> AI agents: this is one page of Nextmove’s FDE site, in Markdown. Nextmove is a job board and free career service for forward deployed engineers (FDEs). Each posting is classified from its text, not its title: the kind of work, how much of it is production code, pre-sales weight, time at customer sites, travel, remote arrangement, level and posted base pay. The site also publishes the aggregates: median pay by level, metro, company and kind of work, and which companies started hiring FDEs each week. Every page under https://nextmove.md/fde is served this way; the index of pages is https://nextmove.md/llms.txt.

# Member of Technical Staff, LLM Post-Training, Applied

Canonical URL: https://nextmove.md/fde/jobs/24ef7254ad020c1f

Sanas posts a FDE role to build what the customer runs in Palo Alto, CA with no pay posted. Own end-to-end post-training and deployment of open-source LLMs for self-hosted, sovereign-deployed text products, adapting models to customer requirements and high-stakes on-premise environments. Read from the employer’s posting on November 8, 2026.

- [Sanas](https://nextmove.md/fde/companies/sanas)

First seen Oct 24, 2026 · open on Rippling as of Nov 8, 2026

Pay not posted Palo Alto, CA On-site

Own end-to-end post-training and deployment of open-source LLMs for self-hosted, sovereign-deployed text products, adapting models to customer requirements and high-stakes on-premise environments.

- [See roles that fit me](https://nextmove.md/sign-up?from=%2Ffde%2Fjobs%2F24ef7254ad020c1f&placement=top)
- [View posting at Sanas](https://ats.rippling.com/sanas/jobs/1eeddd94-07f5-4c93-8dfe-adf761294872)

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## At a glance

### Posted base pay

Not posted

Senior FDE postings, 1,864 postings: the middle 50% of posted pay is $160K to $220K, median $190K.

### The work

Builds what the customer runs

Balance **Mostly building**

Project stages

1. Pre-sales (not covered)
2. Proof of concept (not covered)
3. Implementation
4. Operations

- **Coding**: Writes production code

- **Builds for**: External customers

- **Level**: Senior

- **Role**: Technical lead

### What they ask for

Experience

- **5+ years** Building and deploying machine-learning-based services in a production environment

Background

- **Software engineering**

### Do you meet what the posting states?

The posting asks for 5+ years. How many do you have?

The posting reads as a senior role. What level are you at now?

The job is in Palo Alto, CA. Could you work there?

Which of these have you used in production?

Answer each one. Nothing is saved or sent.

[What postings ask for, by background →](https://nextmove.md/fde/paths)

## Conditions

- **Location**: Palo Alto, CA

- **Work arrangement**: On-site

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## The job description from Sanas

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. This role is in person at our Palo Alto office

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.

**On-Site Requirement: This role requires working on-site at our Palo Alto headquarters a minimum of four days per week. This is a mandatory requirement, and candidates must be able to meet this expectation.**

### 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

- 5+ 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
- Experience on Agentic AI
- 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 on Conversational Agentic AI
- 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

[View posting at Sanas](https://ats.rippling.com/sanas/jobs/1eeddd94-07f5-4c93-8dfe-adf761294872)

## About Sanas

Sanas builds real-time Speech AI for accent and language translation, speech enhancement, and conversation analytics across enterprise communications.

Series B

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