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Robotics Perception Engineer Jobs in Tracy, CA (NOW HIRING)

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Robotics Perception Engineer information

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$31.2K

$113.7K

$181.9K

How much do robotics perception engineer jobs pay per year?

As of Aug 19, 2026, the average yearly pay for robotics perception engineer in Tracy, CA is $113,682.00, according to ZipRecruiter salary data. Most workers in this role earn between $89,900.00 and $136,700.00 per year, depending on experience, location, and employer.

What is a robotics perception engineer?

Robotics Perception Engineers are professionals who specialize in enabling robots to interpret and understand their environment using sensors and data processing algorithms. They work on developing and implementing computer vision, sensor fusion, and machine learning techniques so that robots can perceive objects, people, and surroundings. Their work is crucial for applications such as autonomous vehicles, drones, industrial automation, and service robots. By improving a robot's ability to 'see' and make sense of the world, they help create safer and more effective robotic systems.

What are the key skills and qualifications needed to thrive as a robotics perception engineer?

To thrive as a Robotics Perception Engineer, you need strong expertise in computer vision, sensor fusion, machine learning, and proficiency in programming languages like C++ and Python, often supported by a degree in robotics, computer science, or a related field. Familiarity with tools and frameworks such as ROS (Robot Operating System), OpenCV, and deep learning libraries, as well as experience with sensors like LiDAR and cameras, is typically required. Excellent problem-solving abilities, teamwork, and adaptability help set standout professionals apart in this role. These competencies are crucial for enabling robots to accurately interpret and interact with their environment, leading to robust and reliable autonomous systems.

What are some common challenges faced by robotics perception engineers when integrating new sensors into autonomous systems?

Robotics Perception Engineers often encounter challenges such as sensor calibration, data synchronization, and managing varying data quality when integrating new sensors. Ensuring that sensor data is accurately aligned in time and space is crucial for reliable perception in autonomous systems. Additionally, engineers must address the complexities of fusing data from multiple modalities (like cameras, LiDAR, or radar) while optimizing processing efficiency. Close collaboration with hardware and software teams is essential to troubleshoot integration issues and achieve robust, real-time perception.

What is the difference between Robotics Perception Engineer vs Computer Vision Engineer?

AspectRobotics Perception EngineerComputer Vision Engineer
Required CredentialsBachelor's or Master's in Robotics, Computer Science, or Electrical Engineering; experience with perception algorithmsBachelor's or Master's in Computer Science, Electrical Engineering, or related fields; strong programming skills in vision processing
Work EnvironmentRobotics labs, autonomous vehicle companies, industrial automationSoftware companies, tech startups, research labs focusing on image and video analysis
Industry UsageAutonomous vehicles, robotics, manufacturingHealthcare, security, consumer electronics, automotive

Robotics Perception Engineers focus on developing perception systems specifically for robots, integrating sensors and perception algorithms for navigation and interaction. Computer Vision Engineers primarily develop algorithms for interpreting visual data across various applications. While both roles require strong programming and understanding of perception, Robotics Perception Engineers specialize in sensor fusion and real-time processing within robotic systems, whereas Computer Vision Engineers work more broadly on image analysis and recognition tasks.

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Staff Applied AI Researcher - Agentic Reasoning Systems (India)

