Forward Deployed Engineer | Python | API's | AWS | Docker | Kubernetes | Hybrid | San Francisco, CA
Enigma
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Benefits offered by this job
Job summary
Enigma is seeking a Forward Deployed AI Engineer in San Francisco to bridge cutting-edge AI models with customer workflows, deploying and integrating AI tech within scientific environments. This highly technical, customer-facing role combines deep infrastructure expertise with performance tuning, API integration, and model serving at scale for life sciences.
You will partner with pharmaceutical and biotech teams to understand their environments, deliver production-ready solutions, and capture
Qualifications
- Strong academic background in Computer Science, ML, AI, or a quantitative discipline (BSc/MSc/PhD).
- Experience building systems that interact with large AI models via APIs.
- Hands-on experience designing and maintaining infrastructure for large-scale model serving.
- Experience deploying AI solutions for external customers and translating complex concepts for technical and non-technical stakeholders.
- Strong knowledge of cloud infrastructure, especially AWS; exposure to Azure or Google Cloud.
- Experience with Docker, Kubernetes, CI/CD pipelines, and cloud-native architectures.
- Excellent communication and collaboration skills across technical and business teams.
- Proactive, adaptable, and able to manage multiple customer engagements.
Responsibilities
- Lead end-to-end deployment of AI models into customer environments.
- Design and implement production-ready API integrations, data pipelines, and model-serving infrastructure.
- Work closely with customer engineering and scientific teams to gather requirements, troubleshoot issues, and deliver technical solutions.
- Ensure deployments meet enterprise standards for security, scalability, reliability, and performance.
- Serve as the primary technical contact for assigned customers and build trusted relationships.
- Develop technical documentation, implementation guides, and best-practice resources.
Skills
Education
Tools
Job description
We are looking for a Forward Deployed AI Engineer to serve as the critical bridge between cutting‑edge generative AI models and the customers who rely on them. You will work directly with pharmaceutical and biotechnology organisations to deploy, integrate, and optimise AI technology within their scientific workflows.
This is a highly technical, customer‑facing role that combines deep infrastructure expertise with a passion for solving real‑world challenges in drug discovery and protein engineering.
You will partner closely with customers to understand their technical environments and ensure seamless integration of our generative biology platform into their existing systems. You will own the full lifecycle of customer deployments, from initial technical discovery through production implementation and act as the voice of the customer by providing feedback to internal product, engineering, and research teams.
We are developing next‑generation AI models that advance the understanding and application of biology. Our multidisciplinary team brings together expertise in machine learning, computational biology, software engineering, and scientific research to tackle complex challenges at the intersection of AI and life sciences.
We value scientific excellence, collaboration, continuous learning, and interdisciplinary innovation. Our teams work across multiple international locations, with regular opportunities to collaborate in person and remotely.
We are looking for curious, mission‑driven individuals who are excited by ambitious technical challenges and motivated to create meaningful real‑world impact.
You will ideally have the following:
- A strong academic background in Computer Science, Machine Learning, Artificial Intelligence, or another quantitative discipline (BSc, MSc, or PhD)
- Experience building systems that interact with large AI models through APIs
- Hands‑on experience designing, deploying, and maintaining infrastructure for large‑scale model serving
- Experience deploying AI solutions for external customers and translating complex technical concepts for both technical and non‑technical stakeholders
- Strong knowledge of cloud infrastructure, particularly AWS, with exposure to platforms such as Azure or Google Cloud
- Experience with Docker, Kubernetes, CI/CD pipelines, and cloud‑native architectures.
- Excellent communication and collaboration skills, with the ability to work effectively across technical and business teams
- A proactive, adaptable mindset and the ability to manage multiple customer engagements in a fast‑paced environment
The following would be advantageous:
- Experience applying machine learning within computational biology, protein design, or related life sciences
- Contributions to generative AI research, open‑source software, publications, or production AI systems
- Experience building secure, reliable enterprise software that meets production requirements.
- Familiarity with pharmaceutical or biotechnology environments, including scientific workflows, data governance, or regulatory considerations
- Lead end‑to‑end deployment of AI models into customer environments
- Design and implement production‑ready API integrations, data pipelines, and model‑serving infrastructure
- Work closely with customer engineering and scientific teams to gather requirements, troubleshoot issues, and deliver technical solutions
- Ensure deployments meet enterprise standards for security, scalability, reliability, and performance
- Serve as the primary technical contact for assigned customers
- Build trusted relationships with scientific and engineering stakeholders.
- Gather customer feedback and translate it into actionable recommendations for internal product and engineering teams
- Contribute to product roadmap discussions by sharing insights from real‑world deployments.
- Develop technical documentation, implementation guides, and best‑practice resources
- Stay current with advances in machine learning infrastructure, cloud technologies, and model‑serving practices
- Build domain knowledge in computational biology and related scientific areas relevant to the platform
- Contribute to internal knowledge sharing through technical presentations, documentation, and learning sessions
We offer a competitive compensation and benefits package, which may include:
- Private health insurance
- Retirement or pension contributions
- Generous annual leave and family‑friendly policies
- Hybrid working arrangements
- Opportunities for travel and international collaboration
- A collaborative environment with opportunities to work on cutting‑edge AI applications in life sciences
We are committed to fostering an inclusive workplace and welcome applications from candidates of all backgrounds. We believe that diverse perspectives, experiences, and ideas are essential to building exceptional teams and delivering meaningful innovation.
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