Senior Machine Learning Engineer
Adobe
Job Overview
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Job Description
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
We have a fantastic opportunity for a Senior Machine Learning Engineer to join our Adobe Experience Platform team based in Bucharest.
Adobe Experience Platform is the Adobe solution that helps customers to centralize and standardize their customer data and content across the enterprise powering 360-degree customer profiles, enabling data science and data governance to drive real-time personalized experiences. With Experience Platform, enterprises will be able to use completely coordinated marketing and analytics solutions for driving relevant customer interactions.
Our engineering team is responsible for building the Adobe Experience Platform. We help thousands of customers collect, handle and synthesize petabytes of data with high fidelity and velocity. We help them make sense of that data, glean insights and run algorithms, so they can deliver delightful experiences in real-time, every time.
This position is for a key contributor who will be a part in developing modern data architectures, highly distributed Lakehouse solutions, and implementing those solutions through to production.
What you’ll do:
- Innovate: Design and implement scalable AI/ML solutions, focusing on APIs, RAG architectures, and agent frameworks. Integrate these technologies into Adobe’s ecosystem.
- Collaborate: Work with multi-functional teams consisting of data scientists, researchers, product managers, and software engineers to deliver innovative projects.
- Lead: Mentor team members and drive key ML initiatives from concept to deployment, ensuring robust and scalable solutions.
- Engage: Connect with customers to understand their needs and implement personalized solutions that align with business objectives.
- Represent Adobe: Share insights at industry conferences, publish thought leadership content, and showcase your work internally and externally.
What You Need To Succeed
- Advanced degree or equivalent experience in Computer Science, Machine Learning, Data Science or a related field, with hands-on experience in software engineering.
- Experience: Proficiency in building and deploying scalable ML models.
- Technical Skills: Strong coding skills in Python, Java. Familiarity with API design, RESTful services, CI/CD pipelines, and backend development is crucial.
- Working Experience with RAG architectures and frameworks (e.g., LangGraph, LlamaIndex, Autogen or Crew.ai).
- Knowledge of monitoring and observability tools for AI systems.
- Leadership & Communication: Excellent leadership abilities to guide projects from ideation to implementation.
Nice-to-have skills:
- Experience with conversational AI, text generation, sentiment analysis.
- Experience with TensorFlow, PyTorch, scikit-learn, pandas, NumPy, SQL/NoSQL databases, and cloud infrastructure.
Key skills/competency
- Machine Learning Solutions
- AI/ML Development
- RAG Architectures
- Agent Frameworks
- Python Programming
- Java Development
- API Design
- Distributed Data Systems
- Cloud Infrastructure
- ML Model Deployment
How to Get Hired at Adobe
- Research Adobe's culture: Study their mission, values, recent news, and employee testimonials on LinkedIn and Glassdoor.
- Tailor your resume: Highlight ML engineering, RAG architectures, Python, and Java expertise relevant to Adobe.
- Showcase ML projects: Provide a portfolio of scalable AI/ML solutions you've designed and deployed.
- Prepare for technical interviews: Expect deep dives into ML models, data architectures, and coding challenges.
- Demonstrate leadership: Be ready to discuss mentoring experiences and driving key ML initiatives to completion.
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