Software Engineer II - Recommendations

Responsibilities

• Contribute to the architecture and evolution of reliable, performant backend services and clear APIs for product recommendations.
• Build and maintain large-scale data processing pipelines that transform events and catalog data into features and model inputs.
• Partner with ML engineers and product stakeholders to productionize recommendation models for batch and real-time inference.
• Contribute to the vector database supporting recommendations, semantic search, and agentic use cases.
• Implement observability through metrics, logging, tracing, and dashboards.
• Break projects into milestones while balancing experimentation, technical soundness, and maintainability.
• Lead data-driven decision-making and A/B testing, interpreting results to guide product and engineering iterations.
• Participate in on-call and incident response and drive post-incident improvements to system resilience and operability.
• Use AI to accelerate development, automate testing, and improve monitoring and debugging workflows.
• Share knowledge, mentor junior engineers, and define best practices for data frameworks, distributed systems, and production ML integration.

Requirements

• At least 2 years of professional software engineering experience focused on backend and distributed systems at scale.
• Proficiency in Python and willingness to work in other languages.
• Experience with cloud-native architectures, preferably AWS, and container orchestration such as Kubernetes.
• Experience with data-driven decision-making and A/B testing, including instrumenting experiments and interpreting results.
• Ability to design and query relational, analytical, and NoSQL data models, including systems such as Postgres, MySQL, data warehouses, Redis, and vector databases.
• Familiarity with DevOps practices including CI/CD, monitoring, and alerting for large-scale data and recommendation systems.
• Track record of owning features end to end, from technical design and implementation through rollout, monitoring, and iteration.
• Strong technical collaboration and communication skills across ML engineering, software engineering, product, and other teams.
• Experience experimenting with AI in professional or personal projects and interest in responsible AI tools and workflows.
• Preferred experience with recommendation systems or adjacent ML-powered features such as ranking, personalization, or search.
• Preferred experience with Apache Spark or comparable big-data frameworks such as Flink or Beam.
• Preferred experience integrating ML models into production systems or contributing to ML infrastructure.
• Preferred experience training and iterating on ranking, prediction, or personalization models.
• Preferred experience with ML and distributed-compute frameworks such as Ray.
• Preferred experience partnering with data science or ML teams on feature stores, offline/online parity, model deployment, and monitoring.
• Background in e-commerce, marketing technology, or consumer personalization is a plus.

Benefits

• Onsite in Boston, Massachusetts, five days per week.
• Base salary range of $116,000-$174,000 USD per year.
• Role may require up to 10% travel for onboarding, client or partner work, team meetings, and industry events.
• Comprehensive health, welfare, and wellbeing benefits are available based on eligibility.
• Potential additional compensation may include an annual cash bonus, equity, and sign-on payments.