Build and maintain foundational cloud infrastructure for AI and ML data science teams at Penguin Random House.
Compensation
The salary range for this position is $185,000-$210,000. All positions are currently eligible for an annual profit award or bonus, subject to Company results.
Responsibilities
Design and own core components of our AWS environment used across data science products
Set standards for how people and AI agents change infrastructure, including reviews, tests and guardrails
Build self‑service CI/CD pipelines and promotion gates for scientists to ship models
Help maintain security, compliance and reliability of cloud environments
Shape platform roadmap and communicate progress to stakeholders
Requirements
Nice to have: Evaluation or error analysis of ML or LLM systems
A hands-on Engineering Manager role leading a team team building and supporting internal AI platforms. The role involves people management, architecture shaping, and technical decision-making.
Locations (3)
Cyprus
Spain
Georgia
Responsibilities
Build and lead the team across hiring, planning, task decomposition, process improvements, and cross-team collaboration
Own and drive the development of internal AI platforms from concept to production, and keep them reliable as adoption grows
Turn recurring needs from product teams into reusable platform capabilities rather than one-off solutions
Ensure strong team performance and reliable, on-time delivery aligned with company and business goals
Partner closely with Product, Data, and AI teams, and with the internal teams who use the platforms, to understand their needs and drive adoption
Own end-to-end technical presales and solution delivery for strategic and enterprise customers across the Middle East, from initial discovery through PoC, architecture design, and production readiness.
Serve as a trusted technical advisor to senior customer stakeholders (CTO, Head of ML, Platform Engineering, AI Infrastructure teams).
Lead complex ML/AI infrastructure and MLOps architectures, including large-scale training and inference workloads on GPU-accelerated cloud platforms.
Design, validate, and document reference architectures, Infrastructure-as-Code solutions, and best-practice deployment patterns using Nebius AI.
Drive and execute advanced PoCs, workshops, architecture reviews, and executive-level presentations to demonstrate value and accelerate customer adoption.
Design, develop, and implement scalable data pipeline architectures using Kafka, Spark, and Flink. Manage data ingestion, processing, and distribution across multiple data sources and sinks using Apache Iceberg.
Responsibilities
Design, develop, and implement scalable data pipeline architectures using Kafka, Spark, and Flink
Manage data ingestion, processing, and distribution across multiple data sources and sinks using Apache Iceberg
Collaborate with data scientists and engineers to ensure the system supports business requirements
Optimize data pipelines for performance and scalability
Implement data governance and security measures
Requirements
Master’s degree or foreign equivalent in Computer Engineering, Computer Science, Information Technology, or related field
Design, develop, test, deploy, and support AI software components including foundation model training, large language model inference, agents and multi-agent workflows, similarity search, guardrails, model evaluation, experimentation, governance, and observability.
Locations (4)
McLean, Virginia, United States
Cambridge, Massachusetts, United States
New York, New York, United States
San Jose, California, United States
Compensation
Cambridge, MA: $197,300 - $225,100 for AI Engineer 4
McLean, VA: $197,300 - $225,100 for AI Engineer 4
New York, NY: $215,200 - $245,600 for AI Engineer 4
San Jose, CA: $215,200 - $245,600 for AI Engineer 4
Apply modern AI and machine learning techniques to concrete product problems, such as understanding product specifications, improving product recommendations, and exploring early agentic AI workflows, while learning how AI systems are designed, built, evaluated, and shipped in the real world.
Compensation
Competitive stipend for the duration of the internship
Responsibilities
Contribute to AI-driven initiatives, from problem framing through experimentation and implementation
Build, prototype, evaluate, and iterate on models and AI-powered workflows used in customer-facing product features
Partner with Senior Applied AI Engineers and Product Managers to translate real customer problems into clear technical approaches and tradeoffs
Analyze structured and unstructured data (e.g., product specifications, documents, metadata) to improve model performance
Document assumptions, results, and learnings to support cross-functional alignment and decision-making
Forward Deployed ML Engineers work at the intersection of deep technical work and direct customer impact, partnering with leading AI companies and foundation model labs to achieve state-of-the-art performance on workloads like LLM serving, model training, and audio pipelines.
Responsibilities
Work hands-on with companies like Suno, Cognition, and Meta to architect and optimize production AI workloads on Modal
Contribute to open-source projects (e.g., SGLang) and publish technical content
Collaborate with Modal’s product and sales teams, contributing to the platform as both an engineer and a product stakeholder
Build trusted relationships with technical leaders (CTOs, VPs of Engineering, ML leads) at companies doing frontier AI work
Conduct technical demos, experiments, and proofs of concept
Requirements
Nice to have: Side projects
Nice to have: open-source contributions
Nice to have: published work in ML or systems performance
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