The Applied AI Engineer will architect, implement, and maintain AI agents for Edison's platform, collaborating with science and engineering teams and gathering insights from R&D partners to improve the agents' capabilities and reliability.

As a Machine Learning Engineer, you will build models and agents to accelerate scientific discovery, collaborating with researchers and developing efficient infrastructure.

Develop models and algorithms to enhance scientific research using AI agents. Collaborate with a multidisciplinary team and manage research projects end-to-end.

Lead pharma and biotech engagement to generate new business and strategic partnerships, negotiate and close deals, shape go-to-market strategy, represent the company externally, and coordinate cross-functional teams to ensure successful delivery of AI-driven scientific solutions.

Translate client scientific needs into product requirements, prioritize roadmaps with researchers and engineers, define success metrics, and drive cross-functional delivery of AI-powered life-science products while balancing feasibility and client expectations.

Design realistic biological benchmarks and curate high-quality datasets to evaluate and train AI systems; analyze model failures, iterate on data and evaluation criteria, collaborate with ML researchers, and manage workflows and documentation across domain experts.

Lead backend-focused full-stack development for an AI-driven scientific discovery platform: design and maintain services, APIs, data pipelines, cloud infrastructure, CI/CD, observability, and frontend integrations; drive architecture, mentor engineers, and collaborate with research and design teams to deliver production-grade AI capabilities.

Design, build, and operate large-scale Kubernetes clusters and custom operators (CRDs) to orchestrate thousands of persistent AI agent workloads. Drive cluster scaling, autoscaling, scheduling, storage, networking, observability, and IaC. Troubleshoot distributed infrastructure, set best practices, and partner with backend, ML, and research teams to deliver a production-grade platform for long-running scientific workloads.

Build, deploy, and maintain production AI agents for scientific R&D. Work directly with customers to prototype, integrate, troubleshoot, and iterate on agent architectures, prompting, tooling, and evaluation to deliver measurable scientific and operational impact.

Design and implement performant, scalable front-end interfaces and data visualizations for a scientific discovery platform. Build a modular design system, optimize rendering and real-time data flows, and collaborate with product and research teams to translate scientific workflows into intuitive user experiences.

Drive revenue by prospecting, discovery, solutioning, negotiation, and closing biotech and academic customers. Partner with product and engineering to relay feedback, define ICP, messaging, pricing, and repeatable processes. Build trusted early-customer relationships and contribute to go-to-market strategy in an early-stage, ambiguous environment.