Senior AI Systems and Algorithms Engineer

Description

The company is hiring a senior engineer to push foundation model work forward, covering training, post-training and deployment.

The work sits where large-scale distributed training, reinforcement learning for LLMs and VLMs, model efficiency, multimodal AI and open-source AI infrastructure meet.

The role covers the full generative AI pipeline, from preparing large datasets through training and post-training to inference optimization and framework development.

You will work with research, product and infrastructure teams to design new algorithms, improve existing systems and contribute to the company's open-source stack, which includes Megatron-LM, Megatron Bridge and NeMo-RL.

Responsibilities

• Build scalable systems that prepare high-quality multimodal datasets for training frontier foundation models.
• Create algorithms and systems that make pre-training and post-training larger in scale while cutting their cost and time.
• Develop techniques that speed up inference, lower the cost of deployment and serve models efficiently on both cloud and edge hardware.
• Write reusable infrastructure and add support for new models to the company's open-source GenAI training platform.

Requirements

• A master's or PhD in computer science, AI, applied mathematics or a related field, or equivalent experience.
• At least 5 years of relevant industry experience.
• A solid grounding in machine learning, deep learning and optimization.
• Strong Python and PyTorch engineering skills.
• Experience building high-performance software for large-scale AI systems.
• Strong skills in analysis, debugging and performance optimization.
• Excellent communication and collaboration skills.
• Highly desirable: experience with distributed training at scale using Megatron-LM and Megatron Bridge, including FSDP, TP/PP/CP/DP, heterogeneous or per-module parallelism, optimizer research, and efficient sparse or long-context attention.
• Highly desirable: experience with LLM and VLM post-training, including supervised fine-tuning, reinforcement learning methods such as PPO, GRPO and asynchronous RL, and large-scale RL frameworks such as NeMo-RL.
• Highly desirable: experience with inference efficiency, including quantization (FP8, NVFP4, INT4), pruning, knowledge distillation, neural architecture search, and diffusion or non-autoregressive language models.
• Highly desirable: experience contributing to open-source AI frameworks such as Megatron-LM, Megatron Bridge, NeMo-RL or Hugging Face Transformers, plus GPU performance tuning, distributed systems, latency and throughput analysis, and profiling of large AI workloads.

Conditions

Hybrid work model (LI-Hybrid).

This is an existing vacancy, and applications will be accepted at least until November 6, 2026.

The company uses AI tools in its recruiting process.

The company is an equal opportunity employer and does not discriminate on the basis of any protected characteristic.