-driven, layered architecture, and domain-driven design. - Practical knowledge of testing strategies: unit testing, integration testing, TDD/BDD, and test automation frameworks. - Excellent written and verbal communication skills in English; able to explain complex technical topics clearly to diverse audiences. - Experience designing structured curriculums, learning paths, or technical programs. - Proactive and self-directed: Identifies skill gaps, designs interventions, and drives outcomes without waiting for direction. - Comfortable working with data to measure training impact and iterate on content. - Ability to adapt teaching style across seniority levels—from junior engineers to senior developers. - Strong documentation habits: create reusable, well-organized training assets. - Collaborative mindset: works effectively across teams. - Comfort navigating ambiguity and managing competing priorities in a fast-paced environment. Requirements - Strong plus: Proven expertise in integrating Generative AI frameworks and APIs into production-grade applications. - Strong plus: Working knowledge of AI engineering concepts: LLM APIs, RAG patterns, Prompt engineering, Agentic Workflows. - Experience with MLOps tools and pipelines. - Experience creating technical video content or running live-streamed technical sessions. - Prior experience in nearshore, distributed, or remote-first engineering teams. - Contributions to open-source projects, tech blogs, or conference talks. Benefits - Ownership through equity participation. - Annual company retreat. - Education bonus for continuous learning. - Company-wide winter break. - Paid time off. - Optional in-person events and meetups. - Tailored career roadmaps. - High-performance culture.

Role Description We are seeking an experienced Core Architect to define and drive the technical architecture, engineering standards, and long-term technology strategy for advanced AI-powered platforms. In this role, you will lead multiple engineering teams responsible for AI infrastructure, model orchestration, vector knowledge systems, and developer tooling. The ideal candidate will combine deep expertise in distributed systems, AI infrastructure, and cloud-native platforms with strong leadership capabilities to guide engineering teams and deliver scalable, production-ready GenAI solutions. Key Responsibilities - Platform Architecture & Technical Leadership - Define and own the overall architectural vision across data ingestion, vector retrieval, orchestration, inference, and developer tooling. - Establish engineering standards, design patterns, and best practices for building scalable AI platforms. - Lead architecture discussions and design reviews across engineering teams. - AI Systems & Workflow Design - Architect agentic workflows using modern frameworks such as MCP, LangGraph, CrewAI, and related orchestration systems. - Design and optimize retrieval systems, vector knowledge architectures, and multi-tool AI pipelines. - Build scalable frameworks for integrating embeddings, vector databases, and Retrieval-Augmented Generation (RAG) systems. - Engineering Leadership & Mentorship - Lead and mentor engineers across machine learning, AI application development, and full-stack engineering teams. - Provide technical guidance on system scalability, performance optimization, and architecture decisions. - Platform Reliability & Cloud Infrastructure - Define best practices for MLOps, observability, monitoring, and cost optimization across AI platforms. - Architect secure, cloud-native deployments across multiple regions using modern infrastructure frameworks. - Ensure systems meet reliability, scalability, and operational standards. - Cross-Functional Collaboration - Work closely with product, data, and research teams to convert experimental GenAI concepts into production-ready solutions. - Align architecture decisions with product roadmaps and business goals. Qualifications - 10–15+ years of experience in distributed systems, platform engineering, or large-scale infrastructure architecture. - Deep expertise in AI frameworks, vector databases, embeddings, and Retrieval-Augmented Generation (RAG) systems. - Proven experience leading architecture initiatives and scaling complex platforms. Technical Expertise - Strong proficiency with LangChain, LlamaIndex, and related AI orchestration frameworks. - Hands-on experience with vector databases, embeddings, and AI retrieval architectures. - Experience with agentic frameworks and orchestration tools. - Deep expertise in cloud-native environments including AWS, Azure, Kubernetes, and containerized platforms. - Familiarity with Kafka, Terraform, and observability systems for distributed architectures. Key Skills - AI Platform Architecture - LangChain / LlamaIndex - Vector Databases & RAG Systems - Distributed Systems Design - Cloud-Native Infrastructure (AWS / Azure / Kubernetes) - Kafka & Terraform - MLOps & Observability - Scalable AI System Design

• Design and development of data ingestion pipelines (Databricks background preferred). • Performance tune and optimize the databricks jobs. • Evaluates new features and refractors existing code. • Develop and integrate software applications using suitable development methodologies and standards, applying standard architectural patterns, taking into account critical performance characteristics and security measures. • Collaborate with Business Analysts, Technical Manager, Architects and Senior Developers to establish the physical application framework (e.g. libraries, modules, execution environments). • Perform end to end automation of ETL process for various datasets that are being ingested into the big data platform. • Mentor junior developers and be hands on in development work. • Work with QA and automation team. Must have attention to detail to cover all scenarios for testing. • Handles and supports current production applications. • Performs code reviews and discussion on the changes with Technology Manager.