ML Ops / Infrastructure Fellow

About FairwAI:
Stealth mode mission driven. FairwAI is building the AI bias detection and compliance platform that industries will rely on to ensure fairness in hospitality, and healthcare. We operate at the intersection of AI, policy, and accountability — backed by a team from MIT, and Harvard. Our work blends rigorous research, thoughtful product development, and ethical innovation at global scale.

Shaurjya Mandal – Head of Research, AI Systems & Clinical Innovation
Role: Shaurjya leads FairwAI's research function and the development of its AI methodology — including structured retrieval architectures, sentiment and bias detection systems, and validation frameworks for fairness in clinical and high-stakes AI environments. He ensures FairwAI's research meets the standards of peer-reviewed clinical AI while remaining accessible and useful in real-world deployment.

Bio: Shaurjya Mandal is a machine learning research scientist at Mass General Brigham and Harvard Medical School, where he develops and evaluates AI systems for clinical settings. His research sits at the intersection of responsible AI, healthcare equity, and applied machine learning — with published and applied work in algorithmic bias, fairness in clinical decision support, and AI safety for vulnerable populations. He holds an M.S. in Artificial Intelligence from Carnegie Mellon University, where he conducted research at the Robotics Institute on machine perception and learning systems. Prior to his work at Harvard, Shaurjya served as a research engineer at India's Defence Research and Development Organisation (DRDO) and contributed to AI policy and infrastructure projects across the MENA region, including the design of responsible AI deployment frameworks for healthcare and insurance systems in the United Arab Emirates. He is a Nucleate Pittsburgh fellow, a recognized convener in early-stage biotechnology and AI commercialization. At FairwAI, Shaurjya bridges frontier clinical AI research with practical, ethical deployment — ensuring that what we build for the most underserved populations is also held to the highest scientific standard

Team Mr. Dixon –Chief Compliance Strategist Role: Visionary founder of FairwAI, Mr. Dixon leads overall strategy, mission alignment, institutional partnerships, and compliance innovation. He ensures the platform remains focused on real-world fairness, risk mitigation, and public accountability—rather than abstract DEI. Bio: Mr. Dixon is a serial founder, public interest technologist, and global strategist known for building impact ventures at the intersection of technology, governance, and equity. He is currently a strategic advisor at MIT, where he collaborates across departments such as the MIT Media Lab and Martin Trust Center. Mr. Dixon has served as a policy strategist to President John Mahama of Ghana, worked with the U.S. Department of Energy on distributed energy infrastructure, and led multi-million-dollar public-private partnerships with organizations like GE Healthcare, USAID, and UNICEF. Mr. Dixon is the founder of SolarFi, a social enterprise that builds portable solar-powered infrastructure for clinics, schools, and underserved communities across Africa providing energy to over 600,000 people. Under his leadership, SolarFi was awarded major U.S. government contracts and featured by MIT and the UN. He has also advised on diplomatic and philanthropic partnerships.

FairwAI is Mr. Dixon’s most urgent venture yet. Inspired by lived experiences of racial bias and systemic discrimination in global travel, hiring, and healthcare systems, he founded FairwAI to reframe fairness as a compliance and safety issue, not a corporate DEI trend. Antonio brings a rare combination of field-tested grit, institutional access, tech, diplomacy, and global coalition to ensure accountability becomes enforceable.

Dr. John Cooley – Role: Leads FairwAI’s technical architecture, AI compliance engine, and research partnerships. Bio: Dr. John Cooley is a renowned technologist and serial entrepreneur who holds five degrees from MIT: B.S. in Electrical Engineering, B.S. in Physics, M.S. in Electrical Engineering, an Engineer’s Degree, and a Ph.D. in Electrical Engineering. He earned the David Adler Memorial Thesis Prize and the Morris Joseph Levin Award, recognizing his groundbreaking work in engineering and systems design. John was the CTO and later CEO of Nanoramic Laboratories, where he led the company to raise $44M+ in capital and developed cutting-edge battery and nanotechnology solutions. With 15+ years of experience at the frontier of hardware-software integration and AI systems, John brings a rare blend of research rigor and venture-scale execution. He now advises FairwAI on tech architecture, compliance logic, and academic research integration across MIT and international institutions.

Dr. Ryan Chin – Strategic Advisor, Urban Tech & Mobility Systems Role: Ryan advises FairwAI on systems architecture, urban integration, and commercial strategy. He brings expertise in AI-driven mobility, sensor networks, and resilient infrastructure to inform how FairwAI scales across physical environments like hotels, hospitals, and cities.

