• Support the team in productionising AI and machine learning solutions • Work closely with and under the guidance of senior engineers • Help integrate AI models into production systems • Collaborate with data scientists, software engineers, and product teams • Deliver robust, scalable AI solutions that enhance our Workday product offerings

Role Description SecureBio is looking for a Senior Software Engineer to build, scale, and run biosecurity evaluations on frontier AI systems. The tools you build will directly inform decisions made by frontier AI companies, policymakers, and researchers to improve the safety of AI systems. Our biosecurity evaluations are regularly used to audit state-of-the-art models for biological capabilities and dual-use biology risks prior to their public release. Our work has been publicly cited in model cards from OpenAI, Anthropic, and Google, and has informed safeguards on frontier models. We are a member of the NIST US AI Safety Consortium, and a partner to the European AI Office. Responsibilities - Perform pre-release and post-release assessments of frontier and open-source AI models using SecureBio's suite of biosecurity evaluations. - Build adapters for models, identify and troubleshoot unexpected agent behaviors, and ensure that our evaluations are methodologically rigorous. - Build, scale, maintain, and continuously improve SecureBio's suite of biosecurity evaluations. - Improve our evaluations to use state-of-the-art agent frameworks, jailbreaking methods, and elicitation strategies. - Develop and improve internal tools that enable our researchers to run capability assessments at scale. - Contribute to and maintain cloud infrastructure to run our evaluations and analyses. - Contribute analyses to our public dashboard of trends in AI biology capabilities ( https://securebio.org/benchmarks/ ). Qualifications - A proven track record in software engineering, including 5+ years of experience in industry roles and/or open-source contributions. - Fluency in Python. - Proficiency with Docker and AWS (particularly ECS). - Experience building software tools that use or evaluate LLMs or AI agents. - Self-directed and comfortable working on small, fast-moving teams. - A commitment to good coding practices, including knowing when and how to use (or not use) coding agents. - Care about code quality, readability, and maintaining human oversight of human- and AI-written code. - Alignment with SecureBio's mission of preventing catastrophic pandemics. Requirements - Experience with AI evaluations (agent systems, scorer and grader design, agent trajectory analysis, jailbreaking or red-teaming). - Experience with Terraform or other Infrastructure-As-Code tools (e.g. Tofu or AWS CDK). - Experience with modern, component-based web frontends (e.g. React with TypeScript). - Experience with our specific tech stack: - Evaluation frameworks: Inspect AI - LLM APIs: OpenAI-compatible, Anthropic, Together, etc. - Agent frameworks: Codex, Claude Code, etc. Benefits - Competitive salary based on experience ($180,000 – $245,000). - Remote work flexibility with office locations in Cambridge, MA and Berkeley, CA. Company Description In joining SecureBio, you'll be joining a motivated, mission-driven, and international team of professionals dedicated to protecting the world from biological threats. Through in-house research & development, as well as collaborations with experts in academia, industry & government, we are working to build the tools, technologies, and institutions the world needs to be secure against future biotechnology. Major projects by the SecureBio team include the SecureBio Detection project for reliable pandemic detection and our work to understand and reduce biological risks from advanced artificial intelligence. Application Process We will review applications on a rolling basis, and are looking for someone to start as soon as possible. After considering your written application, we may ask you to complete a short coding test, and invite you to a short screening interview. High-performing candidates may be invited to further interviews and/or paid trial tasks. We will also require up to two reference checks. We're committed to fostering a culture of inclusion, and we encourage individuals with diverse backgrounds and experiences to apply. We especially encourage applications from women, gender minorities, and people of color who are excited about contributing to our mission, and are happy to consider applicants from overseas. We're an equal opportunity employer. If you need assistance or an accommodation due to a disability, or have any other questions about applying, please contact careers@securebio.org .

Role Description AI Engineer with demonstrated production-quality experience in: - Designing, building, and deploying AI/ML solutions in the Azure ecosystem, including Azure ML, Azure OpenAI, and Azure AI search. - Python and SQL fluency in production environments. - 5+ years delivering production-grade classical ML/AI. - 1-2 years building LLM systems in production, including: - API integration, batch and/or streaming pipelines. - Caching, cost management, rate limiting, and token tracking. - RAG architectures. - Tool use and agentic workflows (LangChain/LangGraph). - Designing and operating production APIs with authentication, authorization, logging, monitoring, and observability. - Hands-on experience with vector databases and semantic search, e.g., Azure AI Search (indexing, skillsets, semantic search, filters, etc.). - Building Chatbot and assistant solutions, including: - API endpoints and session management. - Secure authentication. - Feedback loops and continuous improvement - nice to have. - Context management strategies, memory, and session behavior. - Collaborating with Data Engineers to source, prepare, and label datasets. - Designing and implementing MLOps workflow, including: - Model/version control. - CI/CD deployment. - Monitoring and observability. - Exposure to classical ML pipeline alongside GenAI systems. Company Description

Role Description Drive the execution and delivery of AI-powered features, use cases, and platform capabilities across a suite of SaaS products. - Translate business and product team needs into concrete technical requirements, stories, and sprint deliverables for engineering and data science teams. - Coordinate with cross-functional squads to embed AI and automation into existing workflows (e.g., recruiting, onboarding, performance, learning, analytics, and public safety). - Define and manage the backlog for AI feature delivery, balancing short-term wins with long-term platform scalability. - Establish execution roadmaps and milestones for model training, integration, and release across multiple product teams. - Track delivery KPIs (velocity, cycle time, release quality, adoption metrics) and drive continuous improvement in execution efficiency. - Support post-deployment validation to ensure model accuracy, system reliability, and user experience alignment. Company Description