S'INSCRIRE

Offres d'emploi

Trouvez des postes près de chez vous, sur site, hybrides ou à distance.
  • Emplois similaires à : Senior Machine Learning Engineer I II
XX
Senior Staff Machine Learning Engineer, (Machine Learning)Affirm • Austin, Texas, United States
Postuler Maintenant
XX

Senior Staff Machine Learning Engineer, (Machine Learning)

Affirm
  • US
    Austin, Texas, United States
  • US
    Austin, Texas, United States
Postuler Maintenant

À propos

Affirm is reinventing credit to make it more honest and friendly, giving consumers the flexibility to buy now and pay later without any hidden fees or compounding interest. Join the team as a Senior Staff Machine Learning Engineer and become a pivotal part of our innovative ML team. Our team is dedicated to the mission of revolutionizing financial services with transparency and inclusivity at its core, utilizing advanced machine learning techniques to ensure responsible and accessible financial products. What You’ll Do Define and drive a multi‑year, multi‑team technical strategy for machine learning across the organization, ensuring alignment with company priorities and influencing partner roadmaps. Lead the design, implementation, and scaling of advanced ML systems, setting architectural direction for complex, cross‑functional initiatives and ensuring systems remain reliable, extensible, and ready for increasingly sophisticated workloads. Partner deeply with ML Platform, product, engineering, and risk leadership to shape long‑term modeling capabilities, identify new opportunities for ML impact, and guide infrastructure evolution required for next‑generation methods. Provide broad technical leadership across the ML organization, mentoring senior engineers, elevating design and code quality, and spreading expertise through documentation, talks, and cross‑org guidance. Drive clarity and alignment on ambiguous, high‑stakes technical decisions, resolving cross‑team tensions and balancing competing priorities with optimized judgment for the broader engineering organization. Champion operational and system excellence at the area level, owning long‑term health, availability, and evolution of critical ML systems, ensuring robust testing, monitoring, and reliability practices across teams. What We Look For 10+ years (or equivalent) of experience researching, designing, deploying, and operating large‑scale, real‑time machine learning systems, with a record of driving innovation and measurable business impact. Relevant PhD can count for up to 2 Years of Experience. Leadership experience in end‑to‑end ML system design, from data architecture and feature pipelines to model training, evaluation, and production deployment, using distributed frameworks such as Spark, Ray, or similar. Proficiency in Python and ML frameworks, including PyTorch and XGBoost; experience with tooling for training orchestration, experimentation, and model monitoring (e.g., Kubeflow, MLflow, or internal platforms). Strong understanding of representation learning and embedding‑based modeling; deep expertise in neural network‑based sequence modeling (Transformers, recurrent, attention‑based) and multi‑task learning systems, comfortable optimizing models that learn from sequential or temporal event data at scale. Hands‑on experience with large‑scale distributed ML infrastructure (streaming/batch ingestion, feature stores, feature engineering, training pipelines, model serving, inference infrastructure, monitoring, automated retraining). Strong technical leadership skills: defining long‑term strategy, guiding research direction, aligning work across teams, recognized as a trusted expert who can drive clarity and execution in ambiguous problem spaces. Exceptional judgment, collaboration, and communication skills, enabling effective technical discussions with engineers, researchers, and executives, mentoring senior engineers, fostering technical excellence, and contributing to continuous learning. Strong verbal and written communication skills supporting collaboration across a global engineering organization. Practical experience or a bachelor’s degree in a related field. Compensation & Benefits Pay Grade: R; Equity Grade: 15. Base pay range (CA, WA, NY, NJ, CT): $260,000 – $310,000 per year. Base pay range (other U.S. states): $232,000 – $282,000 per year. Remote‑first company; most roles work from any U.S. location, with occasional office presence as required. Competitive benefits: 100 % subsidized medical, dental, and vision coverage for employee and dependents; monthly health, wellness, and tech spending stipends; equity rewards. Time‑off: competitive vacation and holiday schedules. ESPP: employee stock purchase plan allowing stock purchase at a discount. EEO & Inclusion Affirm is proud to be a remote‑first company and believes it’s On Us to provide an inclusive interview experience for all, including people with disabilities. We provide reasonable accommodations during the hiring process. We consider qualified applicants for employment with arrest and conviction records for U.S. positions in Los Angeles or San Francisco. By clicking “Submit Application,” you acknowledge that you have read the Global Candidate Privacy Notice and give informed consent to collection, processing, use, and storage of personal information as described therein.
#J-18808-Ljbffr
  • Austin, Texas, United States

Compétences linguistiques

  • English
Avis aux utilisateurs

Cette offre provient d’une plateforme partenaire de TieTalent. Cliquez sur « Postuler maintenant » pour soumettre votre candidature directement sur leur site.

10'000+ offres d'emploi : Trouvez des postes près de chez vous, sur site, hybrides ou à distance