Principal Machine Learning Engineer (AI)

TL;DR

Principal Machine Learning Engineer (AI): Turning research direction into production-grade ML systems for a proactive AI that understands context across conversations, plans actions, and carries work forward. Focus on building end-to-end ML pipelines for data, training, evaluation, inference, and deployment, fine-tuning models with LoRA, QLoRA, SFT, DPO, distillation, and architecting scalable inference systems under production constraints.

Location: Remote (United Kingdom)

Company

hirify.global is building A1, a proactive AI system integrated into backend, mobile, and desktop products.

What you will do

• Build and own end-to-end ML pipelines spanning data, training, evaluation, inference, and deployment.
• Fine-tune and adapt models using state-of-the-art methods like LoRA, QLoRA, SFT, DPO, and distillation.
• Architect scalable inference systems balancing latency, cost, and reliability, with GPU optimization.
• Design data systems for high-quality synthetic and real-world training data.
• Implement evaluation pipelines for performance, robustness, safety, and bias.
• Own production deployment and collaborate with application engineering for clean integrations.

Requirements

• Located in the United Kingdom for remote work.
• Strong background in deep learning and transformer-based architectures.
• Hands-on experience training, fine-tuning, or deploying large-scale ML models in production.
• Proficiency with PyTorch, JAX, or similar ML frameworks; distributed training frameworks like DeepSpeed, FSDP.
• Strong software engineering for robust, production-grade systems; GPU optimization experience.
• Comfort owning ambiguous, zero-to-one ML systems end-to-end with a bias toward shipping quickly.

Nice to have

• LLM inference frameworks like vLLM, TensorRT-LLM.
• Open-source contributions to ML or systems libraries.
• Background in scientific computing, compilers, GPU kernels.
• RLHF pipelines (PPO, DPO, ORPO); multimodal or diffusion models.
• Large-scale data processing (Apache Arrow, Spark, Ray).

Culture & Benefits

• High talent density, hands-on small world-class teams.
• Collective decision-making, rapid speed, balance between quality and learning.
• Bring structure, exercise judgment, execute independently.

Hiring process

• Applications evaluated by technical team; 3-4 virtual or onsite interviews.
• Prompt decisions with transparency and efficiency.
• Offers extended to those demonstrating exceptional skills and mindset.