---
title: 'Lead Software Engineer, Model Serving Platform at Sciforium'
canonical: 'https://feeny.ai/job/lead-software-engineer-model-serving-platform-sciforium-san-francisco-sbdhk82sxhx3'
type: 'job'
last_seen: '2026-09-06'
---

# Lead Software Engineer, Model Serving Platform at Sciforium

- **Company:** Sciforium
- **Location:** San Francisco, CA
- **Employment:** full-time
- **Work type:** onsite
- **Posted:** 2026-08-17
- **Last confirmed live:** 2026-09-06
- **Apply:** https://jobs.ashbyhq.com/sciforium/52b0fbe3-9e4e-4a80-b2d0-24440db847d4

## Job description

Sciforium is an AI infrastructure company developing next-generation multimodal AI models and a proprietary, high-efficiency serving platform. Backed by multi-million-dollar funding and direct sponsorship from AMD with hands-on support from AMD engineers the team is scaling rapidly to build the full stack powering frontier AI models and real-time applications.

## ABOUT THE ROLE

This is a rare chance to help architect and lead the development of Sciforium’s next-generation model serving platform, the high-performance engine that will bring a multimodal, highly efficient foundation model to market. As a senior technical leader, you’ll not only build core components yourself but also guide and mentor other engineers, influencing engineering direction, standards, and execution quality.

You will learn and shape the full AI stack: from GPU kernels and quantized execution paths to distributed serving, scheduling, and the APIs that power real-time AI applications. If you enjoy deep systems work, thrive on ownership, and want to lead engineers in building foundational AI infrastructure, this role puts you at the center of SciForium’s mission and growth.

## WHAT YOU'LL DO

- Lead the technical direction of the model serving platform, owning architecture decisions and guiding engineering execution.
- Build core serving components including execution runtimes, batching, scheduling, and distributed inference systems.
- Develop high-performance C++ and CUDA/HIP modules, including custom GPU kernels and memory-optimized runtimes.
- Collaborate with ML researchers to productionize new multimodal models and ensure low-latency, scalable inference.
- Build Python APIs and services that expose model capabilities to downstream applications.
- Mentor and support other engineers through code reviews, design discussions, and hands-on technical guidance.
- Drive performance profiling, benchmarking, and observability across the inference stack.
- Ensure high reliability and maintainability through testing, monitoring, and engineering best practices.
- Troubleshoot and resolve complex issues across GPU, runtime, and service layers.

## IDEAL CANDIDATE PROFILE

- Bachelor’s degree in Computer Science, Computer Engineering, Electrical Engineering, or equivalent practical experience
- 5+ years of experience designing and building scalable, reliable backend systems or distributed infrastructure.
- Strong understanding of LLM inference mechanics (prefill vs decode, batching, KV cache)
- Experience with Kubernetes/Ray, Containerization
- Strong proficiency in C++, Python.
- Strong debugging, profiling, and performance optimization skills at the system level.
- Ability to collaborate closely with ML researchers and translate model or runtime requirements into production-grade systems.
- Effective communication skills and the ability to lead technical discussions, mentor engineers, and drive engineering quality.
- Comfortable working from the office and contributing to a fast-moving, high-ownership team culture.

## NICE-TO-HAVE

- Experience with ML systems engineering, distributed GPU scheduling, open source inference engine like vLLM, Sglang, or TRT-LLM
- Experience in building large scale ML/MLOps infrastructure
- Proficiency in CUDA or ROCm and experience with GPU profiling tools
- Experience at an AI/ML startup, research lab, or Big Tech infrastructure/ML team.
- Familiarity with multimodal model architectures, raw-byte models, or efficient inference techniques.
- Contributions to open-source ML or HPC infrastructure

## BENEFITS INCLUDE

- Medical, dental, and vision insurance
- 401k plan
- Daily lunch, snacks, and beverages
- Flexible time off
- Competitive salary and equity

## EQUAL OPPORTUNITY

Sciforium is an equal opportunity employer. All applicants will be considered for employment without attention to race, color, religion, sex, sexual orientation, gender identity, national origin, veteran or disability status.

