Member of Technical Staff, LLM Post-Training, Applied

Sanas is pioneering the future of human communication. Founded by a team of Stanford researchers and entrepreneurs with deep industry experience, Sanas has developed the world's first real-time speech AI platform capable of accent translation, noise cancellation, speech enhancement, cross-language communication, and more.

Sanas makes conversations clearer, more inclusive, and more effective, removing barriers that prevent people from being understood, regardless of accent, background noise, or native language.

Sanas is currently one of the fastest growing startups in Silicon Valley, growing from $16M to $50M ARR in 2025. The company's core business is profitable and is on track to end 2026 with >$120M ARR. Our team combines deep expertise in model innovation and systems engineering with a design-minded product engineering culture to build and ship cutting-edge AI models and experiences - entirely in-house.

Sanas is a 130 person team, established in 2020. In this short span, we've successfully secured over $100 million in funding. Our innovation has been supported by the industry's leading investors, including Insight Partners, Google Ventures, Quadrille Capital, General Catalyst, Quiet Capital, and other influential investors. Our reputation is further solidified by collaborations with numerous Fortune 100 companies. With Sanas, you're not just adopting a product; you're investing in the future of communication.

If you’re looking to have a significant role in roadmapping and driving technical directions, if you’re looking to deploy challenging and big ideas without much overhead or slowness, if you're looking to leave your mark on an ambitious, generational mission to change how the worlds thinks about speech + AI, then Sanas is a well-suited place for you.

About the Role

Sanas is looking for a Member of Technical Staff to lead the post-training and deployment of large language models across a new generation of self-hosted, sovereign-deployed products. This is a rare chance to own applied post-training work end-to-end for text workloads. This role sits at the center of taking strong open-source LLMs and adapting them - through fine-tuning, alignment, and inference optimization - into models that perform reliably in high-stakes, real-world, on-premise environments.

You'll be the technical bridge between what customers need and what actually ships. That means owning engagements end to end - scoping, adaptation, evaluation - and having full say over how text models get shaped and deployed. In between, you'll build the reusable tooling and workflows that make the next engagement faster than the last.

If you care about data quality, evaluation design, and making language models genuinely work in production, this is the role for you.

What You'll Do

• Lead efforts in instruction tuning, preference tuning, and model alignment to ensure models are helpful, safe, and performant in real-world applications.
• Own customer post-training projects end-to-end - from requirements through data generation, training, evaluation, and delivery.
• Customize open-source models for specific customer and product needs, ensuring a seamless path from post-training to serving production workloads.
• Improve inference-time efficiency, reliability, and robustness for high-stakes, real-world deployments - making models dramatically faster and cheaper to run while improving their capabilities.
• Provide technical mentorship and guidance to the team, fostering a culture of engineering excellence and rapid innovation.

What We're Looking For

We need someone who:

• Owns outcomes, not just tasks - carries customer post-training projects from first requirement to final delivery and evaluation, no handoffs mid-stream.
• Sees the whole pipeline as one system - data generation, instruction tuning, alignment, and evaluation aren't separate steps to them, they're one loop that has to work together.
• Cares about what ships, not what publishes - optimizes for model quality and customer outcomes over papers or theory for their own sake.
• Translates in both directions - turns customer needs into technical decisions internal teams can act on, and pushes back when the ask doesn't hold up.

Requirements

Must-have:

• 2+ years of experience building and deploying machine learning-based services in a production environment
• Hands-on experience with data generation and evaluation for LLM post-training
• Experience training or fine-tuning models using SFT, instruction tuning, RLHF, DPO, or similar preference alignment methods
• Strong intuition for text data quality and evaluation design
• Experience with text-specific post-training workflows: chat model alignment, instruction tuning, or text data curation at scale
• Proficiency with the open-source ML ecosystem (Hugging Face, PyTorch) and modern model architectures

Nice-to-have:

• Experience optimizing inference for reduced latency and higher concurrency, especially for on-premise deployments
• Experience improving system performance, efficiency, and scalability of deployed models and applications
• Experience serving low-precision (FP4/FP8) models, multiple LoRA adapters within one model instance (Multi-LoRA), or models distributed across several GPU nodes
• Experience developing large-scale, high-load production systems
• Experience maintaining or contributing to open-source ML projects
• Experience managing machine learning workloads on Kubernetes clusters
• Experience delivering applied ML work to external customers with measurable outcomes
• Familiarity with inference optimization frameworks (vLLM, SGLang, TensorRT)
• Experience building reusable ML tooling or evaluation infrastructure