Senior Machine Learning Research Engineer

Job description

Senior Machine Learning Research Engineer – Federal Government

• 12-month contract with extension potential
• Must be an Australian Citizen preferably with NV1 Security Clearance or eligibility to obtain one
• Location: Melbourne, Vic (onsite, minimum 4 days per week)

Job Details

Our Federal Government client is seeking an experienced Lead Machine Learning Research Engineer to support the Covert & Technical Operations (CTO) Transition Program. This is a highly specialised, hands-on technical leadership role focused on designing, developing and evaluating secure, multimodal AI solutions to support the triage of extremist and distressing digital material.
Working closely with researchers, investigators, technical specialists, wellbeing professionals and governance stakeholders, you will translate complex operational requirements into reliable, explainable and rigorously evaluated machine-learning capabilities. The role will involve developing computer vision, image and video classification, natural-language processing and similarity-detection solutions, alongside secure data pipelines, evaluation frameworks and analyst decision-support tools. You will also lead technical assurance, manage project delivery and contribute to privacy, security, responsible AI and human oversight practices within a sensitive operational environment.

Key Duties and Responsibilities

• Lead the design, development and evaluation of multimodal machine-learning solutions using computer vision, NLP, image/video classification and similarity detection.
• Develop capabilities for visual-marker detection, known-material hashing, near-duplicate detection, text classification and content categorisation.
• Build secure, reproducible data pipelines, model-training frameworks, evaluation processes and deployable software components.
• Select, adapt and evaluate appropriate machine-learning models, prioritising reliable, explainable and operationally useful outcomes.
• Establish evaluation datasets and assess model performance, including precision, recall, false positives, false negatives, calibration, uncertainty and class imbalance.
• Translate operational requirements into technical specifications, user stories, evaluation criteria and delivery milestones.
• Develop or integrate safe-review interfaces and analyst decision-support capabilities that minimise unnecessary exposure to distressing material.
• Support deployment within secure, on-premises, restricted-network or disconnected environments where required.
• Conduct adversarial testing, failure-mode analysis and model assurance, documenting limitations, risks and unsuitable use cases.
• Maintain technical plans, risk registers, evaluation frameworks, delivery schedules and project reporting.
• Collaborate with researchers, investigators, technical specialists, wellbeing professionals and partner organisations to deliver agreed outcomes.
• Communicate complex technical findings, model behaviour, limitations and risks to technical and non-technical stakeholders.
• Contribute to technical documentation, evaluation reports, operational guidance, deployment recommendations and relevant research outputs.
• Uphold privacy, security, ethical AI, human oversight and responsible handling of sensitive information.

Essential Requirements

• Relevant postgraduate qualification in computer science, artificial intelligence, data science, software engineering or a related discipline, or equivalent qualifications and substantial professional experience.
• Demonstrated experience designing and delivering applied machine-learning systems beyond proof-of-concept stage.
• Strong practical experience in at least two relevant areas, including computer vision, image/video classification, NLP, multimodal machine learning, similarity search, perceptual hashing or audio analysis.
• Advanced Python programming skills and experience with machine-learning frameworks such as PyTorch.
• Experience building reproducible data pipelines, model-evaluation frameworks and deployable software components.
• Strong understanding of classification performance, including precision, recall, false negatives, class imbalance, threshold selection, calibration and uncertainty.
• Experience handling sensitive, confidential, regulated or security-controlled information.
• Ability to translate complex operational requirements into practical technical solutions and independently manage substantial technical projects against agreed milestones.
• Excellent stakeholder engagement and communication skills, including the ability to explain model behaviour, limitations and risks to non-technical decision-makers.
• Sound understanding of privacy, security, responsible AI, human oversight and the risks associated with high-consequence machine-learning applications.

Desirable Skills

• PhD or doctoral qualification in a relevant discipline.
• Experience in law enforcement, national security, defence, digital forensics, online safety or other high-trust operational environments.
• Experience deploying machine-learning solutions on-premises, within restricted networks or in disconnected environments.
• Experience working with large image/video collections, digital evidence, content moderation or sensitive-material review.
• Knowledge of human-computer interaction, analyst decision-support systems and safety-centred interface design.
• Experience in adversarial testing, model assurance and independent technical validation.
• Relevant research publications, technical reports, patents or successfully deployed products.
• Strong professional judgement, discretion and sensitivity to the wellbeing of personnel working with distressing material.

To Apply
If this sounds like the right opportunity for you, please submit an updated copy of your resume in MS Word format by clicking APPLY NOW, or contact Humaira Hashmi at HHashmi@dfp.com.au for a confidential discussion.
#SCR-Humaira-Hashmi

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