Staff ML Engineer (Europe-based/Remote)

About the Role

The Applied AI Team is dedicated to delivering digital care that reaches new levels of clinical quality and accessibility. With a strong focus on innovation and excellence, this team is pushing the boundaries of what is possible in AI-powered healthcare. As a key member of this team, you will be expected to drive technical excellence, mentor engineers, and establish best practices.

What You Will Do

• Own complex ML problems end-to-end, taking on the hardest technical challenges from initial exploration through production deployment and real-world iteration
• Define how the company approaches model development, evaluation, and optimization for clinical AI applications
• Build and optimize production LLM systems, designing and implementing fine-tuning pipelines, preference optimization, and rigorous evaluation frameworks for clinical use cases
• Work deeply across the full AI stack, from data preparation and model training through deployment, monitoring, and iteration in production environments
• Partner with Product, Clinical, and Engineering teams to shape technical decisions and ensure the right problems are being solved
• Raise the technical bar by mentoring engineers, establishing best practices, and helping define what excellent ML engineering looks like at Sword
• May lead 1-2 engineers on specific projects, providing technical direction and mentorship
• Develop and maintain expertise in production ML systems, staying up-to-date with the latest advancements and technologies
• Collaborate with the team to identify opportunities for process improvements and implement changes as needed

What We Are Looking For

• Deep expertise in production ML systems, with a track record of solving complex technical problems and shipping ML systems that users depend on
• Production LLM experience, with a strong understanding of how to fine-tune, evaluate, and deploy LLMs in production environments
• Experience with preference optimization, including techniques like RLHF, DPO, or similar approaches
• Strong ML fundamentals, with the ability to design rigorous evaluation frameworks and make principled technical tradeoffs
• End-to-end ownership of complex technical problems, with the ability to take ambiguous problems from initial exploration through production deployment and ongoing iteration
• Excellent engineering skills, with the ability to write production-quality code, understand distributed systems, and debug complex ML pipelines
• Strong communication and influence skills, with the ability to drive technical decisions across teams and explain complex tradeoffs to technical and clinical stakeholders
• Experience working in a fast-paced, dynamic environment, with a strong ability to prioritize and manage multiple tasks and deadlines

Nice to Have

• Experience with healthcare AI or clinical applications, with a strong understanding of the unique challenges of building ML for high-stakes domains
• Track record of designing evaluation frameworks for clinical AI applications
• Experience with agentic workflows and generative AI
• Familiarity with prompt engineering and RAG implementations

Benefits and Perks

• The opportunity to work on complex, high-impact projects that have the potential to revolutionize the healthcare industry
• Collaborative, dynamic work environment with a team of experienced engineers and clinicians
• Professional development opportunities, including training, mentorship, and conference attendance
• Flexible, remote work arrangements, with the ability to work from anywhere in Europe
• Competitive compensation package, with a focus on equity and long-term growth
• Comprehensive benefits package, including health, dental, and vision insurance, as well as retirement savings and paid time off
• Access to the latest tools and technologies, with a focus on staying up-to-date with the latest advancements in AI and ML
• Recognition and reward for outstanding performance, including bonuses and promotions

• When applying, be sure to highlight your experience with production ML systems and LLMs, as well as your ability to drive technical strategy and collaborate with cross-functional teams.
• Showcase your expertise in ML fundamentals, including your understanding of evaluation frameworks and technical tradeoffs.
• Be prepared to discuss your experience with preference optimization and how you have implemented techniques like RLHF, DPO, or similar approaches in production settings.
• Emphasize your ability to work in a fast-paced, dynamic environment and prioritize multiple tasks and deadlines.
• Consider creating a portfolio of your work, including examples of your ML projects and accomplishments, to demonstrate your skills and experience to potential employers.
• Research the company and the role thoroughly, and be prepared to ask informed questions during the interview process.
• Be prepared to discuss your long-term career goals and how they align with the company's mission and vision.