SailPoint
Staff Machine Learning Engineer
Vaga remota de Machine Learning Engineering com fit claro de localização do candidato.
Publicada21 de jun. de 2026
Países elegíveis1 país aceito
Sinal de senioridadeSenior
Modelo de trabalhoRemoto
Locais aceitos para candidatos
Estados Unidos
Resumo da vaga
Staff Machine Learning Engineer
Requisitos e responsabilidades
Conteúdo da vaga extraído em seções para revisão mais rápida.
Responsibilities
- Design, implement, and optimize ML models (supervised, unsupervised, and LLM-based) that power both customer-facing and internal product capabilities.
- Translate AI research and experimental prototypes into scalable, maintainable production systems.
- Lead technical efforts to improve model accuracy, precision/recall trade-offs, and generalization across diverse regions and customer datasets.
- Build and enhance ML infrastructure and pipelines for feature extraction, model training, evaluation, deployment, and monitoring.
- Drive the technical strategy for reproducibility, model versioning, data lineage, and CI/CD automation in ML systems.
- Collaborate with AI platform and DevOps teams to ensure reliable data access, observability, and efficient use of compute resources.
- Set technical direction and best practices for ML engineering across the AI organization, influencing architecture and design standards.
- Mentor and guide engineers in scalable ML design patterns, experimentation frameworks, and software craftsmanship.
- Partner with product and engineering leaders to prioritize and deliver high-impact AI capabilities aligned with business goals.
- Work cross-functionally with architecture, platform, and analytics teams to ensure AI components integrate seamlessly across SailPoint’s ecosystem.
- Advance model lifecycle management, AI governance, and responsible AI practices to ensure quality, fairness, and transparency.
- Communicate complex ML concepts into actionable insights and recommendations for technical and non-technical audiences.
- Support day-to-day team operations in partnership with TPMs and managers, ensuring alignment and delivery across initiatives.
Requirements:
- 8+ years of professional experience in machine learning engineering, software development, or a related technical field.
- Strong programming skills in Python and proficiency with ML frameworks such as PyTorch, TensorFlow, or scikit-learn.
- Proven track record of building and deploying ML models at production scale (cloud-native environments preferred).
- Deep understanding of data modeling, feature engineering, and statistical analysis.
- Expertise in data pipelines, ETL, and feature engineering using frameworks like Spark, Airflow, or dbt.
- Solid knowledge of MLOps practices—including model monitoring, retraining, CI/CD, and experiment tracking.
- Strong foundation in software engineering best practices: testing, modularization, code review, and observability.
- Excellent communication and collaboration skills, with demonstrated experience leading cross-functional technical initiatives.
Preferred
- Experience with LLM-based solutions, embeddings, and retrieval-augmented generation (RAG).
- Familiarity with identity, security, or enterprise SaaS systems.
- Experience designing AI platforms or reusable ML services that support multiple product lines.
- Demonstrated ability to set technical direction, influence architectural decisions, and guide organizational strategy.
Roadmap for success-30 days:
- Gain deep understanding of SailPoint’s AI vision, architecture, and active ML initiatives.
- Familiarize with existing data pipelines, environments, and model deployment frameworks.
- Build relationships with key stakeholders across AI, platform, DevOps, and product teams.
- Conduct hands-on review of current ML models, data flows, and monitoring systems to identify immediate optimization or reliability gaps.
- Begin contributing to small improvements or code reviews to gain familiarity with production practices.
90 days:
- Lead at least one end-to-end ML enhancement or pilot.
- Establish and document best practices for reproducibility, observability, and CI/CD for ML systems.
- Mentor junior engineers and support team-wide code quality and experimentation standards.
- Present a roadmap or proposal for scaling AI components or addressing key technical debt areas.
6 months:
- Deliver measurable impact on model performance, reliability, or scalability for at least one core AI product.
- Lead design and implementation of a shared ML service or reusable component (e.g., feature store, inference service, or monitoring framework).
- Be recognized as a technical go-to for complex ML engineering and architecture decisions.
1 year:
- Establish SailPoint’s ML engineering foundation as robust, scalable, and production-ready across multiple AI initiatives.
- Drive one or more flagship AI capabilities from prototype to production, with demonstrated business or customer impact.
- Mentor and elevate other engineers, fostering a culture of technical excellence and continuous learning.
- Influence long-term AI platform architecture and strategic investment areas as part of the broader AI leadership group.
The Tech Stack (if applicable):
- Core Programming: SQL, Python, Shell/Bash, Go
- Cloud Platform: AWS (SageMaker, Bedrock)
- Data: Snowflake, DBT, Kafka, Airflow, Feast
- Visualization: Tableau, Qlik
- CI/CD: Cloudbees, Jenkins
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