AI Research Engineer, Model Training & Adaptation

United States

Menlo Park, California, United States

ABOUT MATTER

Matter is building the AI-native autonomy stack for physical manufacturing. We own and operate factories as controlled learning environments — collecting data from every stage of production to train and validate AI systems that run on real hardware.

THE ROLE

We are hiring an AI Research Engineer to lead model training and adaptation for MatterOS — spanning pre-training data, domain-specific fine-tuning, and post-training alignment for manufacturing tasks.

You will work across the full model development lifecycle: from curating the manufacturing corpus and designing synthetic data pipelines, to fine-tuning general-purpose VLMs and VLAs for factory-specific tasks, to building the evaluation frameworks that define what “good” looks like in a production context.

WHAT YOU’LL DO

Design and manage synthetic data generation pipelines using physics simulation (NVIDIA Isaac Sim, MuJoCo): domain randomization, procedural scene generation, and sim-to-real transfer validation

Curate and annotate the manufacturing training corpus: CAD data, process plans, equipment programs, assembly sequences, sensor streams, and quality inspection records

Fine-tune and adapt pre-trained VLMs, VLAs, and LLMs to manufacturing-specific tasks: DFM co-pilot, fault detection, assembly instruction generation, and robotic policy learning

Implement post-training alignment methods (RLHF, DPO, or preference optimization) to align model behavior with manufacturing task success criteria

Build domain-specific evaluation benchmarks: manipulation accuracy, instruction-following fidelity, process compliance, and failure mode detection

Collaborate with the VLA Research Scientist and AI Infrastructure Engineer to close the training loop between simulation, real factory data, and deployed models

WHAT WE’RE LOOKING FOR

Strong background in machine learning research with practical experience training large models (VLMs, LLMs, or robot policies)

Hands-on experience with pre-training data pipelines, synthetic data generation, or multi-modal training at scale

Familiarity with fine-tuning and alignment techniques: LoRA, RLHF, DPO, instruction tuning

Proficiency in PyTorch; experience with NVIDIA Isaac Sim, MuJoCo, or physics-based simulation engines

Ability to design evaluation frameworks that go beyond standard benchmarks to measure task-specific real-world performance

Systems thinking: you understand that training data quality and domain alignment matter more than model scale in constrained physical applications

NICE TO HAVE

PhD or graduate degrees in machine learning, robotics, or a related field

Experience with embodied AI, robot learning, or sim-to-real transfer

Background in industrial or manufacturing domains

Experience with distributed training on GPU clusters (SLURM, DeepSpeed, FSDP)

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AI Research Engineer, Model Training & Adaptation

ABOUT MATTER

THE ROLE

WHAT YOU’LL DO

WHAT WE’RE LOOKING FOR

NICE TO HAVE

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