Applied ML Engineerâ Foundation Models & Multimodal Intelligence
Applied ML Engineerâ Foundation Models & Multimodal Intelligence
Posted on :
02-08-2025
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1 Vacancy
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Monthly Salary
USD 147400 - 272100
Vacancy
1 Vacancy
Posted on :
02-08-2025
Job Description
As an ML Engineer you will drive the development of large-scale training and inference pipelines for foundation models enabling agentic system to adapt to Apples diverse ecosystem. You will design and optimize data workflows that handle vast multimodal datasets ranging from text and images to other sensory data and build evaluation frameworks that measure not just raw performance but reasoning capabilities and task success in real-world hand-in-hand with researchers to translate cutting-edge advances such as new alignment grounding and reasoning techniquesinto production-grade implementations striking a balance between rapid experimentation and solid engineering practices. Strong software engineering principles from reproducible training setups to reliable CI/CD for ML will be critical to ensure these models scale to millions of users while meeting Apples high standards for efficiency security and privacy. This role is ideal for engineers who are passionate about turning foundation models into capable reasoning agents who thrive at the interface between research and large-scale systems and who are excited to define how multimodal intelligence will shape the next generation of Apple products.
- BS and a minimum of 3 years relevant industry experience.
- Proficiency in Python and at least one deep learning framework (PyTorch JAX or TensorFlow).
- Hands-on experience with large-scale distributed training pipelines and data engineering for ML.
- Software engineering skills including modular design testing and performance optimization.
- Masters or PhD in Computer Science Machine Learning or related technical field or equivalent industry experience.
- Strong experience in machine learning engineering with expertise in large-scale training fine-tuning or deployment of deep learning models.
- Experience with foundation models (language vision or multimodal).
- Familiarity with agentic or reasoning-based model pipelines such as tool-use orchestration chain-of-thought reasoning or planning systems.
- Expertise in large-scale data pipelines (data curation preprocessing and efficient storage).
- Experience evaluating and optimizing models for multimodal reasoning and task completion.
- Knowledge of retrieval-augmented generation (RAG) personalization or grounding techniques.
- Experience optimizing models for production deployment (latency memory quantization or on-device adaptation).
- Familiarity with privacy-preserving ML or federated learning.
- Ability to collaborate effectively with researchers and product engineers in a cross-functional environment.
Employment Type
Full Time
Key Skills
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