Principal ML Engineer, Machine Learning Platform – Systems Architecture

🕒 vor 1 Monat

🍂 Massachusetts, Virginia – Remote

🍂 Massachusetts – Remote ⚔️ Virginia – Remote

💵 $152.000 - $272.250 / Jahr

⏰ Vollzeit

🔴 Experte

⚙️ Systemingenieur

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Autodesk

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10.000+ Mitarbeiter

Gegründet 1982

📱 Medien

Architecture • Engineering • Media

Autodesk ist ein weltweit führender Anbieter von Software für Designerinnen und Designer, Ingenieurinnen und Ingenieure, Baufachleute und Kreative. Das Unternehmen bietet eine umfassende Suite von Design- und Engineering-Anwendungen, darunter bekannte Produkte wie AutoCAD, Revit und 3ds Max. Über die Design and Make Platform unterstützt Autodesk Fachleute in unterschiedlichsten Branchen dabei, Projekte effizient zu entwerfen, zu visualisieren und zu verwalten – und fördert so Innovation und Nachhaltigkeit in Architektur, Ingenieurwesen, Bauwesen und Fertigung.

Beschreibung

• Lead architecture and delivery for major ML platform capabilities across training, evaluation, deployment, and observability • Design scalable systems for distributed training, data processing, feature and model lifecycle management, and production inference • Own platform-level technical outcomes from design through deployment, operations, and continuous improvement • Drive the design and scaling of data pipelines for large-scale structured and semi-structured technical datasets • Lead architecture for distributed data processing and orchestration systems such as Ray, Airflow, Spark, or similar platforms • Establish strong practices for data lineage, provenance, governance, and responsible data usage in ML systems • Guide the design of model deployment, inference services, monitoring, and observability for production ML workloads • Contribute to the development of ML-ready representations for geometry, graph, hierarchical, or multimodal data • Clarify ambiguous problem spaces, define solution approaches, and lead execution across multiple engineers and teams • Establish and improve engineering standards, operational practices, and architectural patterns for ML systems • Lead incident response for critical platform issues and drive lasting improvements across system health and supportability • Mentor engineers and act as a force multiplier through design leadership, coaching, and technical reviews • Communicate technical strategy, tradeoffs, and execution plans clearly to technical and non-technical stakeholders

🎯 Anforderungen

• Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field, or equivalent industry experience • Typically 6 to 8 years of industry experience in software engineering, ML infrastructure, distributed systems, or platform engineering, including experience leading design and delivery of complex technical systems • Deep experience in software architecture, distributed systems, large-scale data platforms, or ML infrastructure • Strong proficiency in Python and strong command of production software engineering practices • Experience leading complex technical initiatives that span multiple engineers or cross-functional teams • Strong experience with large-scale data pipelines, distributed data processing, and cloud-native platform architectures • Experience with model deployment, inference systems, and production observability • Demonstrated ability to make architecture decisions that balance performance, scalability, reliability, and cost • Strong communication and stakeholder management skills

🏖️ Vorteile

• Health and financial benefits • Time away and everyday wellness