Senior or Mid-level Machine Learning Engineer

Summary

Join ITHAKA's AI/ML team as a Senior Machine Learning Engineer and contribute to developing, implementing, and optimizing AI/ML solutions for real-world problems. You will collaborate with cross-functional teams, work with LLMs, build data pipelines, and ensure model performance and scalability. This role requires a strong background in applied machine learning, data science, and proficiency in various machine learning frameworks and libraries. Senior-level candidates will lead complex projects, mentor junior engineers, and drive innovation. ITHAKA offers a competitive salary, comprehensive benefits, and a collaborative work environment. The position is open to candidates residing in the continental U.S. and does not offer sponsorship.

Requirements

• Minimum 2 years of professional experience applying machine learning and data science to solve real-world problems
• Bachelor’s degree or higher in Computer Science, Data Science, or a related technical field
• Proficiency in machine learning algorithms, techniques, and practical applications
• Proficiency with modern machine learning frameworks and libraries (e.g., TensorFlow, PyTorch, scikit-learn)
• Practical experience with Natural Language Processing (NLP) and/or Computer Vision
• Practical experience working with Large Language Models (LLMs), Generative AI techniques, fine-tuning LLMs, and embedding models
• Proficient in Python and SQL for data manipulation
• Proficient in system design, software engineering, including coding, algorithms, data structures, debugging, version control, and writing clean, maintainable code
• An additional 2 or more years of professional experience, for a total of 4 or more years, in applying machine learning and data science to solve real-world problems
• Greater depth of knowledge in machine learning best practices, including advanced feature engineering and training/evaluation pipelines
• Expert in statistical methods, including probability, distributions, and hypothesis testing, compared to a foundational understanding for mid-level roles
• Advanced data engineering skills for managing large-scale datasets and distributed systems
• Ability to lead and deliver complex projects independently with minimal supervision

Responsibilities

• Collaborate with cross-functional teams to develop new product experiences from ideation to implementation
• Conduct data analysis to validate data quality, uncover insights, and guide the development of machine learning models
• Train, evaluate, and fine-tune machine learning models to meet performance, accuracy, and business requirements
• Deploy machine learning models to production environments, ensuring scalability and robustness
• Monitor and maintain deployed models to ensure reliable performance, addressing issues, and implementing improvements as needed
• Work on LLM integration, including prompt tuning, chaining, and developing agents for suitable use cases, while also designing, implementing, and optimizing workflows to support these capabilities effectively
• Build and optimize data pipelines for machine learning workflows
• Develop and implement robust evaluation frameworks, including creating ground truth datasets, to assess model performance and reliability
• Collaborate on defining metrics and KPIs to measure the cost, success and impact of machine learning projects
• Utilize cloud-based technologies to deliver scalable machine learning solutions
• Participate in code reviews and contribute to best practices for the team's machine learning development process
• Stay current with AI advancements and integrate relevant innovations
• Manage multiple priorities and deadlines effectively in an agile environment, ensuring timely delivery of high-quality results
• Take ownership and lead end-to-end challenging AI/ML projects with a high degree of autonomy, driving innovation and ensuring strategic alignment with organizational goals
• Mentor junior engineers, fostering their technical growth and ensuring the overall strength of the team

Preferred Qualifications

Knowledge of search/information retrieval techniques, Retrieval-Augmented Generation (RAG), recommendation systems, and A/B testing methodologies (preferred)

Benefits

• Medical, dental, and vision plans
• An employer-paid 10% retirement contribution
• Paid parental and caregiver leave
• 22 days of paid time off
• 11 paid holidays
• Up to 12 sick days
• Wellness benefits