Machine Learning (Engineer | Lead)

What You'll Be Responsible For End-to-End ML Systems Ownership Lead the design, implementation, and operation of the complete machine learning lifecycle, including data pipelines, training workflows, evaluation frameworks, inference infrastructure, deployment processes, and production monitoring. Model Adaptation & Optimization Fine-tune and optimize large language models and foundation models using modern techniques such as LoRA, QLoRA, Supervised Fine-Tuning (SFT), Direct Preference Optimization (DPO), distillation, and other emerging approaches to improve quality, efficiency, and task performance. Scalable Inference Architecture Design and maintain robust inference systems capable of serving production workloads while balancing latency, throughput, reliability, and infrastructure cost. Data Systems & Training Infrastructure Develop and manage data pipelines that support the collection, generation, validation, and maintenance of high-quality datasets, leveraging both synthetic and real-world data sources to continuously improve model performance. Evaluation & Quality Assurance Establish comprehensive evaluation methodologies to measure model accuracy, robustness, safety, bias, reliability, and user-impact metrics. Work closely with stakeholders to ensure quality standards are clearly defined and consistently met. Production Readiness & Performance Engineering Own production deployment and operational excellence, including GPU utilization, memory optimization, model serving efficiency, performance tuning, observability, reliability engineering, and scaling strategies. Cross-Functional Collaboration Partner closely with product engineers, backend teams, platform engineers, and other stakeholders to integrate machine learning capabilities seamlessly into user-facing products and workflows. Technical Leadership Provide technical direction, make sound engineering decisions, and help establish best practices that enable the team to move quickly while maintaining high standards of quality and reliability. Continuous Improvement Drive iterative improvements by leveraging production feedback, operational metrics, user insights, and experimentation to enhance system performance and overall user experience. What Success Looks Like In this role, you will: Consistently transform machine learning research, prototypes, and experimentation into reliable production solutions. Build ML infrastructure that is scalable, maintainable, and easy to operate. Establish efficient training, evaluation, and deployment workflows that accelerate iteration without compromising quality. Proactively identify and resolve production issues before they impact users. Enable the broader team to work effectively through strong technical leadership, clear communication, and collaborative problem-solving. Deliver measurable improvements in model quality, system reliability, operational efficiency, and user outcomes. Technology Environment You will work extensively with: Python PyTorch and/or JAX GPU-accelerated training and inference platforms Distributed machine learning systems Model serving and deployment infrastructure Modern data processing and orchestration frameworks What We're Looking For Required Experience Proven track record of building, deploying, and maintaining machine learning systems in production environments. Strong understanding of large language models, foundation models, and their practical limitations, trade-offs, and failure modes. Experience developing scalable ML infrastructure, training pipelines, and inference systems. Strong software engineering fundamentals with a focus on reliability, maintainability, and performance. Demonstrated ability to take ownership of complex technical initiatives from concept through production deployment. Preferred Attributes Comfortable operating in fast-moving environments where priorities evolve quickly and execution matters. Pragmatic problem solver who balances technical excellence with business impact. Strong communicator capable of collaborating across technical and non-technical teams. Passion for building products that deliver meaningful value to users at scale. Natural leadership qualities with the ability to align teams around technical goals and outcomes. Our Working Style We believe exceptional products are built by small, highly capable teams with a strong sense of ownership and accountability. We value thoughtful decision-making, rapid learning, and a bias toward execution. Team me…