Lead the development of next-generation AI applications within a forward-thinking academic innovation environment. As Senior Machine Learning Engineer, you will architect and maintain scalable GenAI systems that power a digital learning platform supporting entrepreneurs. Working at the intersection of research and real-world application, you'll translate advanced machine learning concepts into robust, production-ready services.
Key Responsibilities
- Design and implement end-to-end machine learning pipelines for natural language processing and deep learning models in cloud environments
- Optimize model performance and cost efficiency using techniques such as LoRA and QLoRA on GPU-accelerated infrastructure
- Develop reusable frameworks and tooling to streamline deployment, monitoring, and validation of AI models
- Implement comprehensive observability with logging, tracing, and alerting systems to ensure reliability
- Establish governance protocols including approval workflows, staged rollouts, and compliance safeguards for AI deployments
- Create sandbox environments and onboarding resources to support safe experimentation and contribution
- Enforce rigorous testing and peer review processes for user-generated models before release
- Collaborate with data scientists and product teams to deliver AI-powered features across web and mobile platforms
- Mentor team members in production ML best practices and promote engineering excellence
- Diagnose and resolve issues in live systems, ensuring stable and secure operations
- Partner with technical product managers to track algorithmic KPIs and prioritize improvements
Qualifications
Candidates must have at least seven years of combined education and professional experience, with five years in software development using Python and SQL. Required expertise includes three years deploying NLP and deep learning models in production cloud environments and hands-on work with PyTorch, TensorFlow, or MXNet on GPU clusters.
You should have practical experience with retrieval augmented generation, model chaining, dynamic prompting, and fine-tuning methods such as PEFT and SFT using frameworks like Langchain. Familiarity with bias detection and mitigation using tools such as NeMo is essential. A solid grasp of Transformer architectures and self-attention mechanisms is expected.
Additional requirements include experience with vector databases for semantic search optimization, proficiency with big data technologies (Hadoop, Spark, Kafka), and working knowledge of relational and NoSQL databases in Linux and cloud environments (AWS, GCP, or Azure). Strong grounding in software engineering practices—unit testing, CI/CD, code reviews, and documentation—is required.
Preferred Background
A bachelor’s degree in computer science, engineering, mathematics, physics, statistics, or a related technical field is preferred.
Work Environment
This is a hybrid position based in Boston, MA, requiring a minimum of three days per week on-site. The role operates within a dynamic, mission-driven setting that blends academic rigor with entrepreneurial energy. You’ll work alongside innovators in a collaborative culture that values diverse perspectives and empowers individuals to shape the future of AI in education.
Compensation & Benefits
This position falls under salary grade level 059. Comprehensive benefits include health insurance from day one, generous paid time off with parental leave, retirement contributions, commuter benefits, and access to professional development resources including tuition assistance. Mental health and wellbeing support, along with caregiver resources, are also provided.
Commitment to Inclusion
We are dedicated to fostering a diverse, equitable, and inclusive workplace. Discrimination based on race, color, religion, national origin, sex, gender identity, sexual orientation, disability, veteran status, or any protected category is strictly prohibited. All individuals are welcome to contribute in an environment free from harassment and bias.
