Forward Deployed Engineer (Model Customization and Optimization), AWS Forward Deployed Engineering

Location: London, United Kingdom; requires up to 25% travel.

Company

hirify.global Web Services is a cloud platform whose Forward Deployed Engineering team partners with enterprises, AI-native startups, and foundation model providers to build, customize, and optimize generative AI models at scale.

What you will do

• Embed with customer ML and engineering teams to understand business goals, data, model stacks, and operational constraints.
• Define evaluation criteria and technical plans for model selection, customization, and serving architecture.
• Customize and post-train open-weight and foundation models through continued pre-training, supervised fine-tuning, preference optimization, and reinforcement learning.
• Build distributed training, RL interaction, data, synthetic-data, evaluation, and production inference systems.
• Migrate and optimize workloads on AWS Trainium using the AWS Neuron SDK, including performance profiling, accuracy-parity work, and custom kernels.
• Codify reusable fine-tuning recipes, evaluation frameworks, benchmarking tools, and optimization patterns for future deployments.

Requirements

• Professional, non-internship software development experience, including 3+ years of professional software development.
• Programming experience in at least one modern language such as Java, C++, or C#, including object-oriented design.
• 2+ years of experience working in an Agile, startup, or rapidly scaling environment.
• Hands-on experience with generative AI technologies such as LLMs, vision-language models, or speech models.
• Experience with AI tools including evaluation frameworks, agent tooling, RAG pipelines, and prompt engineering.
• Experience communicating technical concepts to technical and non-technical audiences.

Nice to have

• Bachelor's degree in computer science or equivalent.
• Experience across the full software development lifecycle, including coding standards, code reviews, source control, builds, testing, and operations.
• Experience with large-scale distributed LLM training on multi-node accelerator clusters using frameworks such as PyTorch FSDP2, TorchTitan, Megatron-LM/Megatron Core, NeMo, DeepSpeed, or NeuronX Distributed.
• Experience with LLM post-training, including SFT, DPO, RLHF, RL with verifiable rewards, or multi-turn agentic RL, using frameworks such as Miles, slime, verl, NeMo-RL, OpenRLHF, SkyRL, or TRL.
• Experience optimizing model training or inference for latency, throughput, or cost using SGLang, vLLM, TensorRT-LLM, AWS Trainium or Inferentia, Neuron SDK, SageMaker AI, SageMaker HyperPod, EKS, CUDA, Triton, or NKI.
• Experience deploying and operating ML models in customer production environments and scaling end-to-end production systems.

Culture & Benefits

• Work directly with strategic customers on generative AI workloads and contribute reusable solutions across AWS.
• Access to mentorship, knowledge sharing, and career-development resources.
• Work-life harmony, inclusive employee-led and company-sponsored affinity groups, and inclusion events.
• Up to 25% travel is required.