• Provide vision and technical leadership to guide a team of engineers in designing, delivering, and supporting a robust set of APIs and back-end services. • Collaborate with product management, software engineering, platform, and other cross-functional teams and leaders to iteratively design, develop, and validate outcome-driven technology solutions. • Mentor, coach, train, and support your team and others in their careers and personal growth. • Drive the implementation of functionally appropriate and technically sound solutions that meet all quality & security standards. • Lead the team in achieving ambitious goals, providing regular feedback, and driving continuous improvement.

• As an expert in finance analytics, you will architect and evolve the product finance analytics function • Serve as a strategic finance advisor to the CPO, CTO, and senior leadership team • Lead complex, ambiguous analytical investigations to uncover the drivers behind product growth, engagement, monetization, retention, expansion, and churn • Own the product financial performance management framework • Build and maintain dashboards and reporting tools that deliver timely, trusted product performance and revenue visibility • Write and optimize complex SQL queries to extract and transform large datasets • Build scalable analytics processes and operating models • Uphold data governance and quality standards within the Finance domain

Role Description As a Software Engineer at Nex, you will contribute to building the technical foundations that power our platform's most demanding capabilities. In the ML Engineering track, you will build the infrastructure that accelerates machine learning research: - Training pipelines - Data workflows - Model integration systems - Tools that enable rapid experimentation Your work ensures researchers can iterate reliably and move experiments toward production readiness. The role offers the opportunity to work on deeply technical problems in: - Machine learning systems - Data infrastructure - Sensing technologies - Real-time inference You will be part of a small, highly technical team that values both specialization and collaboration, with clear ownership of core technology areas. The Mindset You are drawn to solving complex technical challenges at the intersection of research and production engineering. You care deeply about building systems that are reliable, performant, and maintainable. You thrive in environments where technical depth matters, where your expertise in: - ML systems - Distributed computing - Real-time software directly shapes what the platform can do. What You’ll Do - Design and build training pipelines, data workflows, and model integration systems - Develop infrastructure that accelerates research iteration and reduces turnaround time - Build systems for data collection, curation, and preprocessing at scale - Create tools and automation that move experiments toward production readiness - Optimize data pipelines for reliability, performance, and observability - Collaborate with ML researchers to understand their needs and remove technical blockers - Work on model serving infrastructure and integration with the production framework - Write clean, well-tested code that maintains high engineering standards - Participate in code reviews and help raise the engineering bar across the team - Contribute to shared tools, infrastructure, and cross-role projects (20% Time) - Work with the dual-leadership model (Engineering Manager and Tech Lead) to understand priorities and technical direction - Document systems and decisions to support team knowledge sharing Qualifications - 3+ years of professional software engineering experience in building production ML systems, training infrastructure, or research platforms - Proficiency in Python, additional experience with at least one other systems language (C++, C#, Java, Rust, or Go) - Hands-on experience with PyTorch or TensorFlow in production or research environments - Experience building or maintaining ML training pipelines or data workflows - Familiarity with model deployment, inference optimization, or MLOps practices Nice To Have - Experience with distributed training systems or GPU-accelerated computing - Knowledge of data versioning, experiment tracking, or ML metadata management - Familiarity with containerization (Docker) and orchestration tools - Contributions to open-source ML projects or research publications - Experience working in small, high-performance technical teams - Background in startups, high-growth environments, or consumer product companies - Passion for pushing technical boundaries and deep problem-solving Benefits - Competitive compensation package - Flexible working hours and vacation policy - Product-driven culture that treasures talents and individual growth - Front-row seat and hands-on experience with cutting edge technologies in the evolving gaming field

• Lead, mentor, and manage a team of software engineers, fostering a culture of collaboration, learning, and accountability. • Drive the technical vision in alignment with product goals, collaborating closely with other engineering and product teams. • Support the career growth of engineers by providing regular feedback, coaching, and development opportunities. • Foster an environment of continuous improvement to identify, develop, and implement process changes to increase efficiency. • Helping the team proactively pay down tech debt by allocating time and resources to refactor and optimise existing systems to ensure long-term stability and scalability. • Evaluate new technology and provide input on best practices in developing proposals and strategies for software design activities. • Grow, develop and retain an incredible team of engineers and foster a culture of excellence and supportive collaboration. • Coordinate tasks and set team goals with a focus on achieving strategic business objectives.