Senior AI Engineer

Traackr•Boston, MA

8d•$120,000 - $170,000•Remote

About The Position

Traackr is a global SaaS technology company providing a data-driven influencer marketing platform that marketers use to optimize investments, streamline campaigns, and scale programs. Our customers range from some of the worldâs largest companies in the beauty and personal care space to digitally native indie brands, which have all made influencer management and engagement a critical practice of their marketing and advertising programs. We are a remote-first company, and for the folks that like to meet in person, we have offices in San Francisco, New York, Boston, Paris, and London. We operate on a culture of mutual respect, with core value pillars including: Trust. We earn the trust of our team, customers, creators, and partners through transparency, predictability, and integrity. Diversity. Bringing diverse perspectives to the table results in stronger outcomes. All are welcome. Value. Through our words and actions, we strive to create tangible value for our customers and peers. We only succeed when our community succeeds. Ownership. We lead with action. We take pride in solving the hardest challenges and feel accountable for our commitments. Mutual success. We share goals with each other and with our clients. Alignment, collaboration, and empathy are the cornerstones of our success. This position is 100% remote, with the understanding that occasional in-person attendance may be required for trainings, meetings, and team gatherings, as determined by your manager. We are looking for a Senior AI Engineer with 4â7 years of experience to design, build, and scale AI-powered systems and data infrastructure. This role combines advanced data engineering with production-grade machine learning, enabling the delivery of intelligent, data-driven products. As a senior member of the team, you will take ownership of key AI and data initiatives, collaborate cross-functionally with Product Managers, Data Scientists, and Engineers, and help drive best practices in both AI/ML engineering and data platform development.

Requirements

4â7 years of experience in AI Engineering, Machine Learning Engineering, or Data Engineering.
Strong programming skills in Python, Java and SQL.
Proven experience designing and building production-grade ML systems and data pipelines.
Experience with Databricks, Apache Spark, or similar distributed data processing frameworks.
Strong understanding of machine learning lifecycle (training, deployment, monitoring, retraining).
Experience with cloud platforms (AWS, Azure, or GCP).
Solid knowledge of data architecture, data modeling, and data warehousing concepts.
Experience with ML frameworks (e.g., TensorFlow, PyTorch, scikit-learn).
Familiarity with MLOps practices and tools (e.g., MLflow, Airflow, CI/CD pipelines).
Experience with version control (Git) and CICD development workflows.
Strong problem-solving, communication, and cross-functional collaboration skills.
Experience with LLMs, NLP, or generative AI applications.
Experience building end-to-end AI products or data-driven platforms.
Familiarity with real-time or streaming data pipelines.
Experience with cost optimization and performance tuning in Databricks.
Exposure to orchestration tools (Airflow, Dagster, etc.).
Experience mentoring or onboarding junior team members.

Responsibilities

Design, build, and deploy scalable machine learning and AI systems in production environments.
Collaborate with Product Managers, Data Scientists and Engineers to lead the implementation of models and integrate them into data pipelines and agentic applications.
Lead model performance monitoring, retraining workflows, and continuous improvement.
Lead the implementation of data preprocessing and feature engineering pipelines for ML use cases.
Lead to experimentation and testing of AI models to improve accuracy and performance.
Develop, maintain, and optimize scalable data pipelines for analytics and machine learning workloads.
Ensure data reliability, quality, and performance across data systems.
Implement and maintain data ingestion pipelines from various internal and external sources.
Build and improve internal tools that support data operations and data quality.
Monitor and troubleshoot data pipelines to ensure consistent and timely delivery.
Contribute to improving platform efficiency and scalability.
Participate in code reviews and follow best practices in data and ML engineering.
Document data pipelines, ML workflows, and system architecture.
Contribute to evolving data and AI engineering best practices.
Stay current with emerging tools, frameworks, and trends in AI, ML, and data engineering.