Staff Machine Learning Engineer

Staff Machine Learning Engineer Needed / $220k-$260k / Santa Clara, CA-Hybrid This Jobot Job is hosted by: Michael Oktay Are you a fit?

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Salary: $220,000 - $260,000 per year A bit about us: Based in Santa Clara, CA, we are an AI company that provides a complete AI platform for talent management to help companies find, recruit, and retain workers.

Why join us?

Hybrid (2 Days a Week) Work Environment Competitive Compensation Comprehensive Medical, Dental, Vision Insurance Discretionary Bonus up to 20% Pre-IPO Equity Job Details Responsibilities: Own, train, build and deploy cutting edge deep learning models across all products, end to end.

Build on top of Open Source LLM (Large Language Models) to leverage a diverse dataset.

Apply innovative solutions from Generative AI Create industry best practices for Machine Learning for Recruiting and HR industry around the globe Do it responsibly to provide equal opportunity for everyone by extending our internal model fairness platform Create innovative algorithms for Machine Learning & AI Implement best practices for building AI-enabled products Develop AI-based systems for Natural Language Processing (NLP) Optimize Machine Learning models for time efficiency, performance, cost, scalability, and accuracy Develop tools and processes for automatically train, updating and evaluate LLM (Large Language Models) Qualifications: Strong foundation in Machine Learning (ML), Deep Learning, LLMs and NLP Hands-on experience in applying Natural Language Processing solutions to challenging real-world problems.

Ability to work cross-functionally & interface with data science experts across all of our customer base Familiar with LLM (Large Language Models), transformers like BERT, GPTs, T-5, HuggingFace etc.

Exceptionally strong knowledge of CS fundamental concepts and ML languages ( like Python, C, C++, Java, JavaScript, R, and Scala, etc.

) Ability to innovate, as proven by a track record of software artifacts or academic publications in applied machine learning.

Prior experience building and deploying machine learning models in production at scale Understanding of data and ML systems with the ability to think across stack layers - REST APIs, microservices, data ingestion and processing systems, and distributed systems.

Extensive experience with scientific libraries in Python (numba, pandas) and machine learning tools and frameworks (scikit-learn, tensorflow, torch, etc.). Experience implementing production machine learning systems, working with large-scale datasets, and a solid understanding of machine learning theory.

Familiar with a cloud-based environment such as AWS, Azure or GCP Nice-to-Have: Metrics-focused and passionate about delivering high-quality models.

Experience with analyzing large data sets, using Hadoop, Spark Familiar with Spark, MLLib, Databricks MLFlow, Apache Airflow and similar related technologies.

Familiarity with MLOps tools and pipelines (MLflow, Metaflow). PhD or Masters in Computer Science or Data Science is preferred.

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