Job description

About Us Kardigan is a heart health company working to make cardiovascular disease preventable, curable and no longer the leading cause of death in the world. It is Kardigan’s mission to develop multiple targeted treatments in parallel that bring people with cardiovascular diseases to the cures they deserve. Led by Tassos Gianakakos, Jay Edelberg, M.D., Ph.D., and Bob McDowell, Ph.D., Kardigan’s co-founders have reunited after leading MyoKardia to discover and develop mavacamten, the first cardiac myosin inhibitor, resulting in an acquisition by Bristol Myers Squibb in 2020. We have a cutting-edge discovery and translational research platform, a pipeline of late-stage candidates, and an industry-leading team that is driven to improve the lives of patients. At Kardigan, we are motivated by our values which guide how we work, interact, and achieve our goals. Driven by patients and their families , we are deeply committed to improving the lives of patients and prioritizing their needs above all else. We believe in being authentic —leading with truth to bring out the best in others by creating an environment where every person knows they will be fully accepted. With an eagerness to learn , we encourage the highest levels of curiosity and are open to changing our minds. We are committed to winning as a team with urgency, excellence, and intention, and support each other no matter what role we play or where we sit. Lastly, we strive to enable the impossible because patients are counting on us. We are not afraid to take risks to unlock innovation and advance scientific discoveries. These values are the foundation of our work, empowering us to make a real difference, every day. Job Overview The Full Stack Clinical Platform Engineer will design, implement, and operate production-grade Generative AI and Machine Learning solutions that power Kardigan’s Global Development Digital Transformation initiative. This role sits at the intersection of data engineering and applied AI—partnering closely with Clinical Operations, Data Management, Regulatory, and IT to design, build, and maintain the modern data platforms and AI-enabled pipelines that underpin the transformation program. This is 4 -day on-site position (M-Th) Candidates must have experience in Biotech/Pharma/Healthcare The ideal candidate brings deep expertise in Biotech/Pharma or Healthcare clinical data infrastructure and modern data engineering, combined with hands-on experience deploying machine learning solutions in regulated life sciences environments. This role will act as both a technical authority and strategic liaison between transformation projects and enterprise IT, ensuring that solutions are scalable, compliant, and aligned with evolving regulatory and data standards. Key Responsibilities Translate ambiguous clinical and operational problems into well-scoped AI solutions, from problem framing and data assessment through prototype, validation, and production deployment. Design and implement MLOps/LLMOps pipelines to deploy, monitor, and manage large language models in production environments, following software engineering best practices Collaborate with data scientists to deploy and/or fine-tune high-performing Generative AI models, and apply modern techniques from relevant published work where appropriate. Develop scalable and robust data and ML pipelines for ingestion, preprocessing, validation, training, evaluation, and model deployment across the clinical development ecosystem. Evaluate and recommend AI tools and frameworks to meet clinical and operational requirements, including decisions around retrieval-augmented generation (RAG), vector databases, embedding models, and LLM providers, balancing compliance, performance, and cost. Develop, deploy, and maintain robust data pipelines for structured and unstructured clinical data, integrating internal systems with CRO and external partner data sources, and ensuring end-to-end data integrity and traceability. Identify and implement opportunities to increase data interoperability and standardization across Global Development systems and other business units, reducing manual effort and accelerating data availability for clinical programs. Develop and implement automated quality monitoring pipelines for both internal and CRO-sourced clinical data, surfacing quality metrics and triggering corrective workflows in alignment with the study’s Medical Monitoring Plan. Ensure all data solutions comply with applicable regulatory frameworks including HIPAA, GDPR, and 21 CFR Part 11, and contribute to data governance strategy, data lineage documentation, and audit-readiness. Maintain and manage code repositories (e.g., Bitbucket, GitHub) with clean, well-documented, version-controlled code; uphold engineering best practices including code review, testing, and CI/CD pipelines. Qualifications Advanced degree in computer science, biomedical informatics, statistics, or a closely related field required; PhD with 6+ years of relevant experience or MS with 10+ years of relevant experience strongly preferred. Minimum of 5–7 years of experience designing, implementing, and leading data engineering solutions in life sciences or healthcare , with demonstrated accountability for end-to-end delivery is required. Demonstrated expertise in designing and maintaining clinical or biomedical data infrastructure, including data lake and warehouse architectures optimized for regulatory-grade clinical data. Expertise in modern cloud data platforms (Snowflake, Databricks, Redshift, BigQuery) and proficiency in Python, SQL, R, and related programming languages. Proficiency in cloud architecture (AWS, Azure, or GCP) and DevOps practices including CI/CD, containerization (Docker/Kubernetes), and infrastructure-as-code; relevant certifications a plus. Demonstrated experience building, scaling, and maintaining pipelines for structured and unstructured data, with the ability to integrate pipelines across the enterprise. Deep knowledge of regulatory frameworks (HIPAA, GDPR, 21 CFR Part 11) and clinical data standards (CDISC, HL7, FHIR), with experience applying them in regulated development environments. Hands-on experience developing or integrating machine learning pipelines and working with clinical AI/ML applications such as natural language processing, anomaly detection, or predictive modeling. Strong scientific communication skills, with the ability to translate complex technical architectures and outputs into clear strategic recommendations for non-technical clinical and executive stakeholders. Experience with data governance frameworks, data quality tooling, and metadata management practices in clinical or regulated settings. Ideal Candidate Trail A builder at heart — someone who moves fluidly from whiteboard to working solution, comfortable owning the full arc from idea through deployed product. Able to assess a clinical or operational challenge and independently determine whether and how AI can meaningfully solve it — not just implement what's handed to them. A natural cross-functional collaborator who earns the trust of clinical, operations, regulatory, and IT stakeholders alike—comfortable operating as both a technical lead and a strategic partner. Solutions-oriented and innovation-driven, with the confidence to constructively challenge legacy thinking and the pragmatism to deliver within the constraints of a regulated environment. Thrives in ambiguity and fast-moving environments, able to balance long-horizon architectural thinking with near-term delivery commitments across multiple concurrent transformation workstreams. A proactive learner who actively monitors advances in AI/ML, data engineering, and clinical informatics and brings external insights back to accelerate the program’s evolution. Exact Compensation may vary based on skills, experience and location. Pay range $234,000 — $305,000 USD

