Staff Machine Learning Engineer
Role details
Job location
Tech stack
Job description
Hybrid role with regular onsite work days in our San Jose, CA office strongly preferred. Other U.S locations may be considered.
You will make an impact by being responsible for:
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Developing and optimizing machine learning models leveraging NLP, Computer Vision, and GenAI
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Architecting and implementing scalable ML pipelines for training, validation, deployment, and monitoring of production models
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Driving the development of large-scale ML infrastructure, ensuring low-latency inference and efficient resource utilization across cloud and hybrid environments
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Implementing MLOps best practices, automating model training, validation, deployment, and performance monitoring
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Working closely with data engineers, software engineers, and product teams to ensure seamless integration of ML solutions into production systems
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Optimizing ML models for performance, scalability, and efficiency, leveraging techniques like quantization, pruning, and distributed training
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Enhancing model reliability by implementing automated monitoring, CI/CD pipelines, and versioning strategies
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Leading efforts in data acquisition and preprocessing, including annotation and refinement of datasets to improve model accuracy
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Staying updated with state-of-the-art ML research, identifying opportunities to integrate new techniques and technologies into production systems
Requirements
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7+ years of hands-on experience designing, building, and deploying machine learning models, with expertise in NLP, Computer Vision, and/or Generative AI solutions
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Proven experience taking ML models from development to production, ensuring scalability, reliability, high availability, and ongoing performance monitoring
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Strong proficiency in Python (required) and working knowledge of R and SQL, with experience leveraging big data technologies (e.g., Spark, Hadoop) for large-scale data processing and analytics
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Deep experience with modern ML frameworks such as TensorFlow and PyTorch, including model training, evaluation, optimization (e.g., quantization, pruning), and inference performance tuning
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Experience building and managing end-to-end ML pipelines, including data ingestion, feature engineering, model training, validation, deployment, and lifecycle management
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Hands-on experience implementing MLOps best practices, including CI/CD for ML, automated model versioning, monitoring for drift/performance, and workflow automation
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Experience with cloud-based ML platforms (e.g., AWS SageMaker, Azure ML, Google AI Platform) for training, deploying, and scaling models in cloud environments
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Practical experience with containerization and orchestration tools (e.g., Docker, Kubernetes) and model serving platforms (e.g., Triton, ONNX) for production-grade deployments
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Experience fine-tuning large language models (LLMs) and applying Generative AI techniques preferred
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Familiarity with distributed training across multi-GPU or cloud environments preferred
You excel in these key competencies:
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Excellent problem-solving skills, with the ability to break down complex challenges in document extraction and transform them into scalable ML solutions
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Strong communication skills, with the ability to articulate ML problems clearly and work autonomously
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Ability to work cross-functionally with engineering, product, and data teams, influence technical direction without formal authority, and drive alignment across stakeholders in a fast-paced environment
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Capacity to connect technical ML solutions to broader business objectives, prioritize high-impact initiatives, and make pragmatic trade-offs that balance innovation with production reliability
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Demonstrates curiosity and agility in staying ahead of rapidly evolving AI/ML advancements, quickly evaluating new technologies, and applying them responsibly to real-world enterprise challenges
Benefits & conditions
The base salary range for this position is $155,000 - $175,000 a year. The base salary ultimately offered is determined through a review of education, industry experience, training, knowledge, skills, abilities of the applicant in alignment with market data and other factors. This position is also eligible for a discretionary bonus, equity and a full range of medical and other benefits.
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Job Segment OR Key Words: SaaS, Machine Learning, ML, Engineering, NLP, Generative AI, APA, Agentic Process Automation