AI Research Scientist

This role is based in our Boston office on a hybrid schedule (3 days/week in-office).

MacPaw is looking for an AI Research Scientist to join our AI&Research Unit, a team dedicated to pushing the boundaries of fundamental artificial intelligence and bridging the gap between academic advancements and real-world technology.

Our fundamental research stream focuses on on-device LLM efficiency, deep architectural explorations, and localized machine learning solutions for the macOS ecosystem. It helps uncover high-level utility and solve complex technical challenges directly on user hardware, establishing our technology as a generalized leader in the global market.

As an AI Research Scientist, you will take deep ownership of our fundamental research initiatives. You’ll define, test, and coordinate advanced optimization workflows and structural model modifications while driving strategic collaborations with global scientific entities.

If you’re excited to lay the foundation for on-device AI efficiency to become a global industry standard, we’d love to hear from you!

In this role, you will:

• Prepare fundamental research proposals within our specialized LLM efficiency and optimization streams.
• Investigate new directions in LLM optimization by surveying relevant academic publications, formulating hypotheses, and running deep-dive experiments.
• Collaborate closely with internal research scientists and external academic labs on joint research projects and scientific publications.
• Work alongside the applied research stream to surface, validate, and transition relevant research prototypes into downstream production environments.
• Contribute to a strong publication record at top-tier international AI conferences,
• Take full ownership of your research niche, driving initiatives from ideation to implementation with a high degree of independence and autonomy.
• Take part in internal knowledge-sharing sessions and weekly paper clubs to consistently improve domain expertise.

Skills you’ll need to bring:

• Deep experience in Natural Language Processing (NLP) or a similar machine learning domain, gained through solid academic work, industry experience, or both.
• Strong theoretical and practical understanding of recent LLM optimization techniques (ex. quantization, KV-cache compression, speculative decoding, and distillation).
• Direct hands-on experience with parameter-efficient fine-tuning (including LoRA and other adapters) and mixture-of-experts (MoE) architectures.
• Advanced prototyping skills and a proven ability to implement complex algorithms and architectures directly from academic papers.
• Fluent programming capabilities in Python and modern frameworks such as PyTorch, JAX, or TensorFlow.
• Robust fundamental knowledge of linear algebra, probability theory, and mathematical statistics.

As a plus:

• A track record of publishing original research at international research conferences in AI/ML/SE/HCI domains.
• Practical experience in performance engineering, including code profiling and low-level optimization (GPU, Metal, C++...).
• Hands-on experience training, running, or deploying localized LLM models on device frameworks like MLX.