Technical Lead, Machine Learning

🕒 July 24

🇪🇺 Europe – Remote

⏰ Full Time

🟠 Senior

🤖 Machine Learning Engineer

👻 Ghost score 39%

What it means: how likely this posting is sitting open without active hiring.How to read it: 60% or higher means we think this is a ghost. Lower scores are less of a worry.How we score it: age, reposts, open roles vs team size, how fast jobs close, and whether pay is listed.Fine print: an estimate from public data, not a judgment of the company. Real jobs can score high, so treat it as a hint, not a fact.

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OnHires

WebsiteLinkedInAll Job Openings

11 - 50 employees

Founded 2020

🎯 Recruiter

👥 HR Tech

🤝 B2B

Recruitment • HR Tech • B2B

OnHires is a global IT recruitment and staffing partner that sources, vets, and places technical and creative talent for fast-growing companies. They provide services including vetted developers, sourcing, executive search, talent mapping, RPO, HR consulting, and flexible hiring models (subscription, pay-per-hire, hourly), with a focus on fast, high-quality hires and scalable talent pipelines.

📋 Description

• Design and scale production ML systems for LLM-based applications • Build training and evaluation pipelines for continuous model improvement • Fine-tune foundation models using LoRA, QLoRA, SFT and DPO • Optimize inference performance, latency and GPU utilization • Develop high-quality training datasets and evaluation frameworks • Own deployment, monitoring and reliability of ML services in production • Define engineering standards and mentor a small ML team • Partner closely with product and backend engineers • Help shape the platform and engineering culture as part of the founding engineering team

🎯 Requirements

• Experience building and shipping production ML systems used by real users • Strong Python and PyTorch (or JAX) experience • Hands-on experience with LLM fine-tuning and inference optimization • Deep understanding of ML infrastructure, distributed training or GPU-based systems • Strong engineering mindset with a focus on scalability, reliability and code quality • Comfortable taking ownership in a fast-moving startup environment • Experience with LoRA, QLoRA, SFT and DPO • Experience with production ML infrastructure, including distributed training, GPU infrastructure, LLM inference optimization, ML deployment, MLOps, Kubernetes, evaluation and monitoring pipelines

🏖️ Benefits

• Competitive cash compensation plus equity • Remote-first environment • Long-term growth opportunities • Significant technical ownership • Opportunity to influence product architecture from the ground up