Principal Machine Learning Engineer

This is a fully remote position, open to applicants in United States.

📋 Description

• Take charge of enterprise AI/ML architecture, standards, APIs, and guardrails across both cloud and on-premises environments.

• Develop production ML and generative-AI solutions, along with lightweight applications that provide sub-second insights.

• Create end-to-end ML pipelines that encompass data ingestion, feature engineering, training, hyperparameter optimization, evaluation, registration, and automated promotion.

• Construct and uphold full-stack AI applications that integrate model services with UI components, workflow engines, or business logic layers.

• Set up observability, SLOs, safe deployment practices, and incident runbooks.

• Lead offline/online evaluations, A/B testing, drift detection, and automated retraining processes.

• Design LLM/RAG systems with prompt management, safety measures, and optimized inference.

• Ensure data quality, lineage, model cards, and data cards while applying privacy-preserving techniques.

• Contribute reusable ML/GenAI components like feature stores, model registries, and experiment-tracking libraries.

• Advocate for best practices that enhance engineering velocity across teams.

• Conduct exploratory data analysis and feature ideation on complex, high-dimensional datasets.

• Prototype and benchmark algorithms, provide guidance on scalability and production readiness, and co-manage model performance KPIs.

• Convert R&D, Manufacturing, and Commercial domain requirements into actionable roadmaps.

• Mentor teams and articulate technical trade-offs effectively.

• Collaborate with DevOps, Security, Compliance, and Product teams to deliver enterprise-grade AI solutions.

⛳️ Requirements

• Doctorate degree and 2 years of experience as a Machine Learning Engineer, OR Master’s degree and 6 years, OR Bachelor’s degree and 8 years, OR Associate’s degree and 10 years, OR high school diploma/GED and 12 years of relevant experience.

• At least 2 years of experience in directly managing people and/or leadership experience in leading teams, projects, programs, or managing resource allocation.

• 3–5 years of experience in AI/ML and enterprise software.

• Strong understanding of machine learning algorithms such as regression, tree-based ensembles, clustering, dimensionality reduction, time-series models, CNNs, RNNs, transformers, and LLM/RAG techniques.

• Proven success in selecting and integrating AI SaaS/PaaS solutions and developing custom ML services at scale.

• Expert knowledge of vector databases, RAG pipelines, prompt-engineering DSLs, and agent frameworks like LangChain, LangGraph, and Semantic Kernel.

• Proficiency in Python and Java programming languages.

• Experience with Docker/Kubernetes for containerization.

• Familiarity with cloud services such as AWS, Azure, or GCP.

• Experience in modern DevOps/MLOps practices, including GitHub Actions and Bedrock/SageMaker Pipelines.

• Strong skills in business cases, including TCO versus NPV modeling.

• Exceptional stakeholder management abilities with the capacity to translate complex technical concepts into concise, outcome-oriented narratives.

• Preferred experience in the biotechnology or pharmaceutical industry.

• Published thought-leadership or conference presentations on enterprise GenAI adoption is preferred.

• Preferred Master’s degree in Computer Science and/or Data Science.

• Familiarity with Agile methodologies and SAFe is preferred.

• Master’s degree with 10–12+ years of experience in Computer Science, IT, or a related field, OR Bachelor’s degree with 12–14+ years of experience in Computer Science, IT, or a related field.

• Excellent analytical and troubleshooting abilities.

• Strong verbal and written communication skills.

• Ability to work effectively with global, virtual teams.

• High level of initiative and self-motivation.

• Capacity to manage multiple priorities successfully.

• Strong presentation and public speaking skills.

🏝️ Benefits

• A comprehensive employee benefits package.

• Retirement and Savings Plan featuring generous company contributions.

• Group medical, dental, and vision coverage.

• Life and disability insurance.

• Flexible spending accounts.

• Discretionary annual bonus program.

• Stock-based long-term incentives.

• Award-winning time-off plans.

• Flexible work models when feasible.

• Opportunities for career development.

• Work/life balance initiatives.

• Financial plans with options to save towards retirement or other goals.