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$70.6K

$154.8K

$175.6K

How much do embedded machine learning jobs pay per year?

As of May 31, 2026, the average yearly pay for embedded machine learning in Philadelphia, PA is $154,777.00, according to ZipRecruiter salary data. Most workers in this role earn between $132,700.00 and $174,600.00 per year, depending on experience, location, and employer.

What is an Embedded Machine Learning job?

An Embedded Machine Learning job involves developing and optimizing machine learning models to run efficiently on resource-constrained devices like microcontrollers, edge devices, and IoT hardware. Professionals in this role work on model compression, low-power inference, and real-time processing, ensuring AI capabilities can function without relying on cloud computing. Responsibilities often include data preprocessing, feature extraction, model training, and deployment on embedded systems using frameworks like TensorFlow Lite or Edge Impulse.

What are the key skills and qualifications needed to thrive in the Embedded Machine Learning position, and why are they important?

To thrive in Embedded Machine Learning, you should have expertise in machine learning algorithms, embedded systems programming (e.g., C/C++, Python), and a solid understanding of hardware-software integration, typically backed by a degree in computer engineering, electrical engineering, or a related field. Familiarity with edge AI tools (such as TensorFlow Lite, ONNX, or Edge Impulse), microcontrollers, and real-time operating systems is highly valued, alongside relevant certifications such as Embedded Systems or AI certificates. Strong problem-solving skills, effective communication, and the ability to work cross-functionally are crucial soft skills in this field. These qualifications and qualities are vital for creating efficient, reliable AI solutions that operate seamlessly within resource-constrained environments and interdisciplinary project teams.

What are some common challenges faced by professionals working in embedded machine learning roles?

Professionals in embedded machine learning roles often face the challenge of optimizing machine learning models to run efficiently on resource-constrained hardware, such as microcontrollers or edge devices with limited memory and processing power. Balancing model accuracy, inference speed, and energy consumption can require creative problem-solving and deep knowledge of both hardware and software. Additionally, collaboration with hardware engineers, data scientists, and software developers is key, as projects typically require cross-functional teamwork to meet performance and deployment goals. Staying current with rapidly evolving tools and best practices is also important in this dynamic field.
More about Embedded Machine Learning jobs
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Embedded Machine Learning jobs near you
Senior Quantitative Equity Systems Developer

Senior Quantitative Equity Systems Developer

Vanguard Group

Malvern, PA • On-site

Full-time

Posted 7 days ago

Vanguard rating

8.7

Company rating: 8.7 out of 10

Based on 60 frontline employees who took The Breakroom Quiz

14th of 138 rated financial services

Job description

What You'll Do:
  • Design, build, and operate cloud-native platforms that run quantitative equity models across research, back-testing, and production workflows
  • Partner closely with portfolio managers, quantitative researchers, and traders to translate investment ideas into robust, scalable technology solutions
  • Architect systems that support active equity portfolio construction, risk modeling, factor analytics, and execution workflows
  • Integrate and work with carefully selected external vendor data provider solutions, ensuring reliable, performant, and well-governed use of data.
  • Collaborate with centralized enterprise technology teams to adopt and implement approved architectural patterns, platforms, and shared services
  • Lead technical design decisions across distributed systems, data pipelines, and compute-intensive workloads
  • Drive engineering best practices around resilience, observability, performance, and security in a front-office environment
  • Mentor and influence other engineers, raising the overall technical bar of the team
  • Ability to be a change agent, embrace innovation and shape people, process and technology view to solving complex problems.
  • Contribute to the long-term technology strategy for quantitative and systematic investing platforms

What we're Looking For:
This role is designed for candidates who are equally comfortable in investment conversations and deep technical discussions. You sit with the investment team, not behind layers of abstraction. It offers a rare opportunity to operate at the center of technology-driven active equity investing.
You will help shape the architecture and direction while embracing and fostering a culture that values technical excellence, intellectual curiosity, and close partnership with the business. If you are a senior engineer or technical lead who enjoys working on complex, high-stakes systems, wants to be deeply embedded in the investment process, and brings both engineering rigor and investment domain knowledge, we would love to hear from you!
Requirements:
  • Minimum of ten years related work experience, with at least five years of development experience. Prior Investment Systems experience required.
  • Undergraduate degree or equivalent combination of training and experience. Graduate degree preferred.
  • Extensive experience building large-scale, production-grade software systems in complex domains.
  • Strong understanding of quantitative or active equity investing, including concepts such as factors, signals, portfolio optimization, risk, and execution.
  • Demonstrated success working in front-office, investment-aligned technology roles.
  • Deep expertise in modern software engineering, including object-oriented and/or functional programming paradigms, distributed systems and data-intensive architectures, and cloud-native platforms such as containerization, orchestration, and managed cloud services.
  • Strong proficiency in Python and modern data processing and numerical computing tools (e.g., Polars).
  • Familiarity with machine learning and AI-assisted development techniques, including the use of AI or agentic coding tools (e.g. Claude Code) to improve research and engineering productivity.
  • Experience building and supporting AWS architecture including services such as EC2, CloudWatch, ECS, Sage Maker, ECS, Steps Functions, Lambda and Postgres.
  • Experience operating in environments where system performance, reliability, and correctness are business critical.
  • Direct experience supporting quantitative equity, systematic, or multi-factor investment strategies.
  • Familiarity with research and back-testing platforms, market and alternative data, and model lifecycle management.

Preferred Experience:
  • Exposure to both legacy enterprise systems and modern engineering stacks, with the ability to modernize thoughtfully.
  • Prior experience working side-by-side with investment teams on a trading floor or similar front-office setting.
  • Strong knowledge of equity trading and execution systems.

Special Factors
Sponsorship
Vanguard is not offering visa sponsorship for this position.
About Vanguard
At Vanguard, we don't just have a mission-we're on a mission.
To work for the long-term financial wellbeing of our clients. To lead through product and services that transform our clients' lives. To learn and develop our skills as individuals and as a team. From Malvern to Melbourne, our mission drives us forward and inspires us to be our best.
How We Work
Vanguard has implemented a hybrid working model for the majority of our crew members, designed to capture the benefits of enhanced flexibility while enabling in-person learning, collaboration, and connection. We believe our mission-driven and highly collaborative culture is a critical enabler to support long-term client outcomes and enrich the employee experience.

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