ML Architect

ML Architect

An innovative and rapidly scaling SaaS platform, backed by over $500M in growth equity, is revolutionizing intelligent systems and autonomy across logistics and high-density fulfillment environments. With impressive ARR growth and a prestigious customer base, this pre-IPO disruptor is building toward the next frontier of AI-powered decision-making.

This firm is searching for an advanced AI professional to drive architectural innovation across multi-agent systems. This is a rare opportunity to lead the evolution of intelligent pathfinding frameworks powered by deep learning and reinforcement learning. The selected candidate will architect distributed, scalable systems that enable real-time planning and autonomous decision-making at scale.

This Role Offers :

Hybrid role with flexible work model (Redwood City area preferred).

Base salary, commensurate with experience and impact.

Competitive equity package, performance bonuses, and top-tier benefits.

Focus :

Spearhead design and development of distributed systems for multi-agent path planning using AI / ML algorithms.

Oversee the deployment of machine learning and reinforcement learning models within production environments using modern MLOps frameworks.

Design and optimize ML pipelines for continuous data ingestion, training, deployment, monitoring, and feedback adaptation.

Integrate AI models with orchestration engines that facilitate real-time learning and system adaptability.

Serve as the architectural thought leader across ML engineering, software, robotics, and operational stakeholders.

Partner with cross-functional teams to translate real-world operational challenges into scalable, intelligent solutions.

Skill Set :

Bachelor’s or Master’s degree in Computer Science, AI / ML, or related field.

12+ years of experience in system design, including 7+ years in AI / ML-based architecture, and at least 2 years in leadership roles.

Extensive background in building and scaling machine learning pipelines, with experience using tools such as Vertex AI or similar platforms.

Strong foundation in graph-based pathfinding, real-time optimization, and multi-agent system algorithms.

Deep fluency in reinforcement learning, deep learning, and probabilistic decision-making frameworks.

Proficiency in Python and familiarity with concurrency-first functional programming languages (e.g., Erlang, Elixir) is a plus.

Advanced understanding of distributed computing, event-driven systems, microservices, and real-time architecture.

Experience with containerized deployments using Docker and Kubernetes in cloud environments (especially GCP).

Solid computer science fundamentals (memory management, networking, OS, concurrency).

Bonus Qualifications :

Contributions to peer-reviewed research, patents, or open-source initiatives related to RL, MAPF, or distributed AI.

Experience in mission-critical environments requiring safety, adaptability, and fault tolerance.