Computer Vision Engineer/ Machine Learning Engineer

About Company:

Kinexcs is an AI-driven digital health platform and wearables company with a mission to enable and empower people for mobility and a better quality of life. It is focused on reducing the burden of musculoskeletal conditions, which affects about 25% of the world's population. Their 1st product, KIMIA Recover, a continuous monitoring device for the knee joint, has obtained CE marking and HSA approval and booked revenues from large implant companies and hospitals. It has recently won the Innomatch grant by the Temasek Foundation (one of the 6 among 256 companies across 32 countries), and is currently deployed in some of the largest public hospitals in Singapore. KIMIA Recover is a comprehensive recovery management solution that provides a reduction in the number of hospital visits and complications. The product was also the National Winner and International Top 20 of the prestigious James Dyson Award 2020 and has gained traction with major public hospitals and orthopaedic clinics. Their digital therapy platform, consisting of KIMIA Assess and KIMIA Move products, uses artificial intelligence technology for digital MSK assessment and therapy. This platform is capable of providing pre-consultation assessments and real-time exercise guidance and feedback to musculoskeletal patients and is being adopted by some of the largest private healthcare providers in the region, along with large corporate players.

Job Summary:

We are building a next-generation digital rehabilitation and fitness platform that combines computer vision, wearable sensors, and AI-driven analytics to improve patient recovery outcomes and long-term physical performance.

As a CV/ML Scientist / Engineer, you will play a core role in transforming our current computer vision pipeline into a robust, clinically reliable, and scalable system, and in developing proprietary sensor + vision fusion algorithms that become a long-term IP moat for the company.

This is not a “model fine-tuning only” role.

You will work on real-world data, noisy environments, diverse patient populations, and production constraints in healthcare.Key Responsibilities

Computer Vision & Pose Estimation

• Design, evaluate, and improve human pose estimation pipelines for rehab and fitness use cases
• Enhance robustness across:
• Clothing variations (loose clothing, hijabs, patterns)
• Lighting and camera placement
• Different body types, mobility limitations, and assistive devices
• Evaluate, integrate and sutomize models such as:
• MoveNet
• HRNet
• MediaPipe (customized / extended)
• Other open-source or commercial CV frameworks

Sensor Fusion & Motion Analysis

• Develop sensor + computer vision fusion algorithms combining:
• IMU / wearable sensor data
• RGB video-based pose estimation
• Design temporal models to improve:
• Angle accuracy (ROM)
• Motion smoothness
• Repetition counting
• Exercise quality validation
• Build algorithms resilient to partial occlusion or sensor dropout

Rehabilitation & Fitness Intelligence

Develop exercise validation and scoring algorithms:

Range of motion (ROM)

Speed, symmetry, control, compensation detection

• Contribute to personalized analytics using continuous ROM and exercise data
• Support wound image analytics and recovery progression insights (future roadmap)

Data & ML Engineering

Build pipelines for:

Data preprocessing and labeling

Model evaluation and benchmarking

Offline experimentation and online inference

Collaborate with backend engineers to deploy models in:

Real-time mobile pipelines

Cloud-based batch and streaming workflows

• Balance accuracy, latency, and compute cost

Clinical & Product Collaboration

• Work closely with:
• Product managers
• UX designers
• Clinicians and rehab specialists
• Translate clinical requirements into measurable technical metrics
• Support validation studies and algorithm documentation (DHF-ready)

Required Qualifications

Technical Skills

• Strong background in Computer Vision and Machine Learning
• Solid experience with:
• Python
• PyTorch or TensorFlow
• Hands-on experience with:
• Human pose estimation
• Time-series analysis
• Model optimization for real-time inference
• Understanding of signal processing fundamentals (IMU / motion data)

Practical Experience

• Experience deploying ML models into production, not just research
• Ability to debug models using real-world data, not curated datasets
• Comfortable working with imperfect, noisy, and limited datasets

Nice-to-Have (Strong Plus)

• Experience in healthcare, rehab, biomechanics, sports science, or fitness tech
• Sensor fusion experience (vision + IMU)
• Experience with:
• Temporal models (LSTM, TCN, transformers for motion)
• Edge or mobile ML optimization
• Familiarity with regulatory or compliance environments (HIPAA, GDPR, medical devices)
• Publications, patents, or open-source contributions in CV / ML