Senior Machine Learning Engineer

Summary

Join ITHAKA's AI/ML team as a Senior/Mid-Level Machine Learning Engineer and contribute to developing, implementing, and optimizing AI/ML solutions for real-world problems. You will collaborate with cross-functional teams, conduct data analysis, train and deploy models, and work on LLM integration. The role requires proficiency in machine learning algorithms, frameworks, and programming languages, along with experience in NLP and/or Computer Vision. Senior-level roles involve leading projects, mentoring junior engineers, and possessing advanced skills in data engineering and statistical methods. ITHAKA offers a competitive salary, comprehensive benefits, and a collaborative work environment. While ITHAKA has physical offices, employees are distributed across the continental US.

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