2 days ago
Responsibilities
- Build RL and inference systems for agentic engineering workflows, including job orchestration, sampling, scoring, caching, experiment tracking, and reproducible evaluation.
- Develop infrastructure for long-horizon and high-latency reward tasks with validation times ranging from minutes to hours.
- Design staged rewards, proxy graders, sliced evaluation paths, retry strategies, and uncertainty-aware evaluation methods.
- Support candidate generation, benchmark execution, correctness checking, profiler feedback, reward modeling, and model-level improvement workflows.
- Build scalable inference and tool-use pipelines for LLM agents interacting with compilers, profilers, simulators, formal tools, benchmark harnesses, and internal knowledge sources.
- Standardize datasets, evaluations, run logs, leaderboards, failure taxonomies, and data collection for future training.
- Analyze experimental results and translate system behavior into improvements for models, agents, tools, and rewards.
- Collaborate with AI research scientists, applied engineers, infrastructure engineers, and hardware domain experts.
Requirements
- Bachelor’s degree in Computer Science, Computer Engineering, Electrical Engineering, Machine Learning, or a related field, or equivalent practical experience.
- Strong programming skills in Python and experience with ML frameworks such as PyTorch, JAX, or TensorFlow.
- Experience building ML systems, RL infrastructure, inference services, agent frameworks, evaluation platforms, or distributed experimentation systems.
- Strong understanding of inference, batching, sampling, latency, throughput, observability, and reliability tradeoffs.
- Ability to design reproducible experiments and evaluation pipelines with clear metrics, logs, and statistical discipline.
- Experience with reinforcement learning, RLHF, GRPO, preference optimization, reward modeling, reward shaping, or post-training systems is preferred.
- Experience with LLM agents, tool-use systems, code generation, automated program repair, compiler optimization, or benchmark-driven development is preferred.
- Experience with distributed systems, job orchestration, Kubernetes, Ray, Slurm, workflow engines, data pipelines, or large-scale experiment management is preferred.
- Familiarity with GPU systems, ROCm/HIP, CUDA, profiling, kernel benchmarking, model serving, or distributed training and inference is preferred.
- Exposure to hardware engineering workflows such as design, verification, firmware, simulation, or performance analysis is preferred.
- Publications or shipped systems in ML systems, RL, inference optimization, AI infrastructure, or hardware/software co-design are valued.
- A master’s degree is preferred and a PhD is a plus, particularly in ML systems, reinforcement learning, distributed systems, GPU computing, or AI infrastructure.
Tech Stack
Categories
About AMD
AMD designs and sells CPUs, GPUs, and adaptive/embedded computing products for PCs, data centers, gaming, and edge devices. Its portfolio includes Ryzen and EPYC processors, Radeon and Instinct graphics, and adaptive SoCs from its Xilinx acquisition, sold to OEMs, cloud providers, and device makers. Founded in 1969 and headquartered in Santa Clara, it is a public company on NASDAQ and supplies semi-custom chips for major game consoles.