Machine Learning Engineer Jobs

Robotics software and AI companies lead hiring, followed by autonomous vehicle developers and aerospace firms. NVIDIA has 51 open positions across perception, simulation, and robotics platforms. Analog Devices and Qualcomm hire ML engineers to build AI capabilities into edge processors. Amazon develops warehouse robots and last-mile delivery systems. Anduril builds defense applications.

Beyond these established names, well-funded startups in manipulation, humanoid robots, agricultural robotics, and construction automation are hiring aggressively. Many have raised significant capital and offer competitive compensation plus meaningful equity.

Geographic concentration is extreme. Most positions are in the Bay Area, Seattle, or Pittsburgh. Some defense contractors offer positions in Southern California and Northern Virginia. Remote work is uncommon since robotics ML requires close collaboration with hardware teams and access to physical systems for validation.

You develop ML models that enable robots to perceive their environment, make decisions, and improve from experience. Common projects include building perception systems that detect and track objects, training manipulation policies that generalize across objects, developing motion planning systems that learn from demonstrations, or creating sim-to-real transfer approaches that reduce the reality gap.

Day-to-day work involves training models on large datasets, debugging why models fail on specific edge cases, optimizing inference for real-time performance on embedded hardware, and validating that models work reliably on physical robots. You'll spend significant time on data infrastructure since robotics datasets are often messy, poorly labeled, or missing the failure cases you care about.

The role differs from pure ML engineering because you must understand the physical constraints and failure modes of robotic systems. A perception model with 95% accuracy might be publishable but completely inadequate for a robot that could injure people if it misclassifies objects. You need to think about worst-case performance, not just average-case metrics.

Based on 400 job postings, median salaries are $185,300 annually. Engineers with ML experience but new to robotics applications typically start around $135,000. Senior engineers with production experience shipping ML-powered robotic systems earn $225,750 or more, with total compensation reaching $465,000 at top-tier companies when equity is included.

The highest earners work at autonomous vehicle companies, large tech firms like NVIDIA or Meta building robotics platforms, or well-funded startups with significant equity upside. Bay Area positions typically pay 30-40% more than similar roles in other regions. Defense contractors often pay lower base salaries but offer better work-life balance and job stability.

Compensation reflects genuine talent scarcity. The skillset requires deep ML knowledge plus understanding of robotics, computer vision, and real-time systems. Published research, particularly at top-tier venues like RSS, ICRA, or CoRL, strengthens negotiating position significantly.

Demand is exceptionally strong. 320 active positions show no signs of slowing as more companies attempt to incorporate learning-based methods into robotic systems. The field sits at the intersection of two high-growth areas, which creates both opportunity and job security.

Career progression offers multiple paths. You can advance to senior IC roles with increasing technical scope and compensation, move into research leadership if you have strong publication records, or transition into ML engineering management. Some engineers shift into robotics startups as founding technical team members, leveraging their expertise to build new companies.

The learning curve never flattens. New architectures, training techniques, and deployment methods emerge constantly. Foundation models, sim-to-real transfer, and data-efficient learning are active research areas with immediate practical applications. Engineers who stay current with research while maintaining strong engineering discipline remain highly marketable.