Principal Machine Learning Engineer

Overview

About Ema

Ema is building the next generation of AI technology to empower every employee in the enterprise to be their most creative and productive. Our proprietary platform enables companies to delegate repetitive tasks to Ema, the Universal AI Employee—a powerful, secure, and intelligent teammate that integrates across workflows and systems.

We’re founded by executives from Google, Coinbase, and Okta, and backed by the world’s top investors and angels. Headquartered in Silicon Valley and Bangalore, Ema operates as a hybrid team. For this role, we expect team members to work from the office three days a week.

Role Overview & Key Responsibilitie

sThis is a high-leverage leadership role that spans architecture, execution, and org-building, and will shape the direction of our AI / ML initiatives at Ema. We are seeking an AI / ML technical leader who can take a vision and build it. As a Principal ML Engineer at Ema, you will be a senior technical leader responsible for shaping the machine learning roadmap, architecting large-scale ML systems, driving innovation, and ensuring our mixture of expert models (LLM + SLM + Custom Model) is accurate and performant at scale. You will collaborate across teams (research, product, infra, data, etc.), mentor senior engineers, and influence strategy and execution at company-wide levels

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Responsibiliti

• esLead 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 product
• s.Architect, design, and implement scalable production pipelines for model training, fine-tuning, retrieval (RAG), agent orchestration, and evaluation — ensuring robustness, latency efficiency, and continuous learnin
• g.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 pipeline
• s.Identify and integrate cutting-edge ML methods / research (deep learning, large models, recommender systems, LLMs, etc.) into Ema’s products or infrastructur
• e.Research, prototype, and integrate cutting-edge ML and LLM advancements (reasoning, memory architectures, multi-modal perception, long-context models, autonomous agents) into the platfor
• m.Optimize trade-offs between accuracy, latency, cost, interpretability, and real-world reliability across the agent lifecycle — from prompt design to orchestration and executio
• n.Champion engineering excellence — drive observability, reproducibility, versioning, testing, and bias-aware development across ML and agentic system
• s.Mentor and elevate senior engineers and researchers, fostering a culture of scientific rigor, experimentation, and system-level thinkin
• g.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 case
• s.Influence data strategy — guide how retrieval indices, embeddings, structured/unstructured corpora, and feedback loops evolve to improve grounding, factuality, and reasoning dept
• h.Drive system scalability and performance — ensuring ML agents and RAG pipelines can operate across billions of knowledge objects, diverse APIs, and real-time enterprise context

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Required Skills & Qualificati

• onsBachelor’s or Master’s (or PhD) degree in Computer Science, Machine Learning, Statistics, or a related fie
• ld.A strong track record (usually 10-12+ years) of applied experience with ML techniques, especially in large-scale settin
• gs.Experience building production ML systems that operate at scale (latency / throughput / cost constraint
• s).Experience in Knowledge retrieval and Search spa
• ce.Exposure in building Agentic Systems and Framewor
• ks.Proficiency in relevant programming languages (e.g. Python, C++, Java) and ML frameworks (TensorFlow, PyTorch, etc
• .).Strong understanding of the full ML lifecycle: data pipelines, feature engineering, model training, serving, monitoring, maintenan
• ce.Experience designing systems for monitoring, diagnostics, logging, model versioning, e
• tc.Deep knowledge of computational trade-offs: distributed training, inference, optimizations (e.g. quantization, pruning, batchin
• g).Excellent communication skills; ability to present complex systems / trade-offs to technical and non-technical stakeholde
• rs.Experience mentoring senior engineers; ability to lead technical discussions and influence across or

gs.
Why Join

• Ema?Work at the forefront of agentic AI systems—driving real enterprise transformat
• ion.Own mission-critical platforms that make or break Ema’s capabilit
• ies.Join an elite team of engineers, product thinkers, and AI researchers who value deep execut
• ion.Help define the architectural, cultural, and operational DNA of a company set to define a new categ

ory.
Our culture & V

• aluesHigh Bar on Quality – We value excellence and expect world-class execu
• tion.Ownership & Accountability – We take full responsibility for our work and outc
• omes.Impact-Driven Approach – We set ambitious goals and measure success through real-world im
• pact.Team Collaboration & Growth – We believe in continuous learning, knowledge sharing, and supporting each o
• ther.No Hierarchy, Just Execution – Everyone is an individual contributor, focused on delivering res

ults.