Role Description This is a senior individual contributor role on the Identity Platform team, responsible for defining and delivering the product strategy for non-human identity. You will own the roadmap for how the platform authenticates, authorizes, and governs machine identities and AI agents alongside the human identities it supports today. This includes vendor evaluation, proof of concept leadership, and build vs integrate decisions, as well as close partnership with security, architecture, AI platform, and engineering teams to establish identity’s role in this space. There is no established playbook—you will create it. You’ll enjoy the flexibility to work remotely from anywhere within the U.S. as you take on some tough challenges. - Own the end-to-end product strategy and roadmap for non-human identity capabilities. - Drives execution by identifying and shaping initiatives across multiple teams, ensuring alignment and progress through influence, rather than direct ownership of delivery. - Define identity’s role across authentication, authorization, and governance for machine identities and AI agents. - Translate ambiguous problem spaces into clear product direction, priorities, and trade-offs. - Lead vendor landscape evaluations and build vs buy decisions. - Drive execution across engineering, security, architecture, and AI platform teams without direct authority. - Communicate strategy and decisions effectively to senior stakeholders. Qualifications - 10+ years of product management experience in platform, infrastructure, or enterprise systems. - Ownership of at least one zero to one product or capability, from definition through delivery or scale. - Experience making enterprise-level product decisions (e.g., vendor evaluations, build vs buy). - Working knowledge of identity systems, including authentication and authorization models (RBAC/ABAC). - Active, hands-on use of AI tools in professional work. - Proven ability to influence without authority across multiple teams. Requirements - Experience with non-human identity, workload identity, or agentic AI. - Experience operating in enterprise scale or regulated environments. - Technical background as an engineer or architect. Benefits - Comprehensive benefits package. - Incentive and recognition programs. - Equity stock purchase. - 401k contribution (all benefits are subject to eligibility requirements). Application Deadline This will be posted for a minimum of 2 business days or until a sufficient candidate pool has been collected. Job posting may come down early due to volume of applicants.

• Design, build, evaluate, and ship agentic based features and interactive experiences to bring our products to the next level • Collaborate with cross functional teams spanning user research, design, data science, product management, and engineering to build new product features that advance our mission to connect artists and fans in personalized and useful ways • Prototype new approaches and productionize solutions at scale for our hundreds of millions of active users • Promote and role-model best practices of ML systems development, testing, evaluation, etc., both inside the team as well as throughout the organization • Actively contributed to a strong community of machine learning practitioners at Spotify

• Design, build, and deploy LLM-powered product features, including lab result summaries, clinical workflow tools, and practitioner-facing conversational agents. • Build backend services that integrate LLMs and ML models into Fullscript’s platform, primarily using Python, with increasing exposure to Elixir as the platform evolves. • Develop AI systems that can support open-ended clinical questions, follow-up interactions, and reasoning over structured and unstructured healthcare context. • Implement prompting, grounding, retrieval, and safety strategies that improve output quality, consistency, and clinical relevance. • Build evaluation, testing, monitoring, and CI/CD workflows for AI features, including approaches for accuracy, hallucination detection, edge cases, and reliability. • Partner with medical, product, analytics, and engineering teams to translate clinical needs into practical AI capabilities that can scale. • Own AI systems end to end, from experimentation and prototyping through production deployment, iteration, and ongoing improvement. • Contribute to architecture and implementation decisions for AI-powered analytics, lab interpretation, and clinical decision-support workflows. • Stay current with fast-moving LLM, agentic AI, and applied ML ecosystems, while staying pragmatic about what is ready for production use.

Role Description Lead Machine Learning Engineers at Thoughtworks use modern architectures to develop end-to-end scalable machine learning systems and applications. They use their specialized depth and breadth of knowledge to impact the achievement of client, project or service objectives and advocate for ways of working to promote and deliver excellence. Their leadership extends beyond technical prowess, encompassing strategic thinking and effective collaboration to drive innovation and deliver solutions that meet and exceed organizational goals. As a lead machine learning engineer on projects, you will be leading the design of technical solutions or perhaps overseeing a program inception to build a new system and/or application. Alongside hands-on coding, as a key influencer, you will shape the trajectory of machine learning engineering initiatives, playing a pivotal role in advancing the field and ensuring impactful outcomes for the broader objectives of the company. - Embrace a strategic mindset, contributing to the direction of machine learning (ML) initiatives and aligning technical solutions with broader organizational goals. - Play a pivotal role in program inception, shaping the development of new systems and applications from idea to reality, overseeing technical feasibility and resource allocation. - Leverage your deep understanding of modern architectures to lead the development of scalable and maintainable ML systems, ensuring optimal performance and efficiency. - Translate client needs into technically feasible and impactful ML applications, driving solution design and deployment within complex, high-stakes projects. - Own the development and maintenance of ML applications, including ML pipelines, model training and deployment, and monitoring and evaluation. - Champion Responsible AI and effective ways of working within the team, advocating for a culture of excellence and continuous improvement. - Navigate intricate technical challenges with proficiency, employing your specialized knowledge to troubleshoot issues and guide the team towards successful resolutions. - Stay at the forefront of the evolving field of machine learning, actively seeking out and implementing new technologies and advancements to ensure Thoughtworks remains a leader in innovation. - Foster a collaborative environment, effectively leading your team through hands-on coding alongside mentorship and guidance, empowering individual growth and knowledge sharing. - Measure and analyze the impact of ML initiatives, iteratively refining approaches and ensuring solutions deliver tangible value to clients and the organization. Qualifications - Experience in developing a technical vision and strategy, keeping it relevant and aligned to the business needs. - Ability to design and execute cross-functional requirements based on business priorities. - Experience in writing clean, maintainable and testable code, demonstrating attention to refactoring and readability of the code using Python or Shell. - Experience with distributed systems and scalable architectures to handle large-scale ML applications. - Experience with building, deploying and maintaining ML systems using relevant ML techniques and platforms, i.e.: Scikit-learn, Tensorflow, MLFlow, Kubeflow, Pytorch. - Experience with application of MLOps principles and CI/CD to ML. - Experience in machine learning engineering and data science, familiar with key ML concepts, algorithms and frameworks, and understand ML model lifecycles. - Experience with designing and operating the infrastructure required to run different types of ML training and serving workloads, i.e.: on-premise vs. cloud infrastructure, infrastructure as code, monitoring, etc. - Hands-on experience with on-premise and cloud services for building and deploying ML pipelines, i.e.: Azure, AWS, GCP or Databricks and associated ML managed services. Requirements - Understand the importance of stakeholder management and can easily liaise between clients and other key stakeholders throughout projects, ensuring buy-in and gaining trust along the way. - Resilient in ambiguous situations and can adapt your role to approach challenges from multiple perspectives. - Willing to take on risks or conflicts and skillfully manage them. - Eager to coach, mentor and motivate others, aspiring to influence teammates to take positive action and accountability for their work. - Enjoy influencing others and advocate for technical excellence while being open to change when needed. - Proven leader with a track record of encouraging teammates in their professional development and relationships. - Natural at cultivating strong partnerships; understand the importance of relationship building and how it can bring new opportunities to our business. Benefits - No one-size-fits-all career path at Thoughtworks; your career development is entirely up to you. - Balance autonomy with the strength of our cultivation culture, supported by interactive tools and numerous development programs. - Value in helping each other be our best and empowering employees in their career journeys.