Articul8

Dublin, CA • On-site

Full-time

Re-posted 15 days ago

Job description

About us:
Articul8 was born from a simple belief: GenAI should work for the enterprise, not the other way around. Our platform combines domain-specific models, autonomous agentic reasoning through ModelMesh(TM), reliable model evaluation through LLM-IQ(TM), and multimodal understanding to serve regulated industries including energy, semiconductor, finance, aerospace, and supply chain. Trusted by Fortune 500 enterprises, we bring together research, engineering, product, and domain expertise to deliver AI that meets the accuracy, explainability, and auditability standards that high-stakes environments demand.
Job Description:
Articul8 AI is seeking a Staff Applied AI Researcher to define how our platform reasons at runtime and how autonomous systems make trustworthy decisions in production. You will lead research across the core runtime intelligence capabilities behind ModelMesh(TM): task decomposition, agent coordination, model and tool routing, probabilistic decisioning, verification, observability-aware execution, and the evaluation methods that determine whether autonomous behavior is reliable enough for enterprise use.
Responsibilities:
  • Set technical direction for agentic reasoning systems and runtime intelligence across ModelMesh™ - define the orchestration strategies, decision policies, verification approaches, and runtime quality standards that determine how massively parallel agent systems reason, coordinate, and self-correct in production
  • Architect the infrastructure for researcher augmentation at scale - design the agentic platforms and orchestration primitives that enable every researcher and engineer at Articul8 to deploy fleets of AI agents for experimentation, evaluation, and production integration - multiplying the depth, breadth, and velocity of the entire organization
  • Go deep: advance the science of autonomous reasoning - design, train, and refine the learned components behind runtime decisioning (routing models, verification models, confidence estimators, reward models, policy selectors), using massively parallel agent-driven experiment pipelines to explore architectural and algorithmic frontiers exhaustively
  • Go broad: unify perception, retrieval, reasoning, and action - build repeatable methodology for composing domain-specific models, data perception systems, knowledge graphs, retrieval layers, and external tools into coherent agentic workflows, delegating integration testing and cross-modal benchmarking to parallel agent systems so you can reason across the full stack simultaneously
  • Drive research on agent reliability for regulated environments - lead failure detection, self-checking, verification workflows, compounding error analysis, and auditable autonomous behavior research, using agent-orchestrated stress testing and red-teaming at scales that manual evaluation cannot reach
  • Define evaluation methodology for runtime intelligence - establish how task success, decision quality, robustness, traceability, and failure recovery are measured under realistic enterprise conditions, building agentic evaluation harnesses that run continuously and surface regressions before they reach customers
  • Influence platform-level architecture - shape decisions on model routing, tool use, observability, governance, access control, and interoperability with external agent ecosystems, ensuring the platform is designed for humans and agents to amplify each other
  • Mentor researchers across levels in the agentic paradigm - raise the bar on technical judgment, experimental rigor, and agent-augmented research practice; contribute to hiring researchers who are driven to maximize their human potential
  • Maintain hands-on research impact - sustain a meaningful personal research contribution through technical work, publications, patents, and externally visible output, modeling what it looks like to be a deeply technical leader who uses agentic systems to go deeper and faster than ever before

Required Qualifications:
  • Education: PhD or MSc in Computer Science, Machine Learning, AI, Robotics, or a related field.
  • Experience: 8+ years in AI/ML research with demonstrated impact on production systems, including 3+ years building LLM-based or autonomous AI systems.
  • Reasoning and orchestration: Deep hands-on experience in at least two of: multi-agent coordination, planning under uncertainty, sequential decision-making, probabilistic inference, model routing, or tool-using agent systems. You've built systems where multiple models must collaborate to produce a reliable outcome.
  • Evaluation of autonomous systems: You have designed evaluation frameworks for systems where correctness is not binary - measuring decision quality, reliability under distribution shift, compounding error rates, and failure recovery in production-like conditions.
  • Systems at scale: You have designed and operated research systems that integrate multiple models, data sources, and control mechanisms in production or near-production settings. You understand the difference between a demo and a system.
  • Software engineering: Proficient in Python with strong software architecture instincts. Your systems are maintainable, testable, and operable.
  • Technical leadership: You have set technical direction for a research area, mentored researchers, and influenced quality standards beyond your immediate team.

Preferred Qualifications:
  • Experience building orchestration systems with non-trivial control flow - dynamic routing, verification loops, probabilistic gating - not just prompt chaining or fixed DAGs.
  • Background in probabilistic modeling, Bayesian inference, control theory, or formal verification applied to ML systems - you can reason about uncertainty, not just measure it.
  • Experience with reliability engineering for autonomous AI in regulated environments: observability, safety constraints, graceful degradation, and audit trails.
  • Track record of integrating heterogeneous components (retrieval, knowledge graphs, domain models, external APIs) into systems that are more reliable than their individual parts.
  • Strong publication record with evidence of sustained, focused research impact - not just breadth.
  • Experience taking reasoning or agent systems from prototype to production serving real enterprise customers.
  • Domain familiarity in energy, semiconductor, finance, aerospace, telecom, or supply chain.

Professional Attributes (Code42):
  • Practice Humility: You recognize that setting technical direction is a responsibility, not a status. You change your mind publicly when the evidence demands it and build a team culture where the best idea wins regardless of who proposed it.
  • Bias for Outcomes: You define success by customer and platform impact, not research novelty alone. You make hard prioritization calls and hold yourself accountable for whether the team's work moved the needle.
  • Care Deeply: You take personal responsibility for the reliability and trustworthiness of the systems your team builds. You invest in the people around you - their growth, their clarity, their ability to do their best work - because that's how real quality is sustained.
  • Dare to Do the Impossible & Embrace Scarcity: You take on problems that don't have known solutions and structure them into tractable research programs. You don't wait for perfect resources - you build with what you have and make the case for what you need with results, not requests.
  • Build a Better World: You ensure that the autonomous systems you build are worthy of the trust enterprises place in them. You hold the team to standards of auditability, reliability, and fairness that go beyond what's required - because you believe the bar should be set by builders, not regulators.

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