ML Engineer designing and building AI applications for customers in production environments. Collaborating with data scientists and engineers to operationalize ML models with a hybrid work environment.
About the role
- Machine Learning Engineer developing AI products and collaborating with teams at Sitemate. Responsible for model architecture, training, deployment, and integration with user-facing features.
Responsibilities
- Help build AI products by architecting, building, training and deploying models.
- Collaborate with product and design teams to experiment and ship fast.
- Participate in code reviews and technical discussions, sharing knowledge and shaping engineering standards.
- Own full AI generation pipelines: data prep/labeling, model selection and fine-tuning.
- Build and maintain data pipelines to ensure clean, reliable, and scalable training datasets.
- Evaluate and integrate third-party AI services and APIs to accelerate development.
- Deploy, monitor, and continuously improve model performance, accuracy and costs.
- Conduct experiments to evaluate new features, algorithms, architectures, or approaches.
- Document workflows, share insights, and contribute to best practices in applied machine learning.
Requirements
- 2+ years shipping production features.
- Strong Python for ML and working knowledge of TypeScript.
- Experience with LLMs (fine-tuning, prompt engineering, evaluation).
- Understanding of ML fundamentals (training/validation/test splits, overfitting, metrics like precision/recall).
- Hands-on experience with structured data extraction from unstructured sources.
- AWS cloud experience.
- Nice to Have: Built agentic systems (e.g., LangGraph, Bedrock Agents/AgentCore, OpenAI Agents SDK) and/or MCP servers.
- Experience with model quantization and optimization techniques.
- MLOps: experiment tracking, model/version management, A/B or shadow testing, rollback strategies.
- Experience building real-time voice features: speech recognition, conversational flow.
- Experience with vector databases and RAG implementation.
- Knowledge of prompt chaining and few-shot learning techniques.
- Data privacy & security for AI workloads (PII handling, auditability, least-privilege IAM).
Benefits
- Competitive, performance-based remuneration
- Equity options - own a piece of what you’re helping to build
- 20 days of paid annual leave, plus sick, carer’s, and compassionate leave
- Paid parental leave - 16 weeks for primary carers, 6 weeks for secondary carers (including adoption and stillbirth support)
- Professional growth budget and a transparent career development framework
- Laptop and home-office setup budget
- Flexible work - remote or hybrid, plus the ability to work from anywhere for a few weeks each year
- Community & connection - weekly team lunches, global offsites, and our signature “Life Story” sessions
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