AI/ML Engineer
Fortive
AirflowAzureCloudDockerKubernetesMicroservices.NETPythonPyTorchScikit-LearnTensorflow
Senior Machine Learning Engineer – Applied Modeling
1848 Ventures
full-time
Posted on:
Location Type: Remote
Location: Remote • 🇺🇸 United States
Visit company websiteJob Level
Senior
Tech Stack
AirflowAmazon RedshiftBigQueryCloudPythonScikit-LearnSQL
About the role
- Designing, training, and deploying models that turn hotel data into reliable production outcomes.
- Collaborate closely with ML Engineering Manager, CTO, and product partners to design modeling approaches.
- Build pipelines and support experiments that drive measurable revenue impact.
- Build and maintain training and evaluation pipelines ensuring offline metrics align with online business outcomes.
- Partner with engineering to integrate models into production services and batch jobs with defined SLAs.
- Contribute production-ready Python and SQL code with strong testing and review practices.
- Support data quality, observability, and reproducibility across the ML lifecycle.
- Collaborate on experiment design, including A/B testing and staged rollouts.
- Analyze experimental results, communicate lift and trade-offs, and document findings.
- Work closely with product, engineering, and leadership to scope problems and propose modeling approaches.
- Participate in code reviews, model reviews, and planning sessions.
- Share learnings and contribute to improving team practices around modeling, metrics, and documentation.
Requirements
- 4+ years of experience building and deploying ML models to production.
- Proficiency in Python (scikit-learn, XGBoost/LightGBM/CatBoost).
- Strong SQL skills and comfort with large, imperfect, real-world datasets.
- Experience across the full model lifecycle: data preparation, feature engineering, training, evaluation, deployment, and monitoring.
- Familiarity with experimentation design and metrics; able to reason about trade-offs and safeguards.
- Clear communicator, comfortable working in a collaborative, fast-paced environment.
- Nice to Have: Experience with causal inference (uplift modeling, causal forests, DML) or time-series modeling.
- Exposure to reinforcement learning concepts (multi-armed bandits, dynamic programming, temporal-difference learning).
- Experience with cloud data warehouses (Snowflake, BigQuery, Redshift) and dbt.
- Familiarity with orchestration frameworks (Prefect, Airflow).
- Familiarity with MLOps frameworks (Sagemaker, Vertex AI).
- Domain knowledge in pricing, demand modeling, or hospitality.
- Former start-up experience.
Benefits
- Travel may be needed for customer discovery, team building, and/or networking.
Applicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard skills
PythonSQLscikit-learnXGBoostLightGBMCatBoostdata preparationfeature engineeringmodel deploymentmodel monitoring
Soft skills
clear communicatorcollaborativefast-paced environmentproblem scopingexperiment designtrade-off reasoningdocumentationteam practices improvement
