- Company Name
- Snap Inc.
- Job Title
- Machine Learning Engineering Manager, Gen AI
- Job Description
-
**Job Title**
Machine Learning Engineering Manager – Generative AI
**Role Summary**
Lead a team of applied Machine Learning Engineers to design, prototype, and ship consumer‑facing Generative AI products. The role focuses on image and video generation/editing and large‑language models, ensuring rapid prototyping, rigorous testing, and production‑ready deployment while guiding the team toward best practices in scalability, cost, and reliability.
**Expactations**
- Drive the entire ML lifecycle from research to production, ensuring high‑quality, maintainable code and operational excellence.
- Mentor and grow a high‑performing engineering squad, hiring, coaching, and retaining talent.
- Make data‑driven build‑vs‑buy decisions on models, APIs, and third‑party services.
- Collaborate closely with Product, Design, Software Engineering, Lens Content, Data Science, and executive stakeholders to translate user insights into ML solutions.
- Lead technical planning, code reviews, and architecture reviews for large, complex initiatives.
**Key Responsibilities**
- Own end‑to‑end ML pipeline: research, prototyping, training, deployment, inference, and A/B testing.
- Collaborate with cross‑functional teams to define product vision, feature scope, and success metrics.
- Evaluate emerging Gen‑AI research, open‑source models, and commercial APIs; conduct feasibility studies and cost‑benefit analyses.
- Maintain production ML services with focus on availability, latency, and cost efficiency.
- Mentor engineering teams, conduct performance reviews, and foster a culture of experimentation and learning.
- Communicate technical trade‑offs and progress to stakeholders at all levels.
**Required Skills**
- Proven experience building and scaling ML‑based backend products.
- Deep knowledge of generative models (GANs, diffusion, transformers/LLMs) and practical application to consumer products.
- Strong leadership, mentorship, and team‑building abilities.
- Excellent written and verbal communication; ability to translate technical concepts to non‑technical stakeholders.
- Experience coordinating with Product, Design, and Data Science in an Agile environment.
- Proficiency in ML frameworks: TensorFlow, PyTorch, PyTorch Lightning, or equivalent; experience with large‑scale distributed training (Spark ML, Horovod, etc.).
- Familiarity with model evaluation metrics (visual quality, user‑centered metrics, latency, A/B testing).
**Required Education & Certifications**
- Master’s or PhD in Computer Science, Electrical Engineering, Applied Mathematics, or related field (or equivalent relevant work experience).
- Demonstrated track record of technical leadership in ML teams.
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