Forward Deployed Engineer, Life Sciences

This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Forward Deployed Engineer, Life Sciences based in United States. This role places you at the intersection of software engineering, AI, and life sciences, working directly within highly regulated customer environments. You’ll build and deploy production-grade AI solutions that address complex, high-impact challenges across the pharmaceutical and life sciences sector. As a Forward Deployed Engineer, you’ll combine deep technical expertise with strong customer and solutions instincts. You’ll work across AI workflows, cloud infrastructure, data integrations, MLOps, and interactive applications. Over time, you’ll progress from learning customer environments to independently owning strategic engagements and advising technical and business stakeholders. The role also gives you the opportunity to turn field experience into reusable engineering practices and product feedback that shapes future platform capabilities. This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Forward Deployed Engineer, Life Sciences based in United States. This role places you at the intersection of software engineering, AI, and life sciences, working directly within highly regulated customer environments. You’ll build and deploy production-grade AI solutions that address complex, high-impact challenges across the pharmaceutical and life sciences sector. As a Forward Deployed Engineer, you’ll combine deep technical expertise with strong customer and solutions instincts. You’ll work across AI workflows, cloud infrastructure, data integrations, MLOps, and interactive applications. Over time, you’ll progress from learning customer environments to independently owning strategic engagements and advising technical and business stakeholders. The role also gives you the opportunity to turn field experience into reusable engineering practices and product feedback that shapes future platform capabilities. Accountabilities: Learn the platform, customer environment, technical architecture, data landscape, tooling, and business objectives during onboarding, working closely with experienced engineering colleagues. Progress toward full ownership of strategic life sciences customer engagements, independently managing prioritized technical backlogs in partnership with Engagement Managers. Design, build, test, and deploy production-grade AI and machine learning solutions within customer environments. Deliver solutions across the MLOps lifecycle, including development, deployment, monitoring, and operationalization of models and applications. Build specialized AI inference workflows, custom cloud and data integrations, interactive applications, and solutions supporting areas such as Statistical Computing Environments and clinical CRM. Advise customer data science and engineering teams on effective platform practices, architecture, deployment approaches, and MLOps workflows. Conduct structured discovery conversations and translate complex or ambiguous customer requirements into practical, testable technical solutions. Troubleshoot infrastructure, networking, compute, Kubernetes, cloud, and platform issues in constrained and highly regulated environments. Develop strong relationships with technical and business stakeholders and serve as a trusted technical advisor within strategic accounts. Create reusable playbooks, integration templates, deployment guides, and other resources that improve delivery efficiency across the broader engineering practice. Capture customer and field intelligence and communicate relevant platform opportunities, issues, and requirements to SRE, Support, and Product teams. Contribute to continuous improvement of delivery practices, technical approaches, and reusable solutions across life sciences engagements. Requirements Strong software engineering background with deep proficiency in Python and working familiarity with SQL, R, and Bash. Experience with Kubernetes and managed Kubernetes services such as EKS, AKS, or GKE. Hands-on experience with Docker and cloud architecture across AWS, Azure, and/or GCP. Ability to troubleshoot networking, compute, infrastructure, and platform-level issues in complex environments. Demonstrated experience delivering machine learning workflows, including model deployment and monitoring. Experience with GPU workloads and generative AI, agent frameworks, or related AI technologies. Experience working in, consulting for, or delivering technology within highly regulated or otherwise constrained environments. Ability to navigate requirements involving compliance, data security, infrastructure limitations, and other operational constraints. Strong consultative communication skills, with the ability to engage effectively with both technical and business stakeholders. Experience leading discovery sessions, technical solutioning discussions, and translating complex requirements into buildable solutions. Action-oriented approach with strong ownership, initiative, and comfort working through ambiguity. Ability to become productive quickly within unfamiliar codebases, technical environments, and customer architectures. Strong customer empathy combined with technical depth and practical problem-solving skills. Growth mindset, intellectual curiosity, and commitment to continuous learning and improvement. Ability to collaborate effectively while working independently in fast-moving customer environments. Benefits Remote work opportunity within the United States. Opportunity to work directly with major life sciences and pharmaceutical organizations on high-impact AI initiatives. Hands-on exposure to production AI, machine learning, MLOps, cloud infrastructure, and emerging agent-based technologies. Opportunity to solve complex technical challenges within highly regulated environments. Direct customer engagement and the chance to develop trusted-advisor relationships with technical and business leaders. Significant ownership and autonomy across strategic customer engagements. Opportunities to create reusable technical assets and influence platform and product development through field insights. Learning-focused environment with an emphasis on teaching, knowledge sharing, and professional growth. Collaborative and inclusive workplace that values diverse backgrounds, perspectives, and experiences. Startup-oriented environment offering the opportunity to contribute to evolving technology, processes, and engineering practices. How Jobgether works: We use an AI-powered matching process to ensure your application is reviewed quickly, objectively, and fairly against the role's core requirements. Our system identifies the top-fitting candidates, and this shortlist is then shared directly with the hiring company. The final decision and next steps (interviews, assessments) are managed by their internal team. We appreciate your interest and wish you the best! Why Apply Through Jobgether? Data Privacy Notice: By submitting your application, you acknowledge that Jobgether will process your personal data to evaluate your candidacy and share relevant information with the hiring employer. This processing is based on legitimate interest and pre-contractual measures under applicable data protection laws (including GDPR). You may exercise your rights (access, rectification, erasure, objection) at any time. #LI-CL1