Compensation
$150,000 – $450,000/year + discretionary performance bonus + benefits
The posted salary range represents the company’s good faith estimate of the base compensation for this position upon hire. The actual base compensation offered may vary within this range depending on individual qualifications, including but not limited to relevant skills, experience, education, certifications, and specific business needs. In addition to base salary, total compensation includes an annual discretionary performance bonus and comprehensive benefits.
Benefits Include
- Comprehensive medical, dental, and vision benefits
- Bonus
- 401K Plan
- Generous paid time off, sick leave and holidays
- Paid Parental Leave
- Employee Assistance Program
- Life insurance and disability
Professional Experience
Minimum
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Experience working with large language models and/or vision-language models, including pre-training, fine-tuning, evaluation, or inference.
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Strong Python and PyTorch development skills for large-scale machine learning research.
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Experience with distributed training systems and large-scale model optimization.
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Familiarity with multimodal datasets and data processing pipelines involving images, text, and video.
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Understanding of modern deep learning architectures, including Transformers, attention mechanisms, and multimodal fusion techniques.
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Experience with ML infrastructure, including model evaluation, debugging, optimization, and large-scale experimentation.
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Problem-solving and research skills with the ability to independently drive research/engineering projects.
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Effective communication and collaboration skills for working across research and engineering teams.
Preferred Skills
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Hands-on experience training or fine-tuning large Vision Language Models or multimodal foundation models at scale.
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Experience with distributed learning frameworks and infrastructure such as PyTorch Distributed, Megatron, Triton, or CUDA.
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Research experience in multimodal reasoning, agentic systems, tool use, OCR, grounding, document understanding, or multimodal retrieval.
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Experience with synthetic data generation, multimodal data curation, or automated evaluation frameworks for VLMs.
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Familiarity with efficient training and inference techniques such as FlashAttention, quantization, tensor parallelism, pipeline parallelism, or memory optimization.
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Experience contributing to open-source ML software and large-scale research codebases.
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Strong publication record in leading AI conferences such as NeurIPS, ICML, ICLR, CVPR, ICCV, ECCV, ACL, EMNLP, or related venues.
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Experience collaborating across research, infrastructure, and product-oriented teams to deliver state-of-the-art multimodal systems.