Director of Machine Learning

At Kalibri, we are helping to redefine and rebuild the hotel industry. We are looking for passionate, energetic, and hardworking people with an entrepreneurial spirit, who dream big and challenge the status quo. We are working on cutting-edge solutions for the industry: we harness cloud-native data pipelines with advanced AI/ML models to drive asset performance. Kalibri is growing, so if you’re ready to make a difference and utilize your talents across a groundbreaking organization, please keep reading!

About Kalibri:

At Kalibri, we are helping to redefine and rebuild the hotel industry. We are looking for passionate, energetic, and hardworking people with an entrepreneurial spirit, who dream big and challenge the status quo. We are working on cutting-edge solutions for the industry: we are using our big data coupled with machine learning and AI to help increase asset values. Kalibri is growing, so if you’re ready to make a difference and utilize your talents across a groundbreaking organization, please keep reading!

About the Role

Our SaaS platform is powered by machine learning, from forecasting and anomaly detection to natural language interfaces and personalized recommendations. We’re looking for a proven ML engineering leader to define and scale our ML platform, build a high-performing team, and ensure the successful deployment of models into production. You’ll partner closely with Data Science, Data Engineering, and Product teams to turn research into robust, scalable, and monitored systems. This is a hands-on leadership role for someone who thrives at the intersection of strategy, architecture, and execution.

Responsibilities

• Define and own the long-term strategy for Kalibri’s ML platform, enabling rapid prototyping, efficient training, and reliable production deployment of models.

• Partner with Data Science to design and implement end-to-end ML workflows, including data ingestion, feature engineering, model training, evaluation, and serving.

• Lead the development and operation of scalable, cloud-native ML infrastructure using Prefect, Snowflake, DBT, and modern MLOps tooling.

• Implement best practices for CI/CD, model versioning, experiment tracking, automated testing, and monitoring of ML systems.

• Build, mentor, and grow a high-performing ML engineering team; foster a culture of collaboration, learning, and operational excellence.
  Ensure production models meet SLAs for accuracy, latency, and availability. Implement and deploy continuous evaluation for drift and degradation.

• Drive cross-functional initiatives with Product, Data Engineering, and Infrastructure to deliver high-impact ML-powered features to customers.

• Evaluate, select, and integrate modern AI/ML tools, frameworks, and services (e.g., PyTorch, TensorFlow, vector databases, LLM frameworks) to accelerate the roadmap.

• Champion security, compliance, and responsible AI practices in all ML development and deployment processes.
  

Requirements

• 10+ years in software engineering, data engineering, or ML engineering roles, including 3+ years in a technical leadership role at the Director+ level.

• Proven track record building and scaling ML platforms and taking models from research to production.

• Deep experience with MLOps best practices: model lifecycle management, orchestration (Prefect strongly preferred, Airflow or similar acceptable), automated retraining, and monitoring in production.

• Strong proficiency in Python and modern ML frameworks (e.g., PyTorch, TensorFlow) and in orchestrating workloads on Kubernetes.

• Expertise with cloud-native environments (AWS preferred), including services like S3, Lambda, Step Functions, and IAM.

• Familiarity with data warehouses and ELT pipelines (Snowflake, dbt) to support ML feature pipelines.

• Strong leadership skills with experience hiring, mentoring, and retaining technical talent.

• Excellent cross-functional collaboration skills with proven ability to align data scientists, engineers, and product managers around shared outcomes.

• Demonstrated ability to deliver complex technical projects in SaaS environments.

• Experience in a startup environment.

Preferred Qualifications

• Experience deploying and optimizing LLMs or other advanced generative AI models in production.

• Knowledge of vector databases, semantic search, and retrieval-augmented generation (RAG) pipelines.

• Familiarity with data streaming technologies (e.g., Kafka) for real-time feature serving.

• Experience with ML observability tools (e.g., LangChain, WhyLabs, Arize, Monte Carlo) and feature stores.

• Background in hospitality, travel, or other data-rich verticals.

• Experience with SOC 2, GDPR, or other compliance-driven environments.
  

The Benefits

• Fully remote work, with a thriving company culture

• Robust medical, dental, and vision plans through Blue Cross Blue Shield, including a $0 cost plan for employees and subsidized coverage for dependents

• 401k plan with employer match

• Flexible Paid Time Off

• $250 new hire allowance for home office setup