- Company Name
- Hightouch
- Job Title
- Machine Learning Engineer, AI Decisioning
- Job Description
-
Job Title: Machine Learning Engineer, AI Decisioning
Role Summary: Lead end‑to‑end development of AI‑driven decisioning engines that personalize marketing content, drive automated experiments, predict audience behavior, generate creative assets, and optimize budgets using large language models and data warehouse infrastructure.
Expectations:
- Design, build, and production‑grade deploy machine learning solutions that ingest cloud data warehouses (Snowflake, Databricks).
- Own the full lifecycle: problem definition, data engineering, feature engineering, model training, evaluation, monitoring, and scalability.
- Collaborate with data scientists, product managers, and customers to translate business problems into predictive and generative AI models.
- Demonstrate strong backend and distributed system architecture skills; design scalable data pipelines and model serving architectures.
Key Responsibilities:
- Create personalized recommendation models for content and product messaging across large state spaces.
- Develop predictive models for conversion, churn, and other audience success metrics.
- Build automated experimentation frameworks to evaluate messaging variants at scale.
- Integrate LLMs for content, image, and creative generation, ensuring relevance and compliance.
- Architect budget‑optimization algorithms that assess incremental conversions and CAC.
- Design data pipelines using Snowflake/Databricks, ensuring efficient feature extraction and model inference.
- Deploy models into production with robust monitoring, automated retraining pipelines, and clear performance SLAs.
- Conduct model interpretability analyses, produce actionable insights for marketing teams.
- Mentor junior engineers, influence best‑practice guidelines for ML infrastructure.
Required Skills:
- Proven experience designing and deploying ML systems in production (at least 3+ years).
- Strong proficiency in Python and libraries such as Pandas, Scikit‑Learn, PyTorch/TensorFlow.
- Deep knowledge of feature engineering, model training pipelines, and A/B testing.
- Experience with large language models (OpenAI, Llama, GPT‑family), inference optimization, and downstream application.
- Expertise in scalable data processing (SQL, Spark, Delta Lake) and cloud data warehouse platforms (Snowflake, Databricks).
- Solid understanding of distributed system design, microservices, and container orchestration (Docker, Kubernetes).
- Familiarity with MLOps tools (MLflow, Airflow, Prefect) and CI/CD for ML workflows.
- Strong analytical, problem‑solving, and communication skills; able to articulate complex technical concepts to non‑technical stakeholders.
Required Education & Certifications:
- Bachelor’s or Master’s degree in Computer Science, Electrical Engineering, Statistics, Data Science, or related technical field.
- Certifications in cloud data platforms (e.g., Snowflake, Databricks) or MLops are a plus.
San francisco bay, United states
Remote
19-11-2025