Senior Staff Engineer, Machine Learning

📋 Description

• • Own the end-to-end technical vision for high-impact machine-learning initiatives that span NLP, computer vision, and generative AI. You will translate complex business problems into elegant, scalable architectures that power real-world products used by millions.
• • Architect and deliver production-grade AI/ML systems on modern cloud stacks (AWS, Azure, GCP) leveraging big-data technologies such as Spark, Kafka, and serverless pipelines. You will decide when to use managed services vs. custom components to balance cost, latency, and maintainability.
• • Design and implement multi-agent AI systems that make autonomous or semi-autonomous decisions in dynamic environments. You will define agent orchestration strategies, inter-agent communication protocols, and conflict-resolution mechanisms that ensure reliability and safety.
• • Lead prompt-engineering efforts for large language models, creating reusable prompt templates and retrieval-augmented generation (RAG) workflows that boost accuracy and reduce hallucinations. You will continuously benchmark against SOTA models and fine-tune prompts based on live feedback loops.
• • Establish MLOps best practices across the organization: containerized training with Docker, reproducible experiments via MLflow, CI/CD pipelines in Kubernetes, and automated model validation. You will mentor teams on monitoring drift, retraining triggers, and rollback strategies.
• • Drive rigorous code and design reviews, focusing on extensibility, security, and non-functional requirements (NFRs) such as latency < 100 ms P95, horizontal scalability to 10× traffic, and GDPR-compliant data handling. You will publish architectural decision records (ADRs) that become canonical references.
• • Conduct rapid proof-of-concepts (POCs) to de-risk technology choices—whether evaluating a new diffusion model for image generation or testing a graph-neural-network approach to fraud detection. You will distill findings into go/no-go recommendations backed by quantitative metrics.
• • Collaborate with product managers, UX researchers, and legal teams to embed responsible-AI principles—bias detection, explainability dashboards, and ethical-review gates—into every stage of the development lifecycle. You will champion fairness audits and privacy-preserving techniques such as federated learning.
• • Translate abstract client requirements into concrete epics and user stories, ensuring alignment between business KPIs (e.g., 5% uplift in conversion, 20% reduction in support tickets) and technical deliverables. You will facilitate discovery workshops and create high-level solution blueprints.
• • Provide technical mentorship to senior and staff-level engineers, running architecture guilds, brown-bag sessions, and pair-programming clinics. You will cultivate a culture of continuous learning, encouraging experimentation with emerging frameworks like JAX, LangChain, or LoRA fine-tuning.
• • Continuously scan the horizon for breakthrough research—whether foundation-model efficiency techniques (e.g., quantization, speculative decoding) or novel evaluation benchmarks—and assess their applicability to current product roadmaps. You will publish internal tech notes and present at meetups.
• • Own post-deployment optimization: A/B testing new model variants, tuning auto-scaling policies, and leading incident-response war rooms when models misbehave. You will turn outages into learning opportunities by instituting blameless post-mortems and actionable remediation plans.