## About Sciforium

## Company Overview
- **One-liner**: Sciforium is building a vertically integrated AI infrastructure platform that owns its hardware (AMD GPUs) to deliver cost-effective, high-performance inference and foundation model training across text, image, video, and audio modalities.
- **Entity Type**: Private (Seed Stage)
- **Headquarters**: San Francisco, California, United States
- **Founded**: 2024
- **Founders**: Hassan Akbari

## Core Business
- **Primary Industry**: AI Infrastructure / Generative AI
- **Target Customers**: B2B; AI teams and enterprises that need scalable, multimodal AI inference and model serving without managing their own infrastructure.
- **Mission**: To rebuild AI serving infrastructure from the ground up—owning the hardware and optimizing the entire pipeline end to end—so that any team, regardless of size or budget, can access the best AI capabilities across every modality without compromise.

## Products & Services
- **AI Inference API**: A drop-in replacement for the OpenAI API format, supporting streaming, tool use, structured outputs, and async generation. Runs on Sciforium’s own AMD hardware for lower cost and stronger privacy.
- **Evaluation Platform**: Built-in pipelines to monitor model performance in real time, catch regressions, and benchmark across models.
- **Native Agents Infrastructure**: Serverless platform for running AI agents at scale without managing servers.
- **Model Library**: Access to state-of-the-art open-source models across text, image, video, and audio (e.g., DeepSeek, Wan2, speech models).

## Market Standing
- **Valuation/Market Cap**: Not disclosed
- **Key Metric**: Total Funding of $3.9M (Seed round closed June 2024)
- **Notable Investors/Partners**: Backed by AMD and SignalFire
- **Growth Signals**: Headcount grew 160% YoY to 8 employees; 10 active job postings as of mid-2025; 99.98% uptime claimed; N+1 power and N+2 cooling redundancy with liquid-cooled infrastructure.

## Competitive Advantages
- **Vertical Integration**: Owns its own AMD hardware and runs its own data centers, removing intermediaries and markups—leading to lower costs and predictable performance.
- **Multimodal Native**: Built from the ground up to handle text, image, video, and audio in a single API, unlike many competitors that focus on text-only.
- **Privacy & Control**: By running on dedicated infrastructure (not shared servers), customers get stronger data privacy guarantees.
- **High Ambition Culture**: Team includes alumni from Google DeepMind, Microsoft, Amazon, Snowflake, Qualcomm, and Columbia University.

## Strategic Focus
- **Infrastructure Ownership**: Continuing to invest in its own AMD GPU clusters and data center operations to maintain cost and performance advantages.
- **Multimodal Expansion**: Scaling support for all data types (text, image, video, audio) with a single API.
- **Agent Readiness**: Building native support for AI agents at scale.
- **Open-Source Ecosystem**: Supporting the latest open-source models on day one.

## Why Work Here
- **Culture**: Highly independent, self-motivated, and creative environment. Small enough that your work is visible from day one. Principles include relentless quality, outcome ownership, and high ambition.
- **Work Policy**: Hybrid and in-office roles available. Offices in San Francisco (HQ) and Los Altos, California.
- **Team**: Small, high-caliber team with deep experience from Google DeepMind, Snowflake, Amazon, Qualcomm, and other top AI/infra companies.
- **Perks**: Work on hard infrastructure problems that matter, at a company backed by AMD and SignalFire. Opportunity to shape the foundation of AI infrastructure from an early stage.

## Sources
1. [sciforium.com](https://sciforium.com/)
2. [sciforium.com/company](https://sciforium.com/company)
3. [linkedin.com/company/sciforium](https://www.linkedin.com/company/sciforium)
4. [builtin.com/company/sciforium](https://builtin.com/company/sciforium)
5. [jobs.ashbyhq.com/sciforium](https://jobs.ashbyhq.com/sciforium)

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