What It's Like to Work There
AI-native and research-oriented. The team includes ML researchers and systems engineers who are passionate about open-source AI. Fast-paced with a focus on making AI infrastructure accessible and affordable.
Resume Tips for Together AI
GPU infrastructure and ML serving experience is the top differentiator. Show you've deployed and optimized model inference at scale.
Experience with open-source LLMs (Llama, Mistral, etc.) and fine-tuning pipelines is directly relevant.
Show knowledge of ML optimization: quantization, distillation, batching strategies, and inference efficiency.
CUDA, Python, and C++ skills are valuable for their core infrastructure work.
If you've contributed to open-source AI projects or frameworks, highlight those contributions.
Hiring Process
Recruiter screen focused on ML systems and infrastructure experience
Technical interview involving ML infrastructure and distributed computing
Onsite with 3-4 rounds: coding, ML systems design, GPU infrastructure discussion, and team fit
They value experience with model serving and GPU optimization
Interview Style
Deeply technical with a focus on ML systems, GPU programming, and model optimization. Expect questions about inference optimization, distributed training, and cloud infrastructure. They want engineers who understand both ML and systems.
Top Roles They Hire
Machine Learning Engineer
Infrastructure Engineer
Research Scientist
Software Engineer
Solutions Engineer
Developer Advocate
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