(Senior) Machine Learning Engineer

A high-impact and technically focused (Senior) Machine Learning Engineer role has become available in a fast-growing AI product company, offering the opportunity to own core ML subsystems.

Key responsibilities:

• Own end-to-end ML subsystems: data preparation, training, fine-tuning, evaluation, inference, and continuous iteration in production.
• Build and improve ML components across the stack: data pipelines, training loops, evaluation frameworks, and inference services.
• Fine-tune and adapt modern models (LLMs and other architectures) to meet strict production constraints on latency, cost, reliability, and safety.
• Design and implement rigorous evaluation and testing to understand model behavior, regressions, and failure modes in real-world usage.
• Develop and maintain robust, reproducible data pipelines for both real-world and synthetic data, supporting training and evaluation at scale.
• Debug complex production ML issues using real user signals; drive measurable improvements in accuracy, latency, efficiency, and reliability.
• Turn research ideas and new model capabilities into working systems that operate reliably in production.
• Provide technical guidance and mentorship to other ML engineers, raising the overall standard of ML engineering and production practices.

Profile:

• Master’s or Bachelor’s degree in computer science, machine learning, statistics, or a related quantitative field (or equivalent practical experience).
• 4+ years of hands-on experience building, training, and deploying ML models in production, with a strong focus on modern neural architectures and LLMs.
• Tech stack: Strong expertise in Python, PyTorch/JAX, GPU-based training and inference, distributed training, model serving infrastructure, data/ML pipelines, and evaluation/benchmarking frameworks; familiarity with cloud infrastructure, Docker/Kubernetes, and workflow orchestration for ML.
• Deep understanding of how ML models behave—and misbehave—in real-world settings, including failure modes, distribution shift, and safety considerations.
• Strong software engineering skills: ability to write clean, production-quality code and think in systems, not just scripts.
• Experience with fine-tuning techniques (e.g., SFT, LoRA, QLoRA, DPO) and adapting models to specific tasks and constraints.
• Proven ability to design and run evaluation/benchmarking systems that inform model selection, iteration, and release decisions.
• Comfortable working under real production constraints: latency, cost, reliability, safety, and scalability.
• Structured, analytical, and able to work through ambiguity with a bias toward shipping, measuring, and iterating.
• Strong ownership and independence: able to take complex problems from idea to production impact with minimal hand-holding.
• Excellent communication skills and a collaborative mindset; able to work closely with research, product, and engineering teams.
• Full professional proficiency in English.

This is a rare opportunity to define how state-of-the-art ML models are adapted, evaluated, and operated in a real-world AI assistant, with direct impact on product quality and user trust. Apply if you believe this is your next challenge.