Senior Machine Learning Engineer

Salary: $142,000 - 194,000 per year Requirements:

• We are looking for 8 years of experience in data science and/or machine learning, software development, computer science, or a related discipline.
• Alternatively, we seek a bachelors degree in Computer Science, Machine Learning, Computer Engineering, Mathematics, Physics, or a related field plus 4 years of experience in data science and/or machine learning, software development, computer science, or a related discipline.
• Alternatively, we seek a masters degree in Computer Science, Machine Learning, Computer Engineering, Mathematics, Physics, or a related field plus 2 years of experience in data science and/or machine learning, software development, computer science, or a related discipline.
• We value demonstrated ability in cloud computing, including deploying, managing, and securing cloud environments and applications.
• We value strong code review skills for software quality assurance.
• We value proficiency in writing maintainable, effective code in high-level programming languages.
• We value expertise in predictive analytics to identify trends and inform strategic decisions.
• We value experience using machine learning frameworks to develop and optimize models for real-world business use cases.
• We value proficiency in major programming languages to deliver effective software solutions.
• We value knowledge of quality assurance and quality control practices.
• We value troubleshooting skills across a range of technical domains.
• We value understanding of data security, privacy regulations, and protection principles.

• We implement machine learning models for production with minimal guidance.
• We help transform machine learning prototypes into production-ready models.
• We collaborate with stakeholders such as development leads, product management, operations, and release management to align technical decisions and support delivery.
• We scale models, clean model code, and ensure production quality standards are met.
• We automate machine learning workflows from ETL through deployment and monitoring to support continuous integration and continuous delivery.
• We use infrastructure and monitoring frameworks to assess model performance and alignment with design criteria.
• We monitor deployed models and troubleshoot independently or with data science partners.
• We interpret new metrics and explain model performance to non-technical stakeholders.
• We identify issues related to data quality, bias, fairness, security, and privacy, and work to reduce their impact.
• We support data cleaning, preprocessing, and feature identification to prepare for model training.
• We collaborate with data scientists and software developers to integrate machine learning models into new or existing systems.
• We support the handoff between model development and operations to enable smooth deployment and continuous improvement.
• We participate in troubleshooting and debugging efforts for machine learning infrastructure and workflows.
• We contribute to the development and maintenance of internal tools, platforms, environments, and services.
• We write efficient, low-complexity, bug-free code from scratch and maintain the codebase.
• We follow best practices for version control, code review, and continuous integration in machine learning projects.
• We maintain professional documentation for technical processes such as experimentation, data collection and analysis, and model building.
• We stay current with developments in the machine learning field and apply new knowledge to model development.
• We build familiarity with third-party machine learning frameworks, packages, and libraries such as PyTorch, TensorFlow, and Keras.
• We independently manage work, track timelines and deliverables, and keep initiatives on schedule.
• We prioritize tasks and adjust plans when resources or timelines change.
• We collaborate across teams to align expectations and achieve shared goals.
• We build a strong understanding of business, stakeholder, and customer needs to support effective partnerships.
• We identify and resolve standard and non-standard issues, escalating more complex matters when needed.
• We analyze information from multiple sources to troubleshoot errors.
• We contribute to knowledge sharing and best practices.
• We pursue continuous learning, seek feedback and training, and support a culture of knowledge sharing.
• We propose improvements to increase the efficiency and effectiveness of team processes and workflows.

• Cloud
• ETL
• Support
• Keras
• Machine Learning
• Model Training
• Oracle
• PyTorch
• Security
• TensorFlow

More:

hackajob is collaborating with Oracle to connect with exceptional professionals for this role. This position focuses on productionizing machine learning models, supporting deployment and monitoring, improving ML workflows, and contributing to internal tools and infrastructure. We work across teams and stakeholders to integrate ML solutions into existing systems, maintain documentation, and drive continuous improvement while staying current with developments in machine learning.

last updated 40 week of 2026