Hybrid AI Systems Engineer – Inference Frameworks

at adaption

• Job title: AI Systems Engineer – Inference Frameworks
• Job type: Full Time
• Experience level: Mid level, Senior
• Salary: Not specified
• Degree requirement: No Education Requirement
• Tech skills: Python
• Location requirements: Hybrid, San Francisco, United States

About the role

• AI Systems Engineer designing and building inference and optimization systems for core product. Work directly with founders, thriving in a zero-to-one environment.

Responsibilities

• You’ll work directly with our founders to design and build the inference and optimization systems that power our core product.
• This role bridges research and production, combining deep exploration of inference techniques with hands-on ownership of scalable, high-performance serving infrastructure.
• You’ll own the full lifecycle of LLM inference—from experimentation and performance analysis to deployment and iteration in production—thriving in a zero-to-one environment and helping define the technical foundations of our inference stack.
• design and build our LLM inference stack from zero to one, exploring and implementing advanced techniques for low-latency, high-throughput serving of language and multimodal models.
• develop and optimize inference using modern frameworks (e.g., vLLM, SGLang, TensorRT-LLM), experimenting with batching strategies, KV-cache management, parallelism, and GPU utilization to push performance and cost efficiency.
• collaborate closely with founders and model developers to analyze bottlenecks across the stack, co-optimizing model execution, infrastructure, and deployment pipelines.

Requirements

• Strong experience building and optimizing LLM inference systems in production or research environments
• Hands-on expertise with inference frameworks such as vLLM, SGLang, TensorRT-LLM, or similar
• Deep performance mindset with experience in GPU-backed systems, latency/throughput optimization, and resource efficiency
• Solid understanding of transformer inference, serving architectures, and KV-cache–based execution
• Strong programming skills in Python; experience with CUDA, Triton, or C++ a plus
• Comfort working in ambiguous, zero-to-one environments and driving research ideas into production systems
• Nice to have: experience with model quantization or pruning, speculative decoding, multimodal inference, open-source contributions, or prior work in systems or ML research labs

Benefits

• Flexible work: In-person collaboration in the Bay Area, a distributed global-first team, and quarterly offsites.
• Adaption Passport: Annual travel stipend to explore a country you've never visited. We're building intelligence that evolves alongside you, so we encourage you to keep expanding your horizons.
• Lunch Stipend: Weekly meal allowance for take-out or grocery delivery.
• Well-Being: Comprehensive medical benefits and generous paid time off.