Team Lead, ML

KIEFER

Lead ML Engineering team at Kiefer Tech delivering cutting-edge AI solutions. Coordinate workflows and mentor team members in a hybrid or fully remote environment.

Posted 4/27/2026full-timeAthens • 🇬🇷 GreeceSeniorWebsite

About the role

Key responsibilities & impact

Lead and orchestrate the ML Engineering team — coordinate efforts, remove blockers, and ensure alignment with company goals
Design and implement structural frameworks: workflows, ownership boundaries, and accountability across all ML workstreams
Raise the technical bar — establish engineering best practices in line with top-tier AI organizations (OpenAI, Google, Qwen-level standards)
Own the end-to-end ML roadmap: take full accountability for delivery, quality, and measurable business impact
Develop team members — mentor individual contributors, support career growth, and build a culture of continuous learning
Drive AI innovation and stay ahead of industry trends — bring new capabilities, tools, and methodologies into the team's practice

Requirements

What you’ll need

Proven experience leading and growing ML engineering teams in high-stakes, fast-paced environments
Background from top-tier AI organizations (e.g., Google, OpenAI, Qwen, or equivalent) with demonstrated ability to apply elite-level engineering standards
Deep expertise in ASR (Automatic Speech Recognition) technologies and production-grade ML systems
Strong knowledge of large-scale ML model development, CPT, fine-tuning, and RL. Familiar with model validation tools
Strong knowledge of model inference optimization, model quantization, experience with vLLM, SGLang, Nvidia Triton/Dynamo etc
High autonomy and self-direction — a strong self-learner who seeks knowledge in communities, follows AI research closely, and translates trends into team action
Exceptional communication skills — able to create clarity from ambiguity and align cross-functional stakeholders

Benefits

Comp & perks

Compensation: upper-quartile for the EU market, aligned to top-tier AI talent benchmarks
Ownership: full accountability over ML Engineering — you define the function, the standards, and the roadmap
Remote: fully remote within the EU, or hybrid from our Athens office
AI-native environment: real challenges across LLMs, ASR, GPU workloads, and advanced AI product development
Growth: dedicated budget for conferences, courses, and certifications — direct path to Head of ML as the function scales
Culture: engineering-first, high autonomy, low bureaucracy — your ideas shape the company's AI direction

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills & Tools

ML engineeringASR technologieslarge-scale ML model developmentCPTfine-tuningreinforcement learningmodel validation toolsmodel inference optimizationmodel quantizationvLLM

Soft Skills

leadershipmentoringcommunicationself-directionautonomyproblem-solvingteam alignmentcontinuous learningstakeholder managementinnovation