Responsibilities
- Guide technical strategy for generative AI and agent-based machine learning systems that support enterprise AI agents, covering reasoning, retrieval, tool integration, and interoperability with SaaS platforms.
- Design and build scalable production pipelines for training, fine-tuning, retrieval-augmented generation, agent coordination, and evaluation, prioritizing reliability, speed, and ongoing learning.
- Own and evolve the long-term machine learning roadmap for generative AI infrastructure, including agent frameworks, retrieval systems, evaluation mechanisms, and integration with multi-modal pipelines.
- Incorporate emerging machine learning techniques—such as deep learning, large language models, and recommendation systems—into product and platform capabilities.
- Investigate, prototype, and deploy novel advancements in machine learning and large models, including reasoning, memory structures, multi-modal inputs, extended context handling, and autonomous behaviors.
- Balance precision, response time, cost, transparency, and real-world performance across all stages of agent development, from prompt engineering to execution.
- Promote high engineering standards by advancing monitoring, reproducibility, model tracking, testing practices, and bias mitigation in machine learning and agent systems.
- Coach and grow senior technical staff, cultivating a culture grounded in scientific method, experimentation, and holistic system design.
- Work closely with product, infrastructure, and research teams to ensure machine learning innovations meet enterprise requirements, including secure integrations and privacy-conscious deployment.
- Shape data strategy by guiding the development of retrieval indexes, embedding systems, data corpora, and feedback mechanisms to enhance factual accuracy and reasoning quality.
- Ensure system performance and scalability so machine learning agents and retrieval pipelines can handle vast knowledge stores, varied APIs, and dynamic enterprise environments.
Compensation
Competitive salary and equity package
Work Arrangement
Flexible, hybrid work model
Team
Collaborative team of machine learning engineers, researchers, and infrastructure specialists focused on building next-generation AI agents
Responsibilities
- Lead the technical direction of GenAI and agentic ML systems that power enterprise-grade AI agents — spanning reasoning, retrieval, tool use, and integrations across various SaaS products.
- Architect, design, and implement scalable production pipelines for model training, fine-tuning, retrieval (RAG), agent orchestration, and evaluation — ensuring robustness, latency efficiency, and continuous learning.
- Define and own the multi-year ML roadmap for GenAI infrastructure — including agent frameworks, RAG systems, world-class evaluation loops, and integration with MCP, browser, and vision pipelines.
- Identify and integrate cutting-edge ML methods / research (deep learning, large models, recommender systems, LLMs, etc.) into Ema’s products or infrastructure.
- Research, prototype, and integrate cutting-edge ML and LLM advancements (reasoning, memory architectures, multi-modal perception, long-context models, autonomous agents) into the platform.
- Optimize trade-offs between accuracy, latency, cost, interpretability, and real-world reliability across the agent lifecycle — from prompt design to orchestration and execution.
- Champion engineering excellence — drive observability, reproducibility, versioning, testing, and bias-aware development across ML and agentic systems.
- Mentor and elevate senior engineers and researchers, fostering a culture of scientific rigor, experimentation, and system-level thinking.
- Collaborate cross-functionally with product, infra, and research teams to align ML innovation with enterprise needs — enabling secure integrations, privacy-aware deployments, and scalable use cases.
- Influence data strategy — guide how retrieval indices, embeddings, structured/unstructured corpora, and feedback loops evolve to improve grounding, factuality, and reasoning depth.
- Drive system scalability and performance — ensuring ML agents and RAG pipelines can operate across billions of knowledge objects, diverse APIs, and real-time enterprise contexts.
Available for qualified candidates
