G2i

• Build, maintain, and optimize client-side video playback components • Develop new playback features and improve existing user experiences • Collaborate with frontend, mobile, and backend engineers on architecture and implementation decisions • Participate in code reviews and technical discussions • Contribute to engineering best practices and code quality initiatives • Work on React Native applications across mobile, TV, and web platforms • Help evolve native player foundations using technologies like ExoPlayer, AVFoundation, and FFmpeg • Take ownership of technical challenges and contribute to team-wide initiatives

• Design and execute fine-tuning strategies for structured generation and editing workflows • Build supervised datasets from successful generations, retries, failures, and user edits • Develop measurable benchmarks for generation quality, correctness, and edit preservation • Experiment with open-source models such as Llama, Qwen, Mistral, DeepSeek, or related architectures • Implement LoRA, QLoRA, supervised fine-tuning (SFT), distillation, preference tuning, or synthetic data approaches where appropriate • Build automated pipelines for collecting, cleaning, evaluating, and promoting production data into training datasets • Use validation systems, intermediate representations, runtime analysis, and rendered outputs as structured feedback signals for models • Improve retry, repair, and self-correction workflows for generation pipelines • Collaborate cross-functionally with engineering, design, and product teams to improve model reliability and output quality

• Help train large language models (LLMs) to write production-grade code across a wide range of programming languages • Compare and rank multiple code snippets, explaining which is best and why • Repair and refactor AI-generated code for correctness, efficiency, and style • Inject feedback (ratings, edits, test results) into the RLHF pipeline and keep it running smoothly

• Help train large language models (LLMs) to write production-grade code • Compare and rank multiple code snippets • Repair and refactor AI-generated code for correctness, efficiency, and style • Inject feedback into the RLHF pipeline

• Help train large language models (LLMs) to write production-grade code across a wide range of programming languages: • Compare and rank multiple code snippets, explaining which is best and why • Repair and refactor AI-generated code for correctness, efficiency, and style • Inject feedback (ratings, edits, test results) into the RLHF pipeline and keep it running smoothly • End result: the model learns to propose, critique, and improve code the way you do.

• Help train large language models (LLMs) to write production-grade code • Compare and rank multiple code snippets • Repair and refactor AI-generated code for correctness, efficiency, and style • Inject feedback into the RLHF pipeline

• Help train large language models (LLMs) to write production-grade code • Compare and rank multiple code snippets • Repair and refactor AI-generated code for correctness, efficiency, and style • Inject feedback into the RLHF pipeline and keep it running smoothly

• Build AI workflows for RFP intake, document parsing, extraction, and qualification • Create RAG and semantic search pipelines over RFP documents • Extract structured data like due dates, project scope, contacts, requirements, and submission instructions • Develop FastAPI services and backend APIs • Build human review and approval workflows • Improve reliability, testing, observability, and AI quality

Role Description We’re looking for a Head of Data & Engineering who combines deep technical execution with strong client-facing communication skills. This is not a purely strategic or management-only role — you’ll be expected to actively contribute to architecture, implementation, troubleshooting, and delivery. You should be comfortable jumping into code, pipelines, CDPs, and automation workflows yourself when needed. You’ll lead client engagements end-to-end while helping shape the company’s technical direction across data engineering, AI-enabled delivery, customer data platforms, and marketing operations. What You’ll Be Doing - Technical Leadership & Delivery - Design and implement modern data architectures, pipelines, and customer data systems - Own solution architecture across ingestion, warehousing, transformation, and activation layers - Work hands-on with engineering implementation, integrations, and troubleshooting - Define standards for event tracking, attribution, CDP architecture, and reporting - Evaluate and implement AI-powered workflows and automation opportunities - Client Engagement - Lead technical discovery, scoping, and solution planning with clients - Translate complex technical concepts into clear business language - Serve as a trusted advisor for both technical and non-technical stakeholders - Support technical pre-sales conversations and solution proposals - Team & Process Leadership - Lead and mentor a distributed technical team across multiple regions - Establish scalable delivery processes and QA standards - Drive AI-first ways of working across the engineering organization - Build reusable frameworks, documentation, and operational playbooks Qualifications - ~7–10 years in Data Engineering, Analytics Engineering, MarTech, or related fields - Strong hands-on engineering background — not just architecture oversight - Experience building and maintaining: - Data pipelines / ETL workflows - CDPs and customer segmentation systems - Marketing automation ecosystems - Event tracking implementations - Attribution and analytics layers - Strong communication and stakeholder management skills - Experience working directly with clients in consulting, agency, or multi-client environments - Comfortable operating in ambiguity and fast-paced environments Requirements - Technical Skills - SQL - Python - Node.js - Cloud data platforms / modern data stack - dbt or similar transformation frameworks - CDPs and engagement platforms such as: - Segment - Customer.io - HubSpot - Braze - Klaviyo - Automation tooling (nice to have): - n8n - Make - AI agents / workflow orchestration Nice to Have - Background in RevOps, MarOps, Growth, or Marketing Technology - Experience in startups, scale-ups, or consulting environments - Strong interest in AI-enabled operational workflows and automation Working Style - Highly autonomous and proactive - Comfortable balancing leadership with execution - Fast-moving, iterative mindset - Strong ownership mentality - Collaborative communicator across technical and business teams Hiring Process - Initial Interview (communication, PM, light technical discussion) - Foundational Technical Interview - Take-Home Assignment (3–7 days) - Technical Presentation & Final Interview Additional Notes - Fully remote role - Must be able to work Australian client hours - APAC candidates strongly preferred due to timezone alignment - Hiring ASAP with onboarding targeted before the end of the month

• Design and build the coding benchmarks and evaluation pipelines used to test frontier AI models on real software engineering work: • Design coding benchmarks that evaluate frontier models on real-world programming tasks — reasoning, debugging, and production-quality code • Build and maintain scalable data pipelines for evaluation workflows • Analyze model-generated code for correctness, reliability, and edge-case failures • Construct structured evaluation scenarios across large repos and multi-language environments • Provide detailed technical feedback on model performance and failure patterns • Contribute to evaluation frameworks that set the bar for how coding ability is measured • End result: benchmarks that meaningfully separate what frontier models can and can't do — and shape how the next generation is trained and improved. • AI coding evaluation in one line: Design task → build harness → run model → analyze failures → feed findings back into the benchmark → evaluations that actually distinguish strong models from weak ones.