Principal ML Engineer (Applied/Systems)

About Soris We are a stealth technology company building at the intersection of foundational AI and systems design for real-world impact. Our mission is bold: to reimagine one of the largest and most entrenched industries on the planet, creating an entirely new model that is faster, fairer, and fundamentally better for humanity. We are building a full-stack AI platform from the ground up; one that is agentic, vertically integrated, and designed for scale. Our founding team has scaled category-defining companies in frontier tech, aerospace, mobility, and AI. We’ve led organizations from zero to multibillion-dollar valuations, pioneered new industries, and built products that reshaped how people live and move. Now we’re assembling a small, fiercely talented team to take on our most ambitious challenge yet. Joining us means working on cutting-edge research with direct application, where your code doesn’t sit in a lab, it changes lives. You’ll be part of an environment that values intellectual rigor, creative rebellion, and the courage to build what others say is impossible. This is a once-in-a-generation opportunity to help design and deploy the infrastructure of the future. We’ve raised capital in record time, and are scaling our foundational team now. If you’re motivated by hard problems, high impact, and the chance to leave a legacy, we’d love to talk. About the Role We are seeking a highly experienced Principal ML Engineer (Applied / Systems) to join our engineering team and report directly to the CTO. This is a leadership-oriented/cross-functional role for a seasoned engineer with deep expertise building proprietary models and turning them into robust, scalable production systems that serve real-world needs. As a Principal ML Engineer at Soris, you will be responsible for driving the end-to-end development and deployment of machine learning and computer vision systems at scale. You will partner with engineering, product, and business stakeholders to prototype rapidly, validate findings, and ship reliable model-powered solutions, all with a strong focus on performance, maintainability, and impact. What You’ll Do Lead design, development, and deployment of machine learning systems from prototype to production, with direct impact on product outcomes. Rapidly experiment with large language models (LLMs), computer vision systems, NLP pipelines, and other ML techniques; validate results and productionize successful approaches. Build, optimize, and scale inference pipelines and model serving infrastructure on AWS. Collaborate closely with backend, data, and platform teams to integrate ML systems into the larger engineering stack. Establish best practices for model versioning, monitoring, performance optimization, reliability, and observability. Influence technical strategy and drive long-term architectural decisions for ML infrastructure. Present complex technical ideas, innovations, and tradeoffs clearly to executives, product partners, and non-technical stakeholders. Mentor and lead other engineers; help grow the ML engineering capability across the organization. Requirements 12+ years of engineering experience with significant focus on machine learning systems, model deployment, and production readiness. PhD or Master's degree in Computer Science, Statistics, or a related field. Strong publication record in top machine learning conferences or journals (e.g., NeurIPS, ICML, ICLR, CVPR, ECCV) or demonstrated experience in relevant engineering roles. Demonstrated success in deploying ML models into production, especially for NLP, computer vision, and large language model applications. Strong proficiency in Python; additional experience in Golang and/or C++ is a strong plus. Breadth of experience with cloud platforms and familiarity with managed inference services, orchestration, and scaling. Deep understanding of ML tooling and practices, including model serving frameworks, monitoring, pipelines, and CI/CD. Excellent communicator, comfortable explaining technical work to executives, partners, and diverse audiences. Proven leadership experience, including mentoring engineers and driving technical initiatives across teams. A bias toward action, with the ability to operate effectively in ambiguous environments. Nice to Have Experience with feature stores, model registries, or MLOps platforms. Background working in early-stage startups or high-growth environments. Prior work shipping products used at scale in production. Experience with AWS. Why Join Us You’ll be part of a team that values speed, quality, and impact, working on cutting-edge ML systems that matter. Your work will influence product direction, architecture, and real-world outcomes.