BioSpace
Sr Machine Learning Engineer -AI/ML- US Remote
BioSpace, Thousand Oaks, California, United States, 91362
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Sr Machine Learning Engineer -AI/ML- US Remote
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BioSpace Role Description: We are seeking a
Sr Machine Learning Engineer
to design the core services, infrastructure, and governance controls that allow hundreds of practitioners to prototype, deploy, and monitor models securely and cost-effectively. Responsibilities
Engineer end-to-end ML pipelines using Kubeflow, SageMaker Pipelines, Open AI SDK, or equivalent MLOps stacks. Harden research code into production-grade micro-services, packaging models in Docker/Kubernetes, and exposing secure REST, gRPC, or event-driven APIs. Build and maintain full-stack AI applications by integrating model services with lightweight UI components, workflow engines, or business-logic layers. Optimize performance and cost at scale by selecting appropriate algorithms, applying quantization/pruning, and tuning GPU/CPU auto-scaling policies. Instrument comprehensive observability, including real-time metrics, distributed tracing, drift & bias detection, and user-behavior analytics. Embed security and responsible-AI controls, including data encryption, access policies, lineage tracking, explainability, and bias monitoring. Contribute reusable platform components, feature stores, model registries, and experiment-tracking libraries. Perform exploratory data analysis and feature ideation on complex, high-dimensional datasets. Partner with data scientists to prototype and benchmark new algorithms, offering guidance on scalability trade-offs and production-readiness. Must-Have Skills
3-5 years in AI/ML and enterprise software. Comprehensive command of machine-learning algorithms, including regression, tree-based ensembles, clustering, dimensionality reduction, time-series models, and deep-learning architectures. Proven track record of selecting and integrating AI SaaS/PaaS offerings and building custom ML services at scale. Expert knowledge of GenAI tooling, including vector databases, RAG pipelines, prompt-engineering DSLs, and agent frameworks. Proficiency in Python and Java, containerization (Docker/K8s), cloud (AWS, Azure, or GCP), and modern DevOps/MLOps (GitHub Actions, Bedrock/SageMaker Pipelines). Strong business-case skills, able to model TCO vs. NPV and present trade-offs to executives. Exceptional stakeholder management, able to translate complex technical concepts into concise, outcome-oriented narratives. Good-to-Have Skills
Experience in Biotechnology or pharma industry. Published thought-leadership or conference talks on enterprise GenAI adoption. Master's degree in computer science and/or data science. Familiarity with Agile methodologies and Scaled Agile Framework (SAFe) for project delivery. Education and Professional Certifications
Master's degree with 8+ years of experience in Computer Science, IT, or related field. Bachelor's degree with 10+ years of experience in Computer Science, IT, or related field. Certifications on GenAI/ML platforms (AWS AI, Azure AI Engineer, Google Cloud ML, etc.) are a plus. Soft Skills
Excellent analytical and troubleshooting skills. Strong verbal and written communication skills. Ability to work effectively with global, virtual teams. High degree of initiative and self-motivation. Ability to manage multiple priorities successfully. Team-oriented, with a focus on achieving team goals. Ability to learn quickly, be organized, and detail-oriented. Strong presentation and public speaking skills.
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Sr Machine Learning Engineer -AI/ML- US Remote
role at
BioSpace Role Description: We are seeking a
Sr Machine Learning Engineer
to design the core services, infrastructure, and governance controls that allow hundreds of practitioners to prototype, deploy, and monitor models securely and cost-effectively. Responsibilities
Engineer end-to-end ML pipelines using Kubeflow, SageMaker Pipelines, Open AI SDK, or equivalent MLOps stacks. Harden research code into production-grade micro-services, packaging models in Docker/Kubernetes, and exposing secure REST, gRPC, or event-driven APIs. Build and maintain full-stack AI applications by integrating model services with lightweight UI components, workflow engines, or business-logic layers. Optimize performance and cost at scale by selecting appropriate algorithms, applying quantization/pruning, and tuning GPU/CPU auto-scaling policies. Instrument comprehensive observability, including real-time metrics, distributed tracing, drift & bias detection, and user-behavior analytics. Embed security and responsible-AI controls, including data encryption, access policies, lineage tracking, explainability, and bias monitoring. Contribute reusable platform components, feature stores, model registries, and experiment-tracking libraries. Perform exploratory data analysis and feature ideation on complex, high-dimensional datasets. Partner with data scientists to prototype and benchmark new algorithms, offering guidance on scalability trade-offs and production-readiness. Must-Have Skills
3-5 years in AI/ML and enterprise software. Comprehensive command of machine-learning algorithms, including regression, tree-based ensembles, clustering, dimensionality reduction, time-series models, and deep-learning architectures. Proven track record of selecting and integrating AI SaaS/PaaS offerings and building custom ML services at scale. Expert knowledge of GenAI tooling, including vector databases, RAG pipelines, prompt-engineering DSLs, and agent frameworks. Proficiency in Python and Java, containerization (Docker/K8s), cloud (AWS, Azure, or GCP), and modern DevOps/MLOps (GitHub Actions, Bedrock/SageMaker Pipelines). Strong business-case skills, able to model TCO vs. NPV and present trade-offs to executives. Exceptional stakeholder management, able to translate complex technical concepts into concise, outcome-oriented narratives. Good-to-Have Skills
Experience in Biotechnology or pharma industry. Published thought-leadership or conference talks on enterprise GenAI adoption. Master's degree in computer science and/or data science. Familiarity with Agile methodologies and Scaled Agile Framework (SAFe) for project delivery. Education and Professional Certifications
Master's degree with 8+ years of experience in Computer Science, IT, or related field. Bachelor's degree with 10+ years of experience in Computer Science, IT, or related field. Certifications on GenAI/ML platforms (AWS AI, Azure AI Engineer, Google Cloud ML, etc.) are a plus. Soft Skills
Excellent analytical and troubleshooting skills. Strong verbal and written communication skills. Ability to work effectively with global, virtual teams. High degree of initiative and self-motivation. Ability to manage multiple priorities successfully. Team-oriented, with a focus on achieving team goals. Ability to learn quickly, be organized, and detail-oriented. Strong presentation and public speaking skills.
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