Machine Learning Technical Lead, Artificial Intelligence

TTEC

Machine Learning Technical Lead overseeing the execution of scalable AI systems in a remote setup. Responsible for end-to-end ML processes and collaborating with research and engineering teams.

Posted 7/10/2026full-timeRemote • California • 🇺🇸 United StatesSenior💰 $190,000 - $225,000 per yearWebsite

Tech Stack

Tools & technologies

PythonPyTorch

About the role

Key responsibilities & impact

Own end-to-end ML system execution: data pipelines, training workflows, evaluation systems, inference architecture, and deployment.
Fine-tune and adapt models using state-of-the-art methods such as LoRA, QLoRA, SFT, DPO, and distillation.
Architect and operate scalable inference systems, balancing latency, cost, and reliability.
Design and maintain data systems for high-quality synthetic and real-world training data.
Implement evaluation pipelines covering performance, robustness, safety, and bias, in partnership with research leadership.
Own production deployment, including GPU optimization, memory efficiency, latency reduction, and scaling policies.
Collaborate closely with application engineering to integrate ML systems cleanly into backend, mobile, and desktop products.
Make pragmatic trade-offs and ship improvements quickly, learning from real usage.
Work under real production constraints: latency, cost, reliability, and safety

Requirements

What you’ll need

Experience building or shipping real Machine Learning systems used by people, not just demos.
Artificial Intelligence (AI) experience required.
Experience working with large models and understanding their failure modes.
Experience writing strong, production-grade code.
You are self-directed, pragmatic, and take full ownership of outcomes.
You communicate clearly and collaborate well in small, high-trust teams.
Tech Stack: GPU-based training and inference system, JAX, Python, and PyTorch.

Benefits

Comp & perks

medical insurance
Dental
Vision
Savings Plan Options
PTO

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills & Tools

Machine LearningModel Fine-TuningData Pipeline DesignEvaluation Pipeline ImplementationProduction-Grade Code Writing

Soft Skills

Clear CommunicationCollaboration in High-Trust TeamsSelf-DirectionPragmatismOwnership of Outcomes