Senior ML Scientist

Senior ML Scientist (Affective Computing & Biometric ML)

Reports to:

Chief Scientific Officer (Dr. Kaushik Ram)

Location:

Australia (Preferred Sydney/Melbourne; Remote welcome)

Employment:

Full-time, Senior

Compensation:

AUD $117,000 – $140,000 + ESOP

About inTruth Technologies

inTruth Technologies is pioneering a new era of emotional health by making emotion measurable as real-time biometric data—objectively, at scale. Using data from wearables, our proprietary Emotion Language Model (ELM) decodes the body's physiological signals into accurate, clinical-grade emotional insights.

We are building a new category — Emotion Biotech — that combines the precision of a clinical device, the scalability of AI, and the ethics of user-owned infrastructure. With sovereignty-by-design at our core, we give individuals and organizations full control of their emotional data, creating technology that serves, not exploits.

We're not just launching a product; we're building the infrastructure for a future where emotional intelligence is as fundamental as literacy, empowering humanity with empathy, resilience, and connection at scale.

Role Summary

Lead the design, validation, and productisation of scientifically-defensible emotion metrics from multimodal wearables and sensors—bridging neuroscience, clinical research, and production ML—to deliver validated, regulatory-ready outputs for research, clinical, and B2B deployment.

Core Responsibilities

A. Hypothesis Design & Experimental Leadership

• Study design, stimuli selection and ground-truth strategy for Phase-1 (Emotion detection) and Phase-2 (Emotion Score/predictive modelling) claims.

• Prepare pre-registered analysis plans (primary/secondary endpoints), power calculations, effect size, stratified subgroup analyses for university-led research and clinical trials.

• Produce statistical analysis plans (SAPs) that include mixed-effects models, equivalence/non-inferiority testing, calibration metrics, confidence intervals, and control for multiple comparisons.

B. Signal Processing & Multimodal Data Engineering

• Build and maintain canonical pipelines: PPG (time/frequency/nonlinear) + EDA decomposition, respiration proxies, eye tracking and video-derived facial features.

• Design artefact detection/repair (motion masks, contact quality, optical interference), resampling & time-synchronisation across devices, and per-window QC flags.

• Design and maintain the feature store and provenance metadata (unit tests & sample vectors).

C. Metric & Model Development (theory → math → code)

• Convert neuroscience constructs (valence/arousal, emotion detection, Emotion Score) into explicit mathematical definitions, weighting schemes, and testable formulas (with unit tests).

• Develop and evaluate models from classical (Random Forest, SVM, XGBoost) to sequence/probabilistic (temporal CNN/RNN/Transformer, Bayesian, mixture models) with explicit calibration, uncertainty quantification (CI, Brier/ECE), and blending diagnostics for mixed emotions.

• Implement LOSO/LOTO and nested CV protocols; report per-subgroup metrics (accuracy, sensitivity, specificity, F1, calibration).

D. Validation, Benchmark & Cross-Device Generalisability

• Execute validation matrix aligning datasets (lab EEG+ECG+PPG, Biostrap, Garmin) to quantify ± tolerance thresholds and implement domain adaptation/cross-device calibration.

• Produce equivalence tests for cultural and demographic cohorts and robustness tests across physiological, lifestyle and medical confounds.

E. Emotion Score and Longitudinal/Predictive Modelling

• Implement and validate Emotion Score (spike detection, α/β weighting, sampling windows, scaling/clipping).

• Build longitudinal models for mood trajectories and early-warning signals (clinical risk flags), with mixed models and survival-type analyses as required.

F. Productisation & MLOps

• Package validated metrics into production artefacts (SDKs/APIs, model registry, Docker images), define CI/CD for model training and deployment, and define telemetry/monitoring for model & data drift.

• Define endpoint SLAs for cloud and feasible on-device inference specs (quantisation/latency/energy budgets).

G. Reporting, Reproducibility & Publication

• Document all protocols, code, validation steps in Confluence/Repo; deliver whitepapers, conference abstracts and peer-reviewed manuscripts aligned to open science where permissible.

• Partner with the Board/CRO/CSO on regulatory strategy (TGA/FDA/SaMD classification) and HREC/HRA submissions.

H. Ethics & Governance

• Data minimisation, de-identification, and secure data handling consistent with HIPAA/GDPR principles and Australian HREC requirements.

• Work with in-house legal/compliance to ensure data flows meet MRFF and trial requirements (eConsent, data retention, re-consent, access control, audit logs).

Must-Have Qualifications & Experience

• PhD (preferred) or MSc + significant relevant experience in Affective Computing, Computational Neuroscience, Biomedical Engineering, Statistics, or Applied ML with demonstrable track record.

• Hands-on expertise with PPG sensors along with time-series signal processing (IBI, EDA, or ECG feature extraction and artefact mitigation).

• Proven experience designing and executing controlled experiments with physiological ground truth (PPG/EEG/clinical endpoints) and strong statistical skills (mixed models, power analysis, equivalence testing).

• Demonstrable experience in time-series ML and multi-modal fusion (sequence models/temporal CNNs/multimodal transformers).

• Production ML skills: Python (NumPy/Pandas/SciPy, scikit-learn), PyTorch/TensorFlow, XGBoost, containerisation (Docker), model versioning (MLflow/DVC) and CI/CD basics.

• Experience writing SAPs, HREC/HRA/IRB submissions, and working with clinical partners (university hospitals or lab partners).

• Exceptional written documentation skills (Confluence) and the ability to present technical results to clinical/regulatory audiences.

Desirable

• Prior experience with Biostrap, Garmin devices, Biopac, Shimmer or Lab Stream Layer integrations.
• Experience with EEG experiments and probabilistic/Bayesian modelling.
• On-device quantisation/edge deployment expertise (TF Lite/TF JS, pruning, 8/4-bit quantisation).
• Prior SaMD regulatory pathway involvement (TGA/FDA) or ISO 13485 familiarity.
• Demonstrated peer-reviewed publications in affective computing, psychophysiology, or related domains.

Success Metrics (KPIs)

The Senior ML Scientist will be evaluated against:

1. Scientific Rigor

– Delivery of at least two pre-registered analysis plans and SAPs per trial with peer-reviewed statistical validity.
2. Pipeline Robustness

– Development of multimodal pipelines (PPG + EDA + additional sensors) with ≥95% artefact detection accuracy within first 6 months.
3. Model Performance

– Emotion detection models achieving ≥70% balanced accuracy on benchmark datasets, with subgroup calibration metrics reported.
4. Emotion Score Delivery

– Prototype Emotion Score (0–100 scale) delivered within 6 months; validated across ≥2 datasets within 12 months.
5. Cross-Device Validation

– Demonstrated generalisability between at least two wearable platforms (e.g., Garmin & Biostrap) with tolerance thresholds defined.
6. Productisation

– Packaging of validated models into SDK/API artefacts with CI/CD pipeline established within first year.
7. Documentation & Publication

– Confluence/Repos fully up-to-date, plus at least one whitepaper or conference submission within first 12 months.
8. Compliance & Ethics

– Zero lapses in GDPR/HIPAA/HREC compliance across all experiments and data pipelines.

Probation & Review Clause

This role is subject to a structured performance review after the initial 90-day period. The review will assess delivery of defined milestones, scientific reproducibility, collaboration with cross-functional teams, and alignment with leadership expectations. Continuation in this role is contingent upon satisfactory performance during this period.

How to Apply

Please send your
resume and a cover letter

outlining your experience and motivation for this role to

. Applications will be reviewed on a rolling basis.