Lead Machine Learning Engineer

Our Company

Changing the world through digital experiences is what Adobe's all about. We give everyone—from emerging artists to global brands—everything they need to design and deliver exceptional digital experiences We're passionate about empowering people to create beautiful and powerful images, videos, and apps, and transform how companies interact with customers across every screen.

We're on a mission to hire the very best and are committed to creating exceptional employee experiences where everyone is respected and has access to equal opportunity. We realize that new ideas can come from everywhere in the organization, and we know the next big idea could be yours

The Opportunity:

Picture a day where every digital interaction you have, from the media you consume to the online shopping you do, is influenced by Adobe's innovative solutions. We are pleased to offer an outstanding opportunity for a Senior Machine Learning Engineer to join our Adobe Agent Orchestrator Platform team in Bucharest.

The Agent Orchestrator Platform sits at the heart of our agentic AI systems, enabling intelligent automation, workflow coordination, and autonomous decision-making at scale.

Lead ML strategy, craft strong models, build scalable ML infrastructure and encourage innovation in AI systems.

What You'll Do:

Lead the design, development, and deployment of modern machine learning models that power Adobe's Agent Orchestrator Platform, enabling sophisticated autonomous agent behaviors in a large-scale, multi-cloud environment.

Drive ML architecture decisions for the platform, establishing guidelines for model development, deployment, and lifecycle management.

Research and implement innovative techniques in LLM fine-tuning, prompt engineering, retrieval-augmented generation, and multi-agent coordination to push the boundaries of agent intelligence.

Architect and optimize end-to-end ML pipelines for model training, evaluation, deployment, and monitoring at scale, ensuring production-grade reliability and performance.

Lead experimentation strategy including A/B testing frameworks, evaluation metrics, and continuous improvement processes to improve business impact.

Establish and evangelize MLOps standard methodologies including model versioning, automated monitoring, retraining pipelines, and performance optimization across the organization.

Partner with platform engineers, product managers, and data scientists to integrate ML solutions seamlessly into production systems with stringent latency, availability, and scalability requirements.

Train and support junior ML engineers, promoting an atmosphere of creativity, precision, and ethical AI approaches.

Drive complex ML projects from research through production deployment with strategic vision, maintaining strong ownership and delivering measurable business outcomes.

What You'll Need to Succeed:

PhD or equivalent experience in Computer Science, Machine Learning, AI, or related field, and/or 5+ years of hands-on ML engineering experience with proven production impact.

Expert-level proficiency in Python and deep learning frameworks (PyTorch, TensorFlow, JAX) with deep understanding of model architectures and optimization.

Extensive hands-on experience with modern LLM frameworks and tools (e.g., OpenAI APIs, Anthropic Claude, LangChain etc .).

Proven track record of architecting, building, and deploying production ML systems that operate at scale with measurable business impact.

Profound knowledge of ML fundamentals encompassing advanced model evaluation, feature engineering, experiment development, and statistical analysis.

Expert knowledge of RAG architectures, vector databases (Pinecone, Weaviate, Chroma, FAISS), embedding models, and semantic search systems.

Strong experience with MLOps platforms and practices (MLflow, Weights & Biases, Kubeflow etc.).

Extensive experience with cloud ML platforms (AWS SageMaker, Azure ML).

Strong software engineering skills with expertise in API design, distributed systems architecture, production code quality, and system design.

Proficient in analyzing and solving problems, capable of debugging intricate ML systems and describing model behavior.

Demonstrated ability to lead technical initiatives, influence architecture decisions, and drive projects to successful completion.

Outstanding communication skills, both verbal and written, with ability to articulate complex ML concepts to technical and non-technical audiences.

Proficiency in English, both written and spoken.

Familiarity with Agile/Scrum project management methodologies.

Highly Valued:

Practical knowledge of reinforcement learning, multi-agent systems, and agent-based learning.

Publications or contributions to ML/AI research.

Deep knowledge of responsible AI, model interpretability, fairness, and bias mitigation in production systems.

Experience in developing and guiding high-performing ML teams.

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