Principal Engineer, Machine Learning

Your experience includes… 

• Bachelor's or Master's degree in Computer Science, Software Engineering, or related field with 8+ years of industry experience in software, data, and machine learning engineering
• Proven track record in deploying and auto-scaling ML pipelines for sparse, high-dimensional data across multiple data and model scales
• Strong proficiency in Python, SQL, and cloud computing platforms (AWS, Lambda, Modal)
• Extensive experience with ML frameworks such as PyTorch, TensorFlow, or JAX
• Familiarity in data and model integration techniques built on heterogeneous data types
• Proficiency in containerization (Docker) and orchestration (Kubernetes, Dagster, Airflow, Slurm) for ML workloads
• Experience with MLOps tools such as MLflow, Kubeflow, and Ray
• Solid understanding of engineering best practices including devops, version control systems (Git) and CI/CD pipelines (Jenkins, GitLab CI)

It's a bonus if you have:

• Experience with databases including relational, vector and graph databases (e.g., GraphQL) or knowledge graphs for modeling complex relationships
• Familiarity with distributed computing frameworks like Apache Spark
• Knowledge of federated learning
• Experience with ML engineering in the context of drug-discovery and/or gene-therapy

You are interested in…  

• Developing innovative solutions for integrating sparse, high-dimensional data from various biological scales (e.g., genomic, proteomic, cellular)
• Designing and implementing ML pipelines that ensure full traceability from raw data to model predictions
• Creating robust data versioning and lineage tracking systems (e.g., using DVC) to support reproducible research
• Implementing best practices in MLOps, with a particular focus on model versioning, provenance, and auditing
• Developing user-friendly interfaces (e.g., Flask) that allow non-computational experts to interact with ML models
• Exploring and implementing novel approaches to handle the challenges of sparse data in ML models, including transfer learning and few-shot learning techniques
• Optimizing ML models for deployment on edge devices or in resource-constrained environments

About you: 

You are an experienced machine learning engineer with a passion for tackling the unique challenges posed by sparse, high-dimensional biological data in gene-therapy. Your expertise spans the entire ML engineering stack, from ETL to model deployment and auto-scaling, with a focus on creating robust, reproducible ML pipelines. You excel at designing systems that handle sparse data effectively, applying advanced techniques to extract insights from limited data. Your proficiency in MLOps tools, containerization, and orchestration enables you to create scalable, reproducible environments for both development and production. As a Principal Engineer, you have a strong track record of mentoring junior team members and collaborating effectively with cross-functional teams, translating complex MLE concepts for non-technical stakeholders. Your passion for democratizing ML tools while maintaining rigorous standards of data handling and model deployment makes you an ideal candidate to advance our gene therapy research through cutting-edge ML solutions.

Leadership Structure: 

All inquiries regarding the posted position should be directed to the Talent Partner (see below). Interested individuals must apply directly to be considered for the opening.  

Pradeep Ramesh - Associate Director, ML/AI

Meet our Leadership Team and Board of Directors 

Meet your Talent Partner:

Annie Edminster - Employee Experience and Talent Lead

Tessera offers a competitive package of base and incentive compensation as well as a comprehensive benefits program designed to support the health, wellness and financial security of our employees and their families. Benefits currently include group medical, vision and dental coverage, group life and disability insurance, 401(k) with company contribution, tuition reimbursement, and much more.