Product ML Engineer - Local AI Systems Architect (Equity)

Alpaca Research; Cambridge (MA); On-site; USD 160,000 - 180,000; Full time

Job summary

Alpaca Research is hiring an ML engineer to bridge the gap between raw model capabilities and full system integration. You will work on model optimization, agent architectures, memory, and evaluation to build capable, efficient, and reliable local AI products.

You’ll experiment with fine-tuning, memory management, and inference-time techniques, translating research ideas into production systems used by real users.

Qualifications

• Strong ML fundamentals with modern language models, transformers, PyTorch, and training/inference techniques.
• Experience with LLM agents, tool calling, planning, retrieval, or multi-step reasoning systems.
• Understanding of long-context processing and memory architectures for context windows.
• Familiarity with quantization, efficient inference, and tradeoffs between quality, latency, and compute.
• Experience with post-training techniques (supervised fine-tuning, RL, distillation).
• Interest in reading papers and turning research into working software.
• Ability to own ambiguous problems and thrive in experiments.

Responsibilities

• Fine-tune, distill, and optimize language models for tool use, instruction following, and complex agentic tasks.
• Develop harnesses, tool-use architectures, planning/execution loops to improve model capabilities and reliability.
• Build retrieval and memory systems for long tasks and conversations.
• Experiment with inference-time techniques and local hardware optimization.
• Develop benchmarks and evaluations for agent capabilities and real-world performance.
• Prototype and implement promising ideas from research into production.

Skills

ML fundamentals

Agentic systems

Memory & context

Model optimization

Post-training

Research mindset

Independence

PyTorch

Transformers

Tools

PyTorch

Transformers

Hardware/ML systems

Job description

Alpaca Research is hiring an ML engineer to bridge the gap between raw model capabilities and full system integration. You will work on model optimization, agent architectures, memory, and evaluation to build capable, efficient, and reliable local AI products.

You’ll experiment with fine-tuning, memory management, and inference-time techniques, translating research ideas into production systems used by real users.