Job Details

Description

Location: San Francisco, Bay Area, Melo Park

ASAP Talent Services is a leading I.T. executive search firm. Our recruiting team has been retained by a late-stage Pre-IPO SaaS business to help scale their team near Menlo Park. If you'd like to grow with a company that is approaching $100M ARR - a firm that has raised over $500M in PE funding (including Blackrock and others) - we need to talk!

1 Openings - see details on left column below 1 Principal Architect (Navigation focused for Robotics). Experience in Multi Agent Path Finding is key for this role - someone out of Robotics, Autonomous Vehicle or Drone space is highly likely.

Job Overview

As a Principal Architect, you will lead the design and evolution of intelligent, scalable systems for Multi-Agent Path Finding (MAPF). You'll define architectural direction, integrate machine learning and reinforcement learning, and build the core capabilities required to achieve safe, efficient, and autonomous decision-making at scale (Thousands or Millions of Nodes).

Key Responsibilities

• Architect distributed systems to support real-time path planning of multi-agent using Machine Learning.
• Lead design and implementation of ML model pipelines, including data ingestion, training, validation, deployment, and monitoring.
• Own production deployment of ML/RL models using MLOps tools such as Vertex AI or similar platforms.
• Integrate ML pipelines with robotic orchestration engines to support continuous learning and adaptation.
• Collaborate closely with software, robotics, product, and operations teams to align system goals with real-world fulfillment challenges.

• Education: B.E/M.S in Computer Science, AI/ML, or a related field.
• Experience: 12+ years of total experience (7+ years of experience in system architecture & AI/ML ; including 2+ years in technical leadership roles).

• Strong knowledge of path planning, graph search algorithms, and optimization techniques for multi-agent systems.
• Deep understanding of machine learning, deep learning, and reinforcement learning, with experience using TensorFlow or PyTorch.
• Proven experience in building, deploying, and maintaining ML models in production environments.
• Hands-on experience with MLOps, including CI/CD for models, pipeline orchestration, and model monitoring.
• Proficiency in Python. Familiarity with Erlang, Elixir, Gleam or other concurrency-first functional programming languages is good to have.
• Solid understanding of concurrency, parallelism, and real-time systems.
• Strong CS fundamentals including algorithms, operating systems, networking, memory management, and performance tuning.
• Experience with distributed systems, microservices, and containerized deployments using Docker and Kubernetes.
• Knowledge of event-driven architectures
• Familiarity with cloud platforms such as Google Cloud Platform (GCP), and specifically Vertex AI.

• (Nice to Have) - Contributions to open-source projects, research publications, or patents in MAPF, RL, or distributed AI systems.
• Any experience in Robotics, Autonomous Vehicles or Drone Tech is desired