Senior AI Engineer

Opportunity:

We are seeking a Senior Artificial Intelligence Engineer with a passion for transforming data into intelligent health experiences at the intersection of network medicine, drug discovery, and precision health.

This is a hands-on technical role for someone who operates fluently across computational biology, chemoinformatics, and modern agentic AI — and who can translate complex data into production systems that deliver meaningful scientific outcomes.

The right person will be passionate about architecting and building multi-agent AI systems, knowledge graph pipelines, the integration of KG with advanced AI/ML models, and recommendation engines that power personalized health solutions grounded in systems biology. If you think in graphs, build in code, and publish in journals, we want to talk.

We are seeking candidates who are highly proficient
working with real data and developing end-to-end solutions, and
with demonstrated interest and expertise (via publications or products) in applied artificial intelligence and machine learning, and with expert-level understanding of methods to solve complex health related problems using a range of data types. Your experience must reflect one or more of the following expertise areas: Network Medicine/Pharmacology, Combinatorial Optimization, Deep Reinforcement Learning, AI recommender systems, Graph AI / Neural Networks or Knowledge Graphs, Generative & Mechanistic models for precision therapeutics innovation, MoE Frameworks.

You would join a team of highly accomplished and deeply technical research scientists and engineers focused on delivering cutting edge machine learning technologies to the health space. As a member of this team, you will have the opportunity to leverage your deep understanding of machine learning, artificial intelligence, and data science to solve challenging scientific-technical problems and deliver the next generation of intelligent health experiences.

*Compensation is currently equity-based only;*
*this is temporary, of course.*
We are fundraising while developing an array of ground-breaking technologies.
*S*
alary will commence upon capitalization, and is anticipated in the coming months given the significant interest from VCs. This is an opportunity to join a startup with unicorn potential, as a first-20 member and shareholder, as with opportunities for founding team designation and a direct path for upward mobility into leadership and executive roles

*No C2C arrangements*
, and we are not sponsoring H1B visa at this time.

What You Will Do:

• Architect, build, and maintain agentic AI systems and multi-agent orchestration frameworks for scientific R&D automation
• Design and optimize knowledge graph infrastructure (construction, integration, querying, reasoning) in production environments using graph databases
• Develop and deploy AI models — including fine-tuning, training, and evaluation — for drug discovery, target identification, and combinatorial optimization
• Build and scale recommender systems and precision/personalized medicine pipelines that leverage heterogeneous health data
• Apply network medicine, network pharmacology, and systems biology frameworks to model complex biological interactions and identify therapeutic opportunities
• Integrate chemoinformatics and bioinformatics tooling into automated research workflows
• Establish and maintain production AI applications, infrastructure, and deployment strategies
• Collaborate cross-functionally with scientific, engineering, and product teams to translate research into deployable systems

Required Qualifications:

• Master's degree or Ph.D. in computational biology, bioinformatics, chemoinformatics, biochemistry, network science, or a closely related field — or demonstrably equivalent depth of expertise
• Deep, hands-on experience with network medicine, network pharmacology, or systems biology approaches
• Proven track record building and orchestrating AI agent systems and multi-agent architectures in applied settings
• Strong experience training, fine-tuning, and deploying machine learning and deep learning models
• Production experience with graph databases and knowledge graphs — including schema design, data integration, graph reasoning, and performance optimization
• Demonstrated ability to work with diverse health data sources (genomic, clinical, phenotypic, real-world evidence) for precision or personalized medicine applications
• Proficiency in Python and relevant scientific computing, ML/AI, and graph ecosystem libraries
• Published work or a demonstrable portfolio of prior projects in relevant domains

Strongly Preferred:

• Deep expertise in natural product chemistry, phytochemistry, or natural product drug discovery
• Experience with combinatorial optimization methods applied to drug formulation, synergy modeling, or multi-target therapeutic design
• Hands-on experience building and deploying recommender systems in health or life sciences contexts
• Track record of peer-reviewed publications in network medicine, AI for drug discovery, or related fields
• Experience with LLM-based agent frameworks, tool-use architectures, and retrieval-augmented generation in scientific applications
• Familiarity with ontologies and biomedical knowledge standards (e.g., ChEMBL, UniProt, GO, MeSH, SNOMED)

What Sets You Apart:

• You think natively in networks and systems, not just individual targets or molecules
• You have shipped agentic AI systems that run in production — not just prototypes
• You can move between wet-lab logic and dry-lab execution without losing fidelity
• You treat knowledge graphs as living infrastructure, not static artifacts
• You are energized by the complexity of natural products and multi-compound, multi-target optimization

Must Be:
(i) able to self-govern, (ii) self-actualized & razor focused, (iii) in control of your ego, honest

A strong desire to revolutionize healthcare and an active interest in the welfare of humanity is a
*must*
.