Bio: Dr. Ryan Chin is a globally recognized expert in smart cities, urban mobility, and sustainable design systems. He co-founded Optimus Ride, a leading autonomous vehicle company spun out of MIT, where he served as CEO and led the development of self-driving tech for campuses, cities, and business districts. Prior to that, Ryan was Managing Director of the City Science Initiative at the MIT Media Lab, where he pioneered AI-integrated mobility systems and urban computing platforms. Ryan holds a Ph.D. and two Master's degrees from MIT in Media Arts and Sciences and Architecture. A serial entrepreneur and systems thinker, Ryan has advised Fortune 100 companies, U.S. government agencies, and international city governments on emerging technologies, sustainability, and mobility innovation. He is a frequent keynote speaker at Davos, Smart City Expo, and the UN. At FairwAI, Ryan supports us with building out our advisory team and the integration of computer vision, IoT, and AI fairness protocols into real-world infrastructure, ensuring our platform is adaptable, sensor-compatible, and aligned with the future of ethical urban technology.

This is a 12-week, full-time fellowship for someone who can build the technical backbone of a real applied AI platform.
Your job is to make sure everything we build is:
• reproducible
• deployable
• observable
• versioned
• defensible
This is not a “DevOps internship.”
This is MLOps in a real-world accountability setting.

You are accountable for making sure the system runs cleanly outside a laptop.
If the demo breaks, if pipelines cannot be reproduced, or if model versions cannot be traced—this role owns the outcome.

What You’ll Build
1️⃣ Reproducible ML Workflows
• Containerize inference pipelines (Docker required)
• Standardize environment setup across the team
• Ensure pipelines run consistently across machines
2️⃣ Model Versioning & Traceability
• Implement model version tracking and metadata logging
• Ensure every inference output is tied to:
• model version
• dataset version (when applicable)
• feature extraction version
• timestamp and run ID
3️⃣ Deployment Infrastructure
• Deploy backend and ML services to cloud infrastructure (AWS preferred)
• Set up staging deployment environment for weekly demos
• Manage environment configuration and secrets
4️⃣ Pipeline Automation
• Build CI/CD workflows (GitHub Actions or similar)
• Automate test runs and deployment checks
• Ensure pipelines run daily or on schedule
5️⃣ Observability & Reliability
• Add structured logging
• Implement monitoring hooks (basic metrics, failure alerts)
• Create retry/failover scaffolding where appropriate
• Document failure modes and recovery procedures
6️Data Safety & Governance Hygiene
• Implement secure handling of datasets and outputs
• Ensure storage practices align with trust and privacy expectations
• Prevent unsafe or undocumented third-party data leakage

Operating Rhythm
This fellowship is fast and execution-heavy.
You should expect:
• Weekly sprint deliverables
• Weekly live demo environment support
• Frequent debugging and integration work
• Continuous collaboration with ML and backend fellows
This is not a passive fellowship. It is an infrastructure build mission.

What Success Looks Like (Week 12 Outcome)
By the end of the fellowship, you will have delivered:
• A stable deployed environment for platform + ML services
• Containerized ML inference and backend services
• A reproducible pipeline that can be run end-to-end
• A model versioning + traceability layer
• CI/CD automation for core pipelines
• Documentation that allows new engineers to onboard quickly
• Demo-ready system reliability
Definition of Done:
A pilot-ready prototype that can be deployed, restarted, and reproduced without tribal knowledge.

You’ll Be a Strong Fit If You Have
We’re looking for someone who can actually ship infrastructure.
You should have experience with:
• Docker (strong)
• AWS (EC2, S3, IAM basics)
• CI/CD (GitHub Actions preferred)
• Python-based ML pipelines
• Structured logging and debugging
• Comfort working with both backend and ML engineers

Bonus Points
• Kubernetes experience
• MLflow or model registry experience
• Airflow, Step Functions, or orchestration tools
• Experience with message queues (RabbitMQ/Kafka)
• Experience designing reproducibility and experiment tracking systems
• Security-first mindset (secrets, permissions, access control)

Performance Expectations
This role is responsible for making the system stable.
You will be expected to:
• anticipate failures
• enforce consistent environments
• prevent dependency chaos
• create clean handoffs between ML and backend teams
• ensure demos don’t collapse under pressure

You may work with:
• Docker
• AWS (EC2, S3, Lambda optional)
• GitHub Actions
• Python
• FastAPI
• PostgreSQL
• MLflow (optional)
• Kubernetes (optional)

Time Zone / Availability
Remote role. Must overlap at least 4 hours with US Eastern Time for collaboration and weekly demos.

Real work
• Competitive
• High technical ownership
• Real-world MLOps portfolio artifact
• Potential continuation into longer-term role depending on pilot traction

How to Apply
Please include:
• Resume
• GitHub (required)
Required Application Question
In 5–10 sentences, describe the most serious deployed ML or backend system you’ve supported. Include:
• deployment environment (cloud/local)
• how you handled versioning
• how you handled failures
• one debugging incident you solved

This role is a minimum of 30 hours a week in the summer or 15 hours a week during the school year. This role is unpaid, however you can do CPT/OPT or extenal school funding from school and foundations for our non profit.