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Software Engineering Lead, Machine Learning

Ema -
San Francisco Bay Area, CA

Apply Now

Job Details

Full-time
$135,000 - $300,000 a year

Qualifications

AI models
TensorFlow
Statistics
Commercial use (data warehousing systems)
Software engineering
Reinforcement learning
Data Integration (Data management)
Big data projects
Azure
Computer Science
Engineering development testing
Automation
PyTorch
Ensemble learning
Software deployment
Data structures
Software implementation
Public Cloud
AI platforms (beyond public GPTs)
Scalable systems
Computational framework
Master's degree
SQL
Doctoral degree in Computer Science
Technical solutions implementation
Production systems
Machine intelligence
Doctor of Philosophy
Model deployment
Industry trends

Full Job Description

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.

Who you are

We're looking for an innovative and passionate Machine Learning Engineers to join our team. You are someone who loves solving complex problems, enjoys the challenges of working with huge data sets, and has a knack for turning theoretical concepts into practical, scalable solutions. You are a strong team player but also thrive in autonomous environments where your ideas can make a significant impact. You love utilizing machine learning techniques to push the boundaries of what is possible within the realm of Natural Language Processing, Information Retrieval and related Machine Learning technologies. Most importantly, you are excited to be part of a mission-oriented high-growth startup that can create a lasting impact.

You will:

Conceptualize, develop, and deploy machine learning models that underpin our NLP, retrieval, ranking, reasoning, dialog and code-generation systems.
Implement advanced machine learning algorithms, such as Transformer-based models, reinforcement learning, ensemble learning, and agent-based systems to continually improve the performance of our AI systems.
Lead the processing and analysis of large, complex datasets (structured, semi-structured, and unstructured), and use your findings to inform the development of our models.
Work across the complete lifecycle of ML model development, including problem definition, data exploration, feature engineering, model training, validation, and deployment.
Implement A/B testing and other statistical methods to validate the effectiveness of models. Ensure the integrity and robustness of ML solutions by developing automated testing and validation processes.
Clearly communicate the technical workings and benefits of ML models to both technical and non-technical stakeholders, facilitating understanding and adoption.

Ideally, you'd have:

A Master’s degree or Ph.D. in Computer Science, Machine Learning, or a related quantitative field.
Proven industry experience in building and deploying production-level machine learning models.
Deep understanding and practical experience with NLP techniques and frameworks, including training and inference of large language models.
Deep understanding of any of retrieval, ranking, reinforcement learning, and agent-based systems and experience in how to build them for large systems.
Proficiency in Python and experience with ML libraries such as TensorFlow or PyTorch.
Excellent skills in data processing (SQL, ETL, data warehousing) and experience working with large-scale data systems.
Experience with machine learning model lifecycle management tools, and an understanding of MLOps principles and best practices.
Familiarity with cloud platforms like GCP or Azure.
Familiarity with the latest industry and academic trends in machine learning and AI, and the ability to apply this knowledge to practical projects.
Good understanding of software development principles, data structures, and algorithms.
Excellent problem-solving skills, attention to detail, and a strong capacity for logical thinking.
The ability to work collaboratively in an extremely fast-paced, startup environment.

For California based candidates:
The standard base salary for this position is $135,000-$300,000 annually.

Compensation offered will be determined by factors such as location, level, job-related knowledge, skills, and experience. Certain roles may be eligible for variable compensation, equity, and benefits.

Ema Unlimited is an equal opportunity employer and is committed to providing equal employment opportunities to all employees and applicants for employment without regard to race, color, religion, sex, national origin, age, disability, sexual orientation, gender identity, or genetics.

Apply Now

Refine Your Search

lead machine learning engineer jobs in san francisco ca

Software Engineering Lead, Machine Learning

Lead Machine Learning Engineer

Lead Data Engineer

Performance Modeling Lead

Tech Lead/Manager, Machine Learning Research Scientist- LLM Evals

Principal Engineer Tech Lead Manager, ML Acceleration

Lead Data Engineer Enterprise Reporting & Analytics

Lead Frontend Engineer (UX & Product)

Lead Product Analyst

Research Engineer / Scientist (3D Tech Lead)

Lead, AI Engineering

Lead AI and Data Science Engineer - Manager

Lead Engineer, Machine Learning

Frontier Data Lead - Code

Lead Machine Learning Engineer

Corporate IT Services Operations Lead

Lead Embedded Software Engineer

Machine Learning Scientist - Open Source Lead

Senior Engineering Leader

Senior Staff Tech Lead, YouTube Shorts Quality

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