You will work closely with Scale’s ML teams and researchers to build the foundation platform which supports all our ML research and development works. You will be building and optimising the platform to enable our next generation LLM training, inference and data curation.

Key responsibilities include:

• Building, profiling and optimising our training and inference framework.
• Collaborating with ML and research teams to accelerate their research and development, and enable them to develop the next generation of models and data curation.
• Researching and integrating state-of-the-art technologies to optimise our ML system.

The ideal candidate will have:

• Passionate about system optimisation.
• Experience with multi-node LLM training and inference.
• Experience with developing large-scale distributed ML systems.
• Experience with post-training methods like RLHF/RLVR and related algorithms like PPO/GRPO etc.
• Strong software engineering skills, proficient in frameworks and tools such as CUDA, PyTorch, transformers, flash attention, etc.

Nice to haves include demonstrated expertise in post-training methods and/or next generation use cases for large language models including instruction tuning, RLHF, tool use, reasoning, agents, and multimodal, etc.

Compensation packages at Scale for eligible roles include base salary, equity, and benefits. The range displayed on each job posting reflects the minimum and maximum target for new hire salaries for the position, determined by work location and additional factors, including job-related skills, experience, interview performance, and relevant education or training.

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Our mission is to create AI systems that can accurately understand the universe and aid humanity in its pursuit of knowledge. Our team is small, highly motivated, and focused on engineering excellence.

As a Member of Technical Staff - Inference, you will be responsible for optimising the latency and throughput of model inference, building reliable and performant production serving systems to serve billions of users, accelerating research on scaling test-time compute and rollout in reinforcement learning training, and model-hardware co-design for next-generation architectures.

To be successful in this role, you will need to have worked on system optimisations for model serving, such as batching, caching, load balancing, and parallelism, worked on low-level optimisations for inference, such as GPU kernels and code generation, worked on algorithmic optimisations for inference, such as quantisation, distillation, and speculative decoding, and low-precision numerics, worked on large-scale inference engines or reinforcement learning frameworks, worked on large-scale, high-concurrent production serving, and worked on testing, benchmarking, and reliability of inference services.

The base salary for this role is $180,000 - $440,000 USD, and we offer a comprehensive total rewards package, including equity, comprehensive medical, vision, and dental coverage, access to a 401(k) retirement plan, short & long-term disability insurance, life insurance, and various other discounts and perks.

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