Mid to Senior Machine Learning Engineer

About Programa

Programa is an expanding SaaS platform designed to assist architects and interior designers globally in managing products, projects, and workflows seamlessly within one platform. The company is currently developing advanced AI layers to help users make better decisions, access information faster, and automate daily tasks more efficiently.

Position Overview

We seek a Mid or Senior Machine Learning Engineer to architect, develop, and maintain production-grade ML systems covering areas such as search, recommendations, Generative AI, and autonomous workflows. The role bridges machine learning fundamentals with software engineering skills, requiring active involvement throughout the ML lifecycle—from problem identification and experimentation to deployment, monitoring, and continuous improvement. This is not a pure research or MLOps-exclusive role but demands contributions in modeling and infrastructure to serve end customers reliably.

Key Responsibilities

• Design and deliver end-to-end machine learning systems, advancing solutions from prototypes to production environments.
• Create and uphold Python-driven APIs and backend services to support ML workloads.
• Implement, monitor, and refine ML deployment and operational reliability within AWS cloud infrastructure.
• Enhance model inference pipelines by improving latency, scalability, and stability.
• Build frameworks for evaluation, including offline assessments and live experimentation.
• Work on systems involving search, ranking, recommendation algorithms, semantic search, and agentic workflows.
• Detect and manage issues like data drift, feature skew, model degradation, and ensure robust system performance.
• Lead improvements in continuous integration/continuous deployment (CI/CD) processes tailored for ML workflows.
• Partner with Data Scientists and Product teams to translate models into customer-facing features.
• Critically assess whether machine learning solutions appropriately address business problems.

Required Qualifications and Skills

• At least 4 years of professional experience in machine learning engineering, software development, data engineering, or related roles.
• Demonstrated commercial experience in designing and deploying production ML systems.
• Strong foundation in software engineering best practices including testing, system architecture, and code quality.
• Proficiency in Python and SQL, with experience building APIs and backend systems.
• Familiarity with cloud services and infrastructure, specifically AWS.
• Solid understanding of classical ML concepts such as overfitting, data leakage, training/validation/test splits, model evaluation metrics, and model monitoring.
• Experience with production system monitoring and troubleshooting performance or reliability issues.
• Excellent communication skills and ability to collaborate effectively in teams.
• Self-driven with capacity to handle ambiguous tasks and maintain ownership.
• A product-focused mindset, able to align technical choices with user and business outcomes.

Preferred but Not Mandatory Expertise

• Experience with ranking, recommendations, search, or retrieval systems.
• Knowledge of OpenSearch or Elasticsearch technology.
• Familiarity with vector databases and semantic search methodologies.
• Hands-on experience with retrieval-augmented generation (RAG) and embedding-centric systems.
• Exposure to large language model (LLM) APIs like OpenAI, Anthropic, or Bedrock.
• Understanding of agent frameworks or complex multi-step LLM implementations.
• Use of ML orchestration tools such as Kubeflow.
• Experience in large scale data processing tools like Spark, and modern data platform technology stacks like Snowflake, dbt, or Dagster.
• Knowledge in monitoring and observability of ML models.
• Background working in SaaS startups or scale-up companies in B2B environments.

Candidate Profile

• Enjoys owning the full ML system lifecycle beyond just modeling.
• Values verifying that developed solutions provide true customer benefit.
• Comfortable bridging ML, software engineering, and infrastructure challenges.
• Capable of swiftly transitioning between experimental and production contexts.
• Seeks pragmatic approaches rather than overly complex solutions.
• Thrives in an agile, lean environment with influence over technical decisions.
• Prefers working on products impacting real customers instead of purely research or internal tools.
• Not suited to candidates who prefer isolated research or who rely on others for deployment and operations.

Work Location and Conditions

The preferred base is Melbourne, Victoria, with expectations to attend the office near Richmond Station two days per week. Remote work arrangements are open for candidates residing anywhere in Australia or New Zealand who possess working rights within Australia.

Additional Information

Applicants must have legal working authorization in Australia. This position offers the chance to contribute meaningfully to AI capabilities actively embedded in an evolving SaaS platform, with opportunities to influence technical strategy and shape the future of the company's machine learning efforts.