Requirements

Must-haves

Translate ambiguous clinical and operational problems into well-scoped AI solutions, from problem framing and data assessment through prototype, validation, and production deployment.
Design and implement MLOps/LLMOps pipelines to deploy, monitor, and manage large language models in production environments, following software engineering best practices
Collaborate with data scientists to deploy and/or fine-tune high-performing Generative AI models, and apply modern techniques from relevant published work where appropriate.
Develop scalable and robust data and ML pipelines for ingestion, preprocessing, validation, training, evaluation, and model deployment across the clinical development ecosystem.
Ensure all data solutions comply with applicable regulatory frameworks including HIPAA, GDPR, and 21 CFR Part 11, and contribute to data governance strategy, data lineage documentation, and audit-readiness.
Maintain and manage code repositories (e.g., Bitbucket, GitHub) with clean, well-documented, version-controlled code; uphold engineering best practices including code review, testing, and CI/CD pipelines.
Minimum of 5–7 years of experience designing, implementing, and leading data engineering solutions in life sciences or healthcare , with demonstrated accountability for end-to-end delivery is required.
Demonstrated expertise in designing and maintaining clinical or biomedical data infrastructure, including data lake and warehouse architectures optimized for regulatory-grade clinical data.
Expertise in modern cloud data platforms (Snowflake, Databricks, Redshift, BigQuery) and proficiency in Python, SQL, R, and related programming languages.
Demonstrated experience building, scaling, and maintaining pipelines for structured and unstructured data, with the ability to integrate pipelines across the enterprise.
Deep knowledge of regulatory frameworks (HIPAA, GDPR, 21 CFR Part 11) and clinical data standards (CDISC, HL7, FHIR), with experience applying them in regulated development environments.
Hands-on experience developing or integrating machine learning pipelines and working with clinical AI/ML applications such as natural language processing, anomaly detection, or predictive modeling.
Strong scientific communication skills, with the ability to translate complex technical architectures and outputs into clear strategic recommendations for non-technical clinical and executive stakeholders.
Experience with data governance frameworks, data quality tooling, and metadata management practices in clinical or regulated settings.
Ideal Candidate Trail
A builder at heart — someone who moves fluidly from whiteboard to working solution, comfortable owning the full arc from idea through deployed product.
Able to assess a clinical or operational challenge and independently determine whether and how AI can meaningfully solve it — not just implement what's handed to them.
A natural cross-functional collaborator who earns the trust of clinical, operations, regulatory, and IT stakeholders alike—comfortable operating as both a technical lead and a strategic partner.
Solutions-oriented and innovation-driven, with the confidence to constructively challenge legacy thinking and the pragmatism to deliver within the constraints of a regulated environment.
Thrives in ambiguity and fast-moving environments, able to balance long-horizon architectural thinking with near-term delivery commitments across multiple concurrent transformation workstreams.
A proactive learner who actively monitors advances in AI/ML, data engineering, and clinical informatics and brings external insights back to accelerate the program’s evolution.

Nice-to-haves

Advanced degree in computer science, biomedical informatics, statistics, or a closely related field required; PhD with 6+ years of relevant experience or MS with 10+ years of relevant experience strongly preferred.
Proficiency in cloud architecture (AWS, Azure, or GCP) and DevOps practices including CI/CD, containerization (Docker/Kubernetes), and infrastructure-as-code; relevant certifications a plus.

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

aimachine learningmlllmci/cdpythonsqlawsazuregcpdockerkubernetes

Platform: greenhouse
Effort: quick