Machine Learning Engineer

Ikigai Labs • Cambridge, MA

2d

About The Position

The Ikigai platform unlocks the power of generative AI for tabular data. We enable business users to connect disparate data, leverage no-code AI/ML, and build enterprise-wide AI apps in just a few clicks. Ikigai is built on top of its three proprietary foundation blocks developed from years of MIT research - aiMatch, for data reconciliation, aiCast, for prediction, and aiPlan, for scenario planning and optimization. Our platform enables eXpert-in-The-Loop (XiTL) for model reinforcement learning and refinement, at scale.
With a combination of enterprise expertise and deep research in the field of AI, Ikigai Labs helps scale enterprises with AI by solving data engineering and modeling problems for business users and data scientists alike. Our unique ability to unlock value in tabular and time series data through AI-powered data harmonization, forecasting, dynamic learning and planning, is our Ikigai, our purpose in the world of AI.
As an AI/ML Engineer at Ikigai Labs, you will be part of a high-performing team responsible for optimizing and deploying ML solutions to maximize performance and scalability. We seek a dynamic and passionate engineer with strong software fundamentals and a keen interest in collaborative problem-solving.

Requirements

• Bachelor’s degree in Computer Science, Math, Engineering, or related field (Master's preferred) with 0-5+ years of experience (depending on the level)
• Strong understanding of data structures, data modeling, algorithms, and software architecture.
• Proficient in probability, statistics, and algorithm development.
• Hands-on experience with ML and deep learning libraries (Scikit Learn, Keras, TensorFlow, PyTorch, Theano, DyLib).
• Proficiency in Python, AWS services, and ETL/ELT pipelines.
• Understanding of key software design principles, design patterns, and testing best practices.
• Ability to learn quickly in a fast-paced, agile environment.
• Excellent organizational, time management, and communication skills.
• Willingness to engage in pair programming, share knowledge, and provide and receive constructive feedback.
• Strong problem-solving skills and the ability to take initiative.

Nice To Haves

• Experience with big data and distributed computing (Hadoop, MapReduce, Spark, Storm).
• Experience with Kubernetes and/or EKS is a plus.

Responsibilities

• ML Optimization and Deployment: Develop and deploy machine learning models for optimal performance and scalability.
• Productivity Tools Development: Build tools and services to enhance the ML platform, utilizing technologies like Kubernetes, Helm, and EKS.
• Model Architecture: Apply a strong understanding of deep learning architectures (CNNs, RNNs, etc.) to solve complex problems.
• Research Adaptation: Stay abreast of recent ML and deep learning literature and adapt findings to real-world applications.
• Collaborative Development: Work with cross-functional teams to integrate AI and ML solutions that drive business value.
• Data Handling: Manage large datasets and build ML pipelines for data processing and training.
• ETL/ELT Processes: Design and develop scalable data integration processes.
• Predictive Modeling Platform: Develop an on-demand predictive modeling platform using gRPC.
• Cloud and Containerization: Utilize Kubernetes for managing Docker containers and various cloud services (AWS, Azure) to solve cloud-native challenges.
• Stakeholder Management: Provide occasional support to our customer success team.

What This Job Offers

• Job Type: Full-time
• Career Level: Entry Level