Senior Machine Learning Engineering Manager
| Company | Adobe |
|---|---|
| Location | Seattle, WA, USA, San Francisco, CA, USA, San Jose, CA, USA |
| Salary | $168200 – $340100 |
| Type | Full-Time |
| Degrees | Master’s, PhD |
| Experience Level | Senior, Expert or higher |
Requirements
- MS/PhD in Computer Science, AI/ML, or related fields, or equivalent industry experience.
- 8+ years of experience in machine learning, including production-scale deployments.
- 5+ years of engineering leadership experience, with a track record of growing and mentoring high-performing teams.
- Strong background in generative AI technologies (e.g., GANs, diffusion models, transformers).
- Proven ability to lead large, cross-functional teams through complex, time-sensitive projects.
- Excellent communication skills, with a knack for influence and driving alignment in matrixed organizations.
Responsibilities
- Tech lead projects that delivers critical ML services and APIs to integrate first- and third-party models and pipelines for Enterprise customers
- Collaborate cross-functionally with PMs, PMMs, TPMs, and other engineering teams to shape the roadmap of Adobe’s Enteprise Gen AI space
- Provide technical leadership, coaching and mentorship for team members
- Explore and research new and emerging ML and MLOps technologies to continuously improve Adobe’s GenAI engineering effectiveness and efficiency
- Review and provide feedback on features, technology, architecture, designs and test strategies.
- Lead the development and delivery of critical ML services and APIs that integrate first- and third-party models into scalable enterprise pipelines.
- Act as a technical leader and coach, guiding a team of ML and platform engineers through complex, high-stakes projects.
- Drive cross-functional collaboration with PMs, TPMs, PMMs, and other engineering groups to align on roadmap and execution.
- Champion innovation by exploring emerging ML and MLOps technologies to boost Adobe’s GenAI effectiveness.
- Oversee technical design reviews, architecture decisions, and system reliability standards.
Preferred Qualifications
- Hands-on experience with training, fine-tuning, inference, and optimization of generative models.
- Hands-on familiarity with optimizing and converting models across formats