Top Biotech Jobs in San Diego, CA

Manage AI and machine learning products for materials science, including customer demonstrations, roadmap planning, product expansion, ML model development, and leadership of the materials science ML team. The role requires deep expertise in materials science and ML, software-industry experience, customer engagement, public presentations, and global travel.

Own the full healthcare sales cycle, from prospecting and pitching clinics to closing new clients. Lead onboarding, implementation, workflow analysis, and long-term clinic success. Build relationships with clinical, financial, and operational stakeholders, identify growth opportunities, improve enrollment and clinical impact strategies, and resolve client challenges. The role requires persistence, analytical problem-solving, adaptability, and availability to support diabetes care clients as needed.

Leads the technical direction, architecture, and delivery of developer-facing desktop, web, and MCP surfaces for Pfizer’s internal developer platform. Provides hands-on software engineering leadership, ensures secure and privacy-first designs, guides cross-team delivery, mentors engineers, oversees code quality and DevOps practices, and leads incident response and continuous improvement. The role influences teams without direct reports and requires expertise across frontend, desktop, backend, developer tooling, enterprise software delivery, and technical architecture.

Drive new client acquisition and revenue through high-volume lead outreach, sales strategy, CRM pipeline management, client pitches, stakeholder engagement, pricing analysis, and cross-functional collaboration to meet sales targets.

Own and expand customer relationships across the Western United States for a cloud-based healthcare genomics platform. Drive adoption, usage, and account growth within hospitals, laboratories, and healthcare organizations; identify expansion opportunities; navigate complex stakeholder groups; provide account support; and partner with sales teams on customer meetings and commercial development. The field-based role requires approximately 30–40% territory travel.

Leads Inato’s marketing organization and strategy, including account-based marketing for enterprise sponsors, integrated sales campaigns, product positioning, go-to-market execution, customer marketing, and advocacy programs. Establishes marketing goals and metrics tied to business outcomes while partnering with Sales, Product, Customer Success, Strategy, and executive leadership. Coaches and develops the marketing team, builds internal product marketing capabilities, and drives measurable pipeline creation, customer expansion, and market differentiation.

Performs recurring quality checks on commercial life sciences datasets, investigates anomalies, validates Databricks data ingestion and integrations, maintains data governance documentation, collaborates with vendors and stakeholders, and improves quality processes through SQL, automation, and observability tools.

Design and operate enterprise AI infrastructure across AWS and Azure, including federated AI agents, MCP servers, agent orchestration, infrastructure-as-code, security, authentication, monitoring, governance, and AI-enabled development workflows. Integrate LLMs and generative AI, lead adoption of AI-augmented software practices, collaborate with internal teams, and mentor engineers.

Lead product cybersecurity for connected heart-recovery medical devices across development and post-market lifecycles. Responsibilities include security architecture, threat modeling, risk assessments, cryptographic controls, secure boot, firmware protection, PKI, vulnerability management, penetration testing, SBOM analysis, secure OTA updates, regulatory submissions, and remediation planning. The role partners with R&D and cross-functional teams to implement secure development practices, embedded protections, cloud and device security, and compliance with medical-device cybersecurity standards.

Design, develop, and evaluate large-scale multimodal foundation models for biological research. Build hybrid LLM and graph neural network architectures, relational foundation models, and distributed multi-GPU training systems. Apply deep learning theory to biological data, transition research prototypes into scalable production systems, publish peer-reviewed research, and mentor junior staff while collaborating across scientific and engineering teams.