Machine Learning Research Engineer

The Company

FL100, Inc. is a privately held, early-stage company pioneering the use of artificial intelligence-based agents to build an integrated, end-to-end platform for AI-driven product innovation. FL100 has created intelligent and reliable AI agent-powered workflows for emergent product innovation. Emergent product innovation leads to new products with breakthrough market adoption that generate new sources of value by solving for large unmet consumer needs and creating demand that did not exist previously.

We're leveraging multi-agent systems to accelerate growth, speed up product design innovation, and automate e-commerce operations. Our vision is to create a multi-platform service that empowers anyone to build and operate an e-commerce business efficiently. Currently, we offer a Shopify app that demonstrates our capabilities, with plans to expand across various e-commerce platforms and online marketplace.

FL100 is backed by Flagship Pioneering, an innovation enterprise that conceives, creates, resources, and builds companies that invent breakthrough technologies to transform health care, agriculture, and sustainability. Flagship has created over 100 groundbreaking companies since 2000, including Moderna.

The Role

We’re seeking a Machine Learning Research Engineer to build and maintain the data and research infrastructure that powers our multi-agent e-commerce automation platform. You will bridge the gap between cutting-edge AI research and production systems, ensuring that our data pipelines, large-scale training workflows, and model-serving strategies are robust, efficient, and reproducible. This role involves close collaboration with AI/ML researchers, software engineers, and product teams to drive innovation, streamline experimentation, and operationalize state-of-the-art models for real-world e-commerce applications.

Key Responsibilities

• Data Pipeline & Infrastructure

• Architect, implement, and optimize large-scale data pipelines to collect, preprocess, and curate datasets for AI research.
• Design and manage data lake or data warehouse solutions to streamline access, ensure quality, and maintain compliance with privacy/security standards.

• Research & Experimentation

• Develop research-oriented tooling and workflows that enable rapid experimentation, tracking, and reproducibility (e.g., MLflow, Weights & Biases).
• Collaborate with AI researchers to prototype and evaluate new models, algorithms, and multi-agent techniques, turning experimental code into maintainable libraries and frameworks.

• Training Frameworks & Distributed Systems

• Create and optimize distributed training setups using frameworks like PyTorch or TensorFlow, ensuring efficient utilization of GPU/TPU resources.
• Implement hyperparameter optimization pipelines, experiment tracking, and result analysis.

• Model Serving & Productionization

• Develop and maintain model-serving infrastructure, ensuring that research models can be deployed at scale (e.g., using Docker, Kubernetes, AWS Sagemaker, or equivalent).
• Collaborate with backend teams to integrate model outputs seamlessly into microservices, APIs, and real-time workflows.

• Performance Monitoring & Iteration

• Implement monitoring for data quality, model performance, and system reliability, identifying data drift and regression to guide iterative improvements.
• Conduct performance profiling and optimization at both code and infrastructure levels to handle growing traffic and data volumes efficiently.

• Cross-Functional Collaboration

• Work closely with product managers to align research objectives with business requirements and user-centric goals.
• Mentor junior engineers and data scientists on best practices for scalable AI research, version control, and continuous integration.

• Stay Current

• Keep abreast of emerging AI/ML research and trends, identifying opportunities to translate advanced concepts into tangible product features.
• Champion MLOps best practices to ensure reliability, reproducibility, and maintainability of our AI pipelines.

Qualifications

• 3+ years of hands-on experience as an ML Research Engineer, Data Scientist, or related roles.
• Proficiency in Python and ML frameworks (e.g., PyTorch, TensorFlow, scikit-learn).
• Demonstrated experience building large-scale data pipelines (e.g., Spark, Airflow, dbt) and distributed training workflows.
• Familiarity with GPU/TPU computing and performance optimization for deep learning.
• Experience with cloud platforms, preferably AWS (S3, EC2, ECR, Lambda, ECS/Fargate).
• Solid understanding of DevOps or MLOps principles (CI/CD, Docker, Kubernetes, Terraform) and tools for experiment tracking (MLflow, Weights & Biases, etc.).
• Strong collaboration skills, comfortable working alongside cross-functional teams in a fast-paced environment.
• Proven ability to handle research prototypes and transform them into scalable, production-ready solutions.

Preferred Qualifications

• Exposure to LLMs, multi-agent systems, or cutting-edge AI research.
• Advanced degree (MSc or PhD) in Computer Science, AI, or a related field.
• Knowledge of serverless architectures, streaming data (Kafka), or big data ecosystems (Hadoop, Hive).
• Familiarity with e-commerce or online marketplace integrations (Shopify, WooCommerce, Amazon, eBay).
• AWS certifications (e.g., Machine Learning Specialty, Solutions Architect).
• Experience with graph data or specialized compute (NLP, Computer Vision).
• Basic front-end or full-stack experience for rapid prototyping.

Values and Behaviors

• Constant Learner: Proactively explores emerging AI/ML research and incorporates the latest advances.
• Customer-Obsessed: Designs solutions that add measurable value to users and solve real-world problems.
• Mission-Oriented: Dedicated to driving breakthroughs in AI for e-commerce and product innovation.
• Entrepreneurial Spirit: Comfortable experimenting with new methods, embracing ambiguity, and iterating quickly.
• Works with Velocity & Urgency: Moves swiftly while maintaining attention to detail and quality.
• Collaborative: Communicates effectively with cross-functional teams, fostering a positive environment.
• Accountable: Takes ownership of projects and commits to delivering measurable outcomes.
• Growth Mindset: Welcomes feedback, iterates on ideas, and pushes for continual improvement.
• Innovative: Looks for opportunities to apply novel approaches and technologies.

More About Flagship Pioneering

Flagship Pioneering is a bioplatform innovation company that invents and builds platform companies, each with the potential for multiple products that transform human health or sustainability. Since its launch in 2000, Flagship has originated and fostered more than 100 scientific ventures, resulting in more than $90 billion in aggregate value. Many of the companies Flagship has founded have addressed humanity’s most urgent challenges: vaccinating billions of people against COVID-19, curing intractable diseases, improving human health, preempting illness, and feeding the world by improving the resiliency and sustainability of agriculture.  Flagship has been recognized twice on FORTUNE’s “Change the World” list, an annual ranking of companies that have made a positive social and environmental impact through activities that are part of their core business strategies, and has been twice named to Fast Company’s annual list of the World’s Most Innovative Companies. Learn more about Flagship at www.flagshippioneering.com.

Flagship Pioneering and our ecosystem companies are committed to equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity or Veteran status.

At Flagship, we recognize there is no perfect candidate. If you have some of the experience listed above but not all, please apply anyway. Experience comes in many forms, skills are transferable, and passion goes a long way. We are dedicated to building diverse and inclusive teams and look forward to learning more about your unique background.

Recruitment & Staffing Agencies: Flagship Pioneering and its affiliated Flagship Lab companies (collectively, “FSP”) do not accept unsolicited resumes from any source other than candidates. The submission of unsolicited resumes by recruitment or staffing agencies to FSP or its employees is strictly prohibited unless contacted directly by Flagship Pioneering’s internal Talent Acquisition team. Any resume submitted by an agency in the absence of a signed agreement will automatically become the property of FSP, and FSP will not owe any referral or other fees with respect thereto.