About the Role
Chess.com is the global leader in the chess community, serving millions of players ranging from casual enthusiasts to the world’s top Grandmasters. We are looking for a visionary Senior Machine Learning Engineer to join our 100% remote team and help shape the future of our platform. In this role, you will harness the power of artificial intelligence to build insightful, engaging, and highly scalable products that revolutionize how people play, learn, and experience chess. You believe in the transformative potential of AI and are eager to apply your expertise to real-world challenges, turning complex data into intuitive features that resonate with a massive global audience.
As a senior member of our engineering team, you will be an architect of innovation, bridging the gap between theoretical research and production-ready applications. You will collaborate with cross-functional leaders to identify where machine learning can provide the most value, whether through optimizing engine analysis, enhancing fair play systems, or pioneering new interactions via foundation models. We are seeking a high-ownership individual who thrives in a fast-paced environment and is excited to mentor others while scaling our internal MLOps practices and AI literacy across the entire organization.
Key Responsibilities- Take full end-to-end ownership of ML-driven product features, managing the lifecycle from initial opportunity identification through to modeling, deployment, experimentation, and impact analysis.
- Partner closely with Product, Growth, and Data Science leadership to determine high-impact problems for ML solutions, defining success metrics and iterating based on live performance.
- Design, build, and maintain scalable ML systems, including automated data pipelines, training workflows, and high-performance production model serving infrastructure.
- Drive rapid prototyping and iterative development, establishing repeatable evaluation frameworks to test hypotheses and improve model results.
- Shape the long-term ML architecture and MLOps standards, ensuring best practices for experimentation, deployment, monitoring, and retraining as our platform continues to scale.
- Lead innovation in Generative AI, exploring the integration of Large Language Models (LLMs) and agents to power novel user experiences and internal productivity tools.
- Provide technical leadership and mentorship to junior team members, influencing the strategic roadmap and raising the bar for technical excellence within the team.
- At least 5 years of hands-on experience building and scaling complex machine learning systems and training large-scale models in a professional production environment.
- A minimum of 1 year of specialized AI engineering experience, particularly focusing on the modern AI application stack and the lifecycle of foundation models.
- Exceptional technical skills in coding, testing, and software architecture, with a focus on building systems that are resilient, maintainable, and highly scalable.
- Deep practical and theoretical knowledge of the machine learning lifecycle, from data curation and model evaluation to deployment and monitoring.
- Proven ability to operate with a high degree of autonomy and a strong sense of ownership in fast-paced, evolving, and often ambiguous environments.
- Excellent communication and collaboration skills, with the ability to translate complex technical concepts into actionable insights for non-technical stakeholders.
- A fully remote work environment, offering you the flexibility to work from anywhere in the world while maintaining a healthy work-life balance.
- The opportunity to impact a global product used by millions of people every day in the vibrant world of chess.
- A collaborative and inclusive culture that values transparency, innovation, and professional growth.
- Competitive compensation and the chance to lead high-stakes projects at the cutting edge of AI and gaming technology.
- A mission-driven organization dedicated to growing the game of chess and supporting players of all skill levels globally.