Member of Technical Staff - ML Systems

NextToken AI Inc.; Mountain View (CA); Presencial; USD 180 000 - 250 000; Tempo integral

Resumo da oferta

NextToken AI Inc. is building scalable ML infrastructure to improve model performance in production environments. You’ll work across training systems, model serving, and distributed data pipelines, shaping reliability and efficiency for researchers and engineers.

You’ll design and implement end-to-end experiments, optimize resource usage, and diagnose bottlenecks across GPUs, memory, and scheduling. You’ll collaborate with researchers and customers to turn ideas into dependable training and

Qualificações

• Experience in ML infrastructure and training/serving systems.
• Ability to explain architectural decisions and measurable improvements.
• Knowledge of PyTorch or JAX and profiling workloads.
• Strong distributed systems fundamentals: scheduling, storage, recovery.
• Experience with distributed training frameworks and container orchestration.

Responsabilidades

• Develop infrastructure for post-training experiments.
• Improve resource allocation and parallel execution.
• Make model serving efficient and diagnose bottlenecks.
• Build scalable data generation and evaluation pipelines.
• Contribute to benchmarks, observability, and automation.

Conhecimentos

ML infrastructure

Training systems

Model serving

Distributed data pipelines

Ferramentas

CUDA

Triton

PyTorch

JAX

Descrição da oferta de emprego

NextToken builds specialized models and the systems that help them improve. We work with AI product teams to turn production traces, expert feedback, and real tasks into evaluations, training data, and more capable models. Our goal is to make it practical for companies to develop intelligence of their own: models that understand their work and improve as that work evolves.

Our founding team has built search infrastructure and post-trained models at frontier AI labs and on large-scale product teams. You’ll work directly with the founders on research and engineering, help shape our technical direction, and see your work deployed in customer products.

The role

You’ll build the systems that make model improvement reliable and repeatable, from generating data and running experiments to evaluating and deploying checkpoints. Researchers and customer teams should be able to understand a run, recover from failures, and use its results without rebuilding the process each time.

You’ll work across data pipelines, distributed execution, training, and serving, with initial responsibility shaped around your strongest area. An important part of the job is deciding where existing tools fit and where the gaps lie.

What you’ll work on

Training and reinforcement learning

Develop the infrastructure for post-training experiments, from distributed rollout execution to training and checkpoint evaluation. Improve resource allocation, parallel execution, and recovery so researchers can run more experiments and spend less time managing them.

Inference and performance

Make model serving efficient for both agent workloads and training data generation. Investigate bottlenecks in batching, KV-cache use, scheduling, and memory movement, and evaluate changes against latency, throughput, cost, and model quality. Depending on your specialty, this can include kernel and runtime optimization with CUDA, Triton, or related tools.

Data generation and environments

Build scalable systems for collecting trajectories, coordinating teacher models, and running agent environments. Make data generation and evaluation reproducible, with reliable execution and clear connections between datasets and the experiments that use them.

Research tooling and deployment

Develop benchmarks, observability, and automated experiment workflows that help researchers test ideas and understand results. Work with researchers and customers to turn new methods into dependable training and deployment capabilities, and share useful systems work through technical writing and open-source contributions where appropriate.

What we’re looking for

Technical depth in ML infrastructure, with experience in training systems, model serving, or distributed data and rollout pipelines. You can explain the architectural decisions and measurable improvements in work you've owned.

Knowledge of how PyTorch, JAX, or similar frameworks operate beneath their APIs, with the ability to follow execution through the code, profile workloads, and diagnose correctness or performance issues.

Strong distributed systems fundamentals, including resource scheduling, communication, storage, and recovery. You can reason about how these interact in demanding ML workloads.

Understanding of the execution demands of post-training: how sampling, reward evaluation, optimization, and checkpoint updates fit together, and where efficiency or reliability can break down.

Ability to evaluate systems changes through reproducible measurements and work with researchers and customers to decide which problems matter most.

Experience with distributed training frameworks, vLLM or SGLang, GPU profiling, container orchestration, or research platforms is particularly relevant. CUDA, Triton, and compiler or runtime expertise are useful specializations. We value depth in one area and the ability to work across the surrounding stack; kernel expertise is not required.

We welcome engineers from ML infrastructure teams, research groups, and startups, including earlier-career engineers with a substantial systems project. The depth of your contribution matters more than a particular title or tenure.