Machine Learning Engineer

We're partnered with a venture-backed technology company using AI to solve complex, real-world operational challenges at scale. The team is building production AI systems that combine retrieval, ranking, decision-making, and generative models to automate highly complex workflows while maintaining reliability, explainability, and measurable business outcomes.

This is an opportunity to join a high-calibre engineering team where you'll work across the full lifecycle of machine learning systems, from early-stage experimentation and model development through to deployment, monitoring, and continuous improvement in production.

What You'll Be Working On

• Building and scaling machine learning infrastructure that enables rapid experimentation, benchmarking, evaluation, and deployment.
• Designing and developing AI systems for retrieval, ranking, classification, recommendation, and generative AI applications.
• Owning the end-to-end lifecycle of machine learning systems, from problem definition and model selection through production deployment and iteration.
• Creating robust evaluation frameworks, datasets, and validation methodologies to measure real-world performance and business impact.
• Developing ranking and decision-making systems that intelligently route tasks between automated systems and human operators.
• Building feedback loops and data flywheels that continuously improve model performance over time.
• Working closely with engineering, product, and domain experts to translate complex business problems into scalable AI solutions.
• Prototyping new AI capabilities from inception and evolving them into reliable production systems.
• Contributing to machine learning best practices across model development, experimentation, deployment, and monitoring.

What We're Looking For

• 5\+ years of software engineering experience, including at least 2\+ years working directly with machine learning systems.
• Strong experience developing and deploying machine learning models into production environments.
• Proficiency with Python and modern machine learning frameworks such as PyTorch, TensorFlow, or JAX.
• Experience building large-scale AI systems involving retrieval, ranking, recommendation, search, classification, or generative AI.
• Strong understanding of machine learning evaluation, experimentation, benchmarking, and model performance optimization.
• Experience designing scalable distributed systems and production-grade software architectures.
• Ability to move comfortably between research, engineering, and product discussions.
• Strong communication skills and a bias toward ownership and execution.

Nice to Have

• Experience working with LLMs, RAG systems, AI agents, or generative AI applications.
• Experience building experimentation platforms, evaluation frameworks, or ML infrastructure.
• Background in ranking systems, recommendation systems, search, or decision intelligence.
• Experience operating machine learning systems at scale with real-world users and production traffic.
• Previous experience mentoring engineers or leading technical initiatives.