Senior Backend Engineer, Inference Platform
Together AI is hiring a Senior Backend Engineer, Inference Platform in San Francisco, United States. It pays $200k-$290k a year and Level rates it ; you can apply on Together AI.
Level's read on this job
- Level rates this software engineer role Builds AI ●●●●: the job is building AI systems.
- Pay on this posting starts 19% higher than the typical software engineer posting on Level.
- The posting mentions vLLM, Cursor and Elevenlabs among its tools and skills.
- Level classes it as a senior role.
- Together AI has 84 other open roles on Level.
AI in this role
About the Role
Together AI is building the Inference Platform that brings the most advanced generative AI models to the world. Our platform powers multi-tenant serverless workloads and dedicated endpoints, enabling developers, enterprises, and researchers to harness the latest LLMs, multimodal models, image, audio, video, and speech models at scale.
If you get a thrill from optimizing latency down to the last millisecond, this is your playground. You’ll work hands-on with tens of thousands of GPUs (H100s, H200s, GB200s, and beyond), figuring out how to fully utilize every FLOP and every gigabyte of memory.
You’ll collaborate directly with research teams to bring frontier models into production, making breakthroughs usable in the real world. Our team also works closely with the open source community, contributing to and leveraging projects like SGLang, vLLM, and NVIDIA Dynamo to push the boundaries of inference performance and efficiency.
- Shape the core inference backbone that powers Together AI’s frontier models.
- Solve performance-critical challenges in global request routing, load balancing, and large-scale resource allocation.
- Work with state-of-the-art accelerators (H100s, H200s, GB200s) at global scale.
- Partner with world-class researchers to bring new model architectures into production.
- Collaborate with and contribute to the open source community, shaping the tools that advance the industry.
- A culture of deep technical ownership and high impact — where your work makes models faster, cheaper, and more accessible.
- Competitive compensation, equity, and benefits.
Responsibilities
- Build and optimize global and local request routing, ensuring low-latency load balancing across data centers and model engine pods.
- Develop auto-scaling systems to dynamically allocate resources and meet strict SLOs across dozens of data centers.
- Design systems for multi-tenant traffic shaping, tuning both resource allocation and request handling — including smart rate limiting and regulation — to ensure fairness and consistent experience across all users.
- Engineer trade-offs between latency and throughput to serve diverse workloads efficiently.
- Optimize prefix caching to reduce model compute and speed up responses.
- Collaborate with ML researchers to bring new model architectures into production at scale.
- Continuously profile and analyze system-level performance to identify bottlenecks and implement optimizations.
Requirements
- 5+ years of demonstrated experience building large-scale, fault-tolerant, distributed systems and API microservices.
- Strong background in designing, analyzing, and improving efficiency, scalability, and stability of complex systems.
- Excellent understanding of low-level OS concepts: multi-threading, memory management, networking, and storage performance.
- Expert-level programming in one or more of: Rust, Go, Python, or TypeScript.
- Knowledge of modern LLMs and generative models and how they are served in production is a plus.
- Experience working with the open source ecosystem around inference is highly valuable; familiarity with SGLang, vLLM, or NVIDIA Dynamo will be especially handy.
- Experience with Kubernetes or container orchestration is a strong plus.
- Familiarity with GPU software stacks (CUDA, Triton, NCCL) and HPC technologies (InfiniBand, NVLink, MPI) is a plus.
- Bachelor’s or Master’s degree in Computer Science, Computer Engineering, or related field, or equivalent practical experience.
About Together AI
Together AI, the AI Native Cloud, is purpose-built for AI engineers. AI application developers get high-performance inference that scales reliably, fine-tuning and reinforcement learning for creating frontier-level specialized models, and pre-training at massive scale for fully custom intelligence, all around a marketplace of leading open models that teams can run, adapt, and own. Trusted by Cursor, Decagon, ElevenLabs, Salesforce, and Zoom, Together serves 400+ trillion tokens a month.
Compensation
We offer competitive compensation, startup equity, health insurance and other competitive benefits. The US base salary range for this full-time position is: $200,000 - $290,000 + equity + benefits. Our salary ranges are determined by location, level and role. Individual compensation will be determined by experience, skills, and job-related knowledge.
Equal Opportunity
Together AI is an Equal Opportunity Employer and is proud to offer equal employment opportunity to everyone regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity, veteran status, and more.
Please see our privacy policy at https://www.together.ai/privacy
How we rate this
Senior Backend Engineer, Inference Platform at Together AI rates 99 out of 100 for how much of the daily work is AI. That makes it Builds AI (AI Level 4 of 4). The level is about AI in the job, not seniority.
Builds AI. The job is building AI systems.
- ●●●● Builds AI80 to 100
- ●●●○ Works on AI60 to 79
- ●●○○ Uses AI40 to 59
- ●○○○ Little AI0 to 39
Levels come from how often the tools, models and workflows of the role are named in the posting itself. Read the description and count the AI tools, models and workflows it names.
Prepare for this job
Built from this posting and from similar jobs on Level.
Skills and AI tools this role asks for
Pay
- This job starts at
- $200k
- Typical software engineer job
- $168k
This posting starts 19% above the typical software engineer job. See software engineer jobs
Questions you could be asked
- Walk me through fine-tuning a model: what data did you use, and how did you check the result?
- Walk me through how you've used vLLM in your day-to-day work.
- What are the limits of Cursor that you've run into, and how did you work around them?
- What's a project where you used Elevenlabs hands-on?
- Walk me through how you've used Decagon in your day-to-day work.
Adapt your resume
- List these exact terms on your resume: Fine-tuning, vLLM, Cursor, Elevenlabs, and Decagon. An applicant tracking system matches the wording, not the idea.
- Attach one line of real, concrete experience to at least one of them. A tool named with nothing behind it rarely survives a human read.
- Lead with what you built, trained or shipped. This role is judged on the AI system itself, not the tools around it.
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