ML Model Serving Engineer
Required skills
- PyTorch
- machine learning models
- serving
- high throughput
- low latency
- systems programming
- high-performance server systems
- VLLM
- PyTorch codebase
- performance engineering
Nice to have
- LLM serving
- SGlang deployment
- GCP
- AWS
- Azure
- Kubernetes
Role summary
Sesame is seeking an ML Model Serving Engineer to enhance their serving layer for LLM, speech, and vision models. This role involves partnering with ML infrastructure and training engineers to build a fast, cost-effective, and reliable serving infrastructure. Responsibilities include modifying LLM serving frameworks like VLLM and SGlang, optimizing models for high throughput and low latency using techniques like in-flight batching and caching, and reducing initialization times. Required qualifications include expertise in PyTorch, model serving optimization, systems programming, and performance engineering. Preferred qualifications include familiarity with LLM serving frameworks, cloud platforms (GCP, AWS, Azure), and Kubernetes/Ray.
About Sesame
Sesame believes in a future where computers are lifelike - with the ability to see, hear, and collaborate with us in ways that feel natural and human. With this vision, we're designing a new kind of computer, focused on making voice agents part of our daily lives. Our team brings together founders from Oculus and Ubiquity6, alongside proven leaders from Meta, Google, and Apple, with deep expertise spanning hardware and software. Join us in shaping a future where computers truly come alive.
Responsibilities:
Turbocharge our serving layer, consisting of a variety of LLM, speech, and vision models.
Partner with ML infrastructure and training engineers to build a fast, cost-effective, accurate, and reliable serving layer to power a new consumer product category.
Modify and extend LLM serving frameworks like VLLM and SGLang to take advantage of the latest techniques in high-performance model serving.
Work with the training team to identify opportunities to produce faster models without sacrificing quality.
Use techniques like in-flight batching, caching, and custom kernels to speed up inference.
Find ways to reduce model initialization times without sacrificing quality.
Required Qualifications:
Expert in some differentiable array computing framework, preferably PyTorch.
Expert in optimizing machine learning models for serving reliably at high throughput, with low latency.
Significant systems programming experience; ex. Experience working on high-performance server systems—you’d be just as comfortable with the internals of VLLM as you would with a complex PyTorch codebase.
Significant performance engineering experience; ex. Bottleneck analysis in high-scale server systems or profiling low-level systems code.
Always up to date on the latest techniques for model serving optimization.
Preferred Qualifications:
Familiarity with high-performance LLM serving; ex. experience with VLLM, SGlang deployment, and internals.
Experience with a public cloud platform such as GCP, AWS, or Azure.
Experience deploying and scaling inference workloads in the cloud using Kubernetes, Ray, etc.
You like to ship and have a track record of leading complex multi-month projects without assistance.
You’re excited to learn new things and work in a multitude of roles.
Sesame is committed to a workplace where everyone feels valued, respected, and empowered. We welcome all qualified applicants, embracing diversity in race, gender, identity, orientation, ability, and more. We provide reasonable accommodations for applicants with disabilities. Contact careers@sesame.com for assistance.
Full-time Employee Benefits:
401 (k) max employer match: 3.5% of compensation
100% employer-paid health, vision, and dental benefits for you and your dependents
Unlimited PTO and sick time
Flexible spending account with employer matching up to $1,650/year (medical FSA)
Guardian Employee Assistance Program (EAP)
Opportunity to share in the company's success with competitive stock options
Benefits do not apply to contingent/contract workers.
