Staff Software Engineer, Headspace Health

• Bachelor's or Master’s degree in Computer Science, Engineering, or a related field, or equivalent hands-on experience
• Exceptional problem-solving abilities with the communication skills to influence decisions across teams and drive alignment
• Proven track record in designing and delivering robust, scalable, and highly reliable services in production environments
• A deep passion for technical excellence, continuous learning, and innovation
• 5+ years of DevOps experience, with hands-on expertise in AWS services, including SageMaker, Lambda, S3, DynamoDB, and IAM
• Proficiency with Infrastructure as Code (IaC) tools such as Terraform and development languages like TypeScript
• Strong background in implementing unit, integration, and end-to-end testing, along with setting up and managing CI/CD workflows
• 2+ years of experience in MLOps, including developing, standardizing, and automating machine learning workflows
• Hands-on experience managing machine learning systems in production, ensuring reliability and scalability
• Proficiency in object-oriented programming and design, particularly in Python
• Experience driving ML initiatives from inception to deployment, with a focus on measurable business impact

Desirable

• In your role as a Staff Machine Learning Engineer, you will take the lead in shaping the vision and architecture of our core ML Platform.
• You will drive key decisions that define the future of how ML systems are built, scaled, and deployed at Headspace.
• By combining your technical expertise with strategic foresight, you will spearhead initiatives that enhance productivity, ensure scalability, and support our mission to improve the health and happiness of the world
• Shape ML Platform Architecture: Drive the design, development, and evolution of our internal ML platform, taking it from high-level vision to robust implementation
• Engineer Scalable Systems: Build and support complex, scalable, and multi-component data and ML systems that integrate seamlessly across the organization
• Automated Model Lifecycle Management: Develop frameworks for continuous retraining of production models, enabling online learning and adaptive system improvements
• Collaborative Problem-Solving: Partner with cross-functional teams to align technical decisions with organizational goals, ensuring cohesive and impactful solutions
• Technical Leadership: Serve as a go-to expert and mentor, exemplifying excellence in AI/ML engineering and inspiring others to pursue technical career growth
• Champion Code Quality: Advocate for and contribute to high-quality engineering standards through rigorous code reviews and constructive, actionable feedback