About The Position

We are seeking an ML Solutions Architect who brings broad software engineering expertise along with strong machine learning-adjacent experience. In this role, you will lead the high-level design of systems that integrate ML models into our broader product suite. You will act as a technical consultant, evaluating customer requirements and determining whether they can be addressed with off-the-shelf solutions or should be escalated as specialized research initiatives for our Deep Learning and Reinforcement Learning teams. This role is ideal for a systems-minded engineer who can translate product vision into scalable architecture, while balancing technical feasibility, performance, and maintainability.

Requirements

  • Degree in Computer Science, Software Engineering, or a related field.
  • Proven experience as a Software Architect or Systems Engineer in a fast-paced environment.
  • Prior experience in industries with complex multi-disciplinary teams such as robotics, smart grids, precision agriculture, game development, or aerospace.
  • Fluency with Git, and the Unix shell, with a strong ability to work across multiple programming languages as needed (ideally including one or more of Python, C++, C#).
  • Deep understanding of how to integrate ML models into production software (e.g., API design, message brokers, and containerization, compute and memory budgeting).
  • While you may not be training models daily, you must have enough ML-adjacent experience to understand model constraints, data requirements, and the "state of the art."
  • Ability to perform "Build vs. Buy" analyses for ML components.
  • Exceptional ability to translate high-level product vision into concrete engineering specs for both technical leadership and specialized engineers.
  • High attention to detail regarding system stability and interoperability.

Nice To Haves

  • Experience with fine-tuning and deploying models is a plus.

Responsibilities

  • Design and implement the software layers that allow ML models to interact with a real-time rendering engine. This includes managing data pre-processing and post-processing (coprocessing) to ensure high-performance execution.
  • Evaluate incoming customer requirements to determine the optimal path forward. You will decide if a task can be solved using off-the-shelf tools or if it requires a deep-dive research project to be handed off to our Deep Learning or Reinforcement Learning engineers.
  • Build and maintain wrappers, APIs, and microservices that allow our ML stack to remain flexible and language-agnostic across different production environments.
  • Act as the primary technical liaison between technical leadership, customers, and the core engineering team to spec out data and integration requirements.
  • Break down complex product visions into manageable architectural components, ensuring that ML components ship as part of a stable, scalable software product.
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