Doublepoint
Senior ML Engineer
DoublepointFinland11 hours ago
Full-timeEngineering
Background

Doublepoint creates cutting-edge interaction technology for next generation user interfaces on smartwatches, TV’s and AR. Our smartwatch algorithms detect subtle, intuitive hand gestures using only built-in sensors like the IMU and PPG — something that hasn’t previously been possible.

To expand the number of gestures we support and push our current set closer to perfection, we’re growing our Algorithm Team. We’re looking for a Principal ML Engineer with deep time-series expertise and strong technical leadership to help own and develop our core product: the gesture classification algorithm.

Responsibilities

As Principal ML Engineer, your primary responsibility is technical. This means owning the design, experimentation, performance and deployability of our gesture classification model as a whole which may be at varying levels of technological maturity. This includes:

  • Assessing and understanding the feasibility of classification tasks with current signals
  • Defining meaningful test sets and metrics, and maintaining both a general and detailed understanding of model performance
  • Defining, requesting, and curating the right training data or hardware modifications needed to improve model quality
  • Guiding ML Ops engineers on building the required scalable deployment and evaluation infrastructure
  • Leading efforts to deploy models securely on embedded compute across a range of sensor hardware

As a technical leader on the team, you’ll also:

  • Provide structure, processes, and technical direction for the Algorithm Team when needed
  • Help coordinate sprint planning and prioritize work aligned with company-wide quarterly goals
  • Mentor junior developers and coordinate the work of relevant freelancers

In Addition, You’ll Collaborate Cross-functionally With

  • The data collection team to define effective dataset collection strategies
  • The user research team to help define new gesture types
  • The hardware team to inform future revisions of our hardware sensing stack

What’s Not Your Responsibility

  • Implementing hardware
  • Pitching to clients
  • Leading or implementing ML Ops infrastructure
  • Owning the full tech roadmap

Requirements

  • 5 or more years of machine learning engineering experience in production, ideally in a real-time, time-series and user-centred environments.
  • Deep scientific understanding and proven track record with Machine Learning including supervised and unsupervised learning, deep learning, data augmentation, classification, and embedded deployment.
  • Strong software engineering skills in Python, Pytorch, TensorFlow Lite, and familiar with scalable ML pipeline tools like Kubeflow, MLflow, Optuna, Hydra and CICD workflows.
  • Experience with high accuracy models where high recall and low false positives is critical.
  • Experience with signal and model performance variability due to different sensor manufacturers, user anatomies, or usage environments.

Our ideal candidate would also possess

  • Familiarity with human computer interaction, and both online and offline testing
  • Familiarity with hardware sensors and embedded systems such as IMUs, PPGs, ARM M4 processor architectures, and Tensorflow Lite
  • Defining hardware and datasets for models
  • Experience with basic signal processing, bayesian statistics.

Qualities

  • Cares deeply about both model performance and the end user experience
  • High agency including taking initiative, owning challenges and outcomes
  • Adaptability to work on both high-level strategy and detailed implementation
  • Clear communication to articulate complex ideas to technical and non-technical stakeholders.

Hiring Process

  • Intro Call & Q&A with CTO Jamin Hu (30 mins)
  • Take-home challenge and team review
  • Technical Interview with Algorithm Team (1:30h)
  • Culture Interview with CEO Ohto Pentikäinen (30 mins)
  • Offer

If you have any questions about the role, don’t hesitate to ask. We welcome you to join us in inventing the future standard of human computer interaction.

Keywords

Machine Learning, Deep Learning, Signal Processing, Algorithm Engineer, Software Engineering, ML Ops, Time Series, Sensors, Human Computer Interaction (HCI)

Key Skills

Ranked by relevance
Apply