Trust & Safety ML Engineer safeguarding Hapiko’s generative - AI sticker printer for children. Owning moderation pipelines, safety evaluations, classifiers, red teaming, and compliance systems.
About the role
- Applied ML Engineer working on AI-driven insights at Kaseya. Collaborating with product teams to enhance features with machine learning and data analysis.
Responsibilities
- Explore and analyze data using Python, pandas, and PySpark (or similar tools).
- Use matrix factorization, clustering, dimensionality reduction, and related techniques to understand and prepare data for modeling, and to identify and label latent factors (e.g., user behavior patterns, content/topic clusters, expertise dimensions).
- Create, tune, and productionize ML models for:
- • Categorization / classification
- • Recommendations and similarity
- • Other prediction or ranking tasks that power product features
- Design and implement AI-driven ingest flows that turn unstructured inputs (tickets, emails, forms, messages, logs, etc.) into well-structured data that models and downstream systems can use.
- Build workflows where AI can:
- • Auto-fill or suggest key fields and metadata.
- • Proactively ask users/customers for missing or ambiguous information (e.g., via email or messaging).
- • Surface similar past items or solutions to assist humans in decision-making.
- • Fully handle simple, repetitive “Level 1” style requests end-to-end when safe to do so.
- Work closely with engineers to integrate models and workflows into production systems with proper monitoring, fallbacks, and guardrails.
- Work with multiple product teams to help them identify and scope AI opportunities in their areas.
- Define patterns, templates, and best practices for data ingestion, feature creation, model usage, and evaluation that teams can reuse.
- Serve as a trusted advisor and technical lead:
- • Provide design and architecture guidance on data and ML-heavy features.
- • Join projects to handle the most complex modeling or workflow automation pieces when teams get stuck.
- Mentor and guide junior data/ML engineers and analysts:
- • Conduct code and model reviews.
- • Pair with them on tricky problems.
- • Help them develop good intuitions about metrics, evaluation, and operational reliability.
- Help establish and socialize standards for experimentation, documentation, and responsible AI usage across teams.
Requirements
- 5+ years in data science, ML engineering, or a similar applied role, with a strong record of shipping production data/ML features.
- Strong Python skills and experience with pandas for data analysis.
- Experience with PySpark or other distributed data processing frameworks.
- Solid understanding of ML fundamentals, including:
- • Supervised learning and classification models
- • Matrix factorization / embeddings / latent factor models
- • Feature engineering and model evaluation (offline metrics and online experiments)
- Proficiency with PyTorch (or a similar deep learning framework) and related ML tooling.
- Strong SQL and experience with modern data warehouses / data lakes.
- Comfort working with APIs, microservices, and production integration of ML models, including performance and reliability considerations.
- Experience serving as a technical lead or senior individual contributor across multiple teams or projects.
- Proven ability to translate business problems into data/ML projects, and to clearly explain tradeoffs to non-ML stakeholders.
- Track record of mentoring junior engineers/analysts and improving team practices (e.g., review culture, testing, monitoring).
- Strong communication skills and the ability to drive alignment across product, engineering, and operations.
Job title
Job type
Experience level
Salary
Degree requirement
Tech skills
Location requirements
Report this job
Found something wrong with the page? Please let us know by submitting a report below.
ML Engineer training diffusion models for Hapiko’s voice - activated Stickerbox, which turns kids’ spoken ideas into stickers. Bringing Spin Master characters into production safely and on - model.
MLOps Engineer building production ML infrastructure, pipelines, and monitoring for exacare ai’s post - acute care AI platform. Scaling reliable systems that help healthcare teams make safer placement decisions.
Senior Data Engineer building cloud - native data and ML platforms for Hive.co’s event - marketing automation products. Owning pipelines, model infrastructure, and audience - data systems at scale.
Senior Machine Learning Engineer building causal and decision systems for CSC Generation’s AI - native omnichannel retail brands. Deploying machine learning that improves pricing and other commercial decisions.
Director leading agentic AI and machine learning products for Instacart’s grocery technology platform. Building retailer solutions and guiding teams from strategy through production deployment.
New - grad software engineer building Quora’s distributed ML platform, model serving, and developer tooling. Supporting Quora’s global knowledge - sharing product with scalable GPU infrastructure.
Applied Machine Learning Scientist developing Generative AI and predictive ML solutions for TD banking. Evaluating models, managing AI lifecycles, and supporting responsible implementation.
Applied Machine Learning Scientist developing Generative AI and predictive ML solutions at TD, a major North American bank. Supporting model evaluation, deployment, monitoring, and responsible AI governance.
Senior Machine Learning Engineer building conversational AI agents and production ML systems. Helping Numa automate automotive dealership service and sales through evaluation - first tooling and infrastructure.