About Ema
Ema is building the world’s leading Agentic AI platform to transform enterprise productivity. We enable organizations to delegate repetitive tasks to Ema, the Universal AI Employee, delivering 10x gains in workforce efficiency, across functions. Founded by former executives from Google, Coinbase, Flipkart, and Okta, our team includes engineers from premier tech companies and graduates of Stanford, MIT, UC Berkeley, CMU, and IITs.
We are backed by industry leading investors including Accel, Naspers/Prosus, Section32, and angels like Sheryl Sandberg and Dustin Moskovitz. Headquartered in Silicon Valley and with offices in London, Bangalore and Vancouver and Bangalore, Ema is at the frontier of what Agentic AI can do in production — we ship real systems that run real business processes at scale.
Role Overview & Key Responsibilities
This 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.
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.
Required Skills & Qualifications
- Bachelor’s or Master’s (or PhD) degree in Computer Science, Machine Learning, Statistics, or a related field.
- A strong track record (usually 10-12+ years) of applied experience with ML techniques, especially in large-scale settings.
- Experience building production ML systems that operate at scale (latency / throughput / cost constraints).
- Experience in Knowledge retrieval and Search space.
- Exposure in building Agentic Systems and Frameworks.
- 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, maintenance.
- Experience designing systems for monitoring, diagnostics, logging, model versioning, etc.
- Deep knowledge of computational trade-offs: distributed training, inference, optimizations (e.g. quantization, pruning, batching).
- Excellent communication skills; ability to present complex systems / trade-offs to technical and non-technical stakeholders.
- Experience mentoring senior engineers; ability to lead technical discussions and influence across orgs.