Ema Unlimited
Software Engineer, Machine Learning
San Francisco Bay Area
Sponsorship not specified$135k-$200kDetected 498 days ago
PythonData StructuresAlgorithmsSQLGCPCloud PlatformsMachine LearningTensorFlowData EngineeringNLPAgentic AIMLOpsA/B TestingTest AutomationProblem Solving
About the role
- We enable organizations to delegate repetitive tasks to Ema, the Universal AI Employee, delivering 10x gains in workforce efficiency, across functions.
- 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.
Responsibilities
- 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.
Requirements
- Deep understanding and practical experience with NLP techniques and frameworks, including training and inference of large language models.
- 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.
- Familiarity with cloud platforms like GCP or Azure.
- The ability to work collaboratively in an extremely fast-paced, startup environment.
- 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.
Compensation
- The standard base salary for this position is $135,000 to $200,000 annually.
Benefits
- 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.
- Clearly communicate the technical workings and benefits of ML models to both technical and non-technical stakeholders, facilitating understanding and adoption.
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