Jobs at Immunai, Inc.

Design, build, and maintain scalable pipelines for heterogeneous biological, genomic, single-cell, and clinical data. Enhance data warehouse capabilities, productize scientific algorithms, and develop AI-native data infrastructure. Establish governance for data quality, security, and privacy; collaborate with data scientists and AI/ML engineers; mentor engineers in AI-assisted development; and maintain technical documentation.

Owns end-to-end clinical data ingestion, from initial review through database integration. The role harmonizes and quality-controls clinical trial data, ensures regulatory and contractual compliance, collaborates with clinical, scientific, product, data, and engineering teams, and improves ingestion, mapping, review, and QC workflows using AI and other technical solutions.

Owns the end-to-end ingestion of clinical trial data into Immunai’s platform, including data review, structuring, harmonization, quality control, database integration, and regulatory compliance. Partners with clinical, scientific, data, product, and engineering teams to resolve data issues and define requirements. Develops scalable standards and workflows while using AI tools to automate data review, mapping, harmonization, and quality-control processes.

Manage biological sample acquisition projects from feasibility through delivery and data return. Coordinate academic, biobank, CRO, clinical research, regulatory, and internal stakeholders; track milestones, risks, dependencies, sample quality, timelines, and operational issues. Evaluate sample sources and cohorts, maintain collaboration records, document project scope and status, manage vendor relationships, and enforce quality standards and applicable clinical research and data privacy practices.

Support lab operations and biorepository workflows in an on-site NYC lab: manage incoming samples and chain-of-custody, process biological samples, validate reagents and perform material QC, handle inventory and shipping, maintain lab cleanliness and EHS compliance, and serve as trained backup on the after-hours on-call rotation.

Design, build, and maintain scalable cloud infrastructure for big data and ML workloads. Manage Kubernetes/GKE, Helm, GPU clusters, CI/CD (GitHub Actions), GitOps (Argo CD), observability (Grafana/Loki/Prometheus/Datadog), and ClearML-based MLOps. Collaborate across DevOps, ML, data, and engineering teams to ensure reliable, secure, and cost-aware production environments.

Lead computational biology efforts for external pharma, biotech, and academic projects using single-cell multi-omics. Define objectives with stakeholders, execute reproducible analyses, develop new methods (including LLM-based tools), collaborate with multidisciplinary teams, communicate results via presentations and reports, and drive large-scale initiatives in immuno-oncology and autoimmune disease research.

Lead architecture and scaling of Immunai's data and AI platforms. Build end-to-end MLOps systems, manage GPU infrastructure and data stack (BigQuery, object storage, orchestration), improve developer experience with self-service platforms, mentor and manage a small ML infra team, and partner on security, compliance, and reliability.

Lead AI research and engineering for multimodal foundation models and reasoning/agent systems. Manage and grow teams of researchers, computational biologists, and engineers; own AI roadmap, model design, evaluation, and productionization. Build scalable training/serving pipelines, champion reproducibility, MLOps, CI/CD and cost-efficient GPU/TPU usage. Translate biological questions into impactful AI projects and align cross-functionally.

Lead product strategy and roadmap for Immunai's AI modeling framework, manage a team of product managers, align AI platform development with R&D and business goals, evangelize the product internally and externally, and drive value for internal users and external collaborators in drug discovery.

Hands-on research role developing and training transformer-based and related deep learning models for large-scale single-cell RNA data. Design experiments, perform ablations, analyze results, and collaborate with engineers, data scientists, and biologists to productionize foundation-model methods and ML infrastructure.

Lead computational biology efforts for industry collaborations, design and execute reproducible single-cell and multi-omics analyses, liaise with internal and external stakeholders, communicate results via visualizations and presentations, and develop novel methods to advance immuno-oncology and autoimmune disease research.

Lead design and implementation of LLM-based agentic systems to reason over biological data and literature. Build evaluation frameworks, benchmarks, and datasets; analyze agent behavior and failure modes; collaborate with immunologists and product teams to turn prototypes into production-grade AI tools for therapeutic discovery.

Lead translational immunology projects and advise computational teams; analyze single-cell RNA-seq data (R/Python); translate analyses into biological insights and actionable recommendations; shape bioinformatic platforms; communicate results to stakeholders and perform literature reviews.

Build and maintain benchmarking and evaluation infrastructure for foundation and multimodal models. Design abstractions/APIs, implement metrics, integrate models and baselines, support model development, debug evaluations, and ensure reproducible, versioned workflows.