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Phd Machine Learning Jobs in Cambridge, MA (NOW HIRING)

MatrixSpace

Machine Learning Engineer

Burlington, MA · Remote

Required * BS, MS, or PhD in Computer Science, Electrical Engineering, Applied Mathematics, Machine Learning, AI, Robotics, or a related field. * Strong hands-on programming experience in C++ and ...

Neurable, INC

Machine Learning

Cambridge, MA · On-site

... expert and PhD neuroscience, enables people to control hardware and software in real-time with ... We are currently looking for a Machine Learning Scientist/Researcher to join our team. We would ...

Neurable, INC

Machine Learning

Cambridge, MA

... expert and PhD neuroscience, enables people to control hardware and software in real-time with ... We are currently looking for a Machine Learning Scientist/Researcher to join our team. We would ...

Ayo Semiconductor

Machine Learning Architect

Boston, MA · On-site

PhD in machine learning, representation learning, theory of computation, or a related field - or equivalent industry experience working on foundation models at scale. * Experience training models at ...

Bayer

Principal Machine Learning Scientist

Cambridge, MA · On-site

$128K - $192K/yr

Principal Machine Learning Scientist The Principal Machine Learning Scientist will develop novel ... Preferred Qualifications: 5+ years of relevant post-PhD experience, including 2+ years in industry;

Cellarity

Machine Learning Scientist

Somerville, MA · On-site

PhD in Computer Science, Computational Biology, or related field, OR Master's degree with 3+ years ... apply machine learning to diverse disease areas. Cellarity is a product of Flagship Pioneering ...

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Phd Machine Learning information

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$15

$24

$33

How much do phd machine learning jobs pay per hour?

As of Aug 31, 2026, the average hourly pay for phd machine learning in Cambridge, MA is $24.94, according to ZipRecruiter salary data. Most workers in this role earn between $21.54 and $27.84 per hour, depending on experience, location, and employer.

What is a PhD in machine learning?

A PhD in Machine Learning is an advanced doctoral degree focused on developing new algorithms, theories, and applications in the field of machine learning. Graduates typically conduct original research, contribute to academic publications, and often specialize in areas like deep learning, reinforcement learning, or probabilistic modeling. This degree prepares individuals for careers in academia, industry research labs, or leadership roles in tech companies. The program usually involves coursework, comprehensive exams, and the completion of a dissertation based on novel research.

What are the key skills and qualifications needed to thrive as a PhD-level machine learning professional?

To thrive as a PhD-level Machine Learning professional, you need deep expertise in mathematics, statistics, computer science, and advanced machine learning algorithms, typically supported by a doctoral degree. Proficiency with programming languages like Python or R, machine learning frameworks such as TensorFlow or PyTorch, and experience with large-scale data systems are essential. Strong problem-solving skills, critical thinking, and effective communication set outstanding candidates apart by enabling them to tackle complex research challenges and collaborate across teams. These skills and qualities are crucial for driving innovation, publishing research, and developing impactful machine learning solutions.

What are some common challenges faced by PhD-level professionals in machine learning when transitioning from academia to industry roles?

PhD graduates in machine learning often encounter challenges such as adapting to faster-paced project timelines, aligning research with business objectives, and collaborating in multidisciplinary teams. Unlike academia, where projects can be exploratory and long-term, industry roles usually require actionable results within shorter deadlines. Additionally, communicating complex technical ideas to non-technical stakeholders and prioritizing practical solutions over theoretical novelty are key adjustments. However, these challenges also present opportunities for professional growth and broader impact.

What is the difference between Phd Machine Learning vs Data Scientist?

AspectPhd Machine LearningData Scientist
Required CredentialsPhD in Computer Science, AI, or related fieldBachelor's or Master's in Data Science, Statistics, or related field
Work EnvironmentResearch labs, academia, R&D departmentsBusiness, tech companies, analytics teams
Industry UsageResearch-focused roles, advanced algorithm developmentData analysis, model building, business insights
Common Search/ComparisonYesYes

While both roles involve working with data and algorithms, a Phd Machine Learning typically focuses on research, developing new models, and theoretical work, often in academic or R&D settings. A Data Scientist applies these techniques to solve practical business problems, analyze data, and generate insights in industry environments.

How much does a PhD in machine learning make?

A PhD in machine learning typically earns between $100,000 and $150,000 annually in industry roles, with salaries increasing for senior positions or in high-demand sectors. Academic positions may offer lower salaries but include research funding and teaching responsibilities.

What can you do with a PhD in machine learning?

A PhD in machine learning prepares individuals for advanced roles such as research scientist, machine learning engineer, data scientist, or AI specialist. These roles involve developing algorithms, analyzing large datasets, and applying AI techniques across industries like technology, healthcare, finance, and autonomous systems. Strong programming skills and knowledge of tools like Python, TensorFlow, or PyTorch are essential for these positions.

What cities near Cambridge, MA are hiring for Phd Machine Learning jobs?

Cities near Cambridge, MA with the most Phd Machine Learning job openings:

Phd Machine Learning jobs near you
Infographic showing various Phd Machine Learning job openings in Cambridge, MA as of August 2026, with employment types broken down into 1% As Needed, 71% Full Time, 26% Part Time, 1% Temporary, and 1% Contract. Highlights an 83% Physical, 3% Hybrid, and 14% Remote job distribution, with an average salary of $51,881 per year, or $24.9 per hour.

Machine Learning Engineer

Burlington, MA • Remote

MatrixSpace
Computer Networking • 11 - 50 employees
Learn more about MatrixSpaceexternal

Full-time

Re-posted 23 days ago

Job description

Help us bridge machine learning research and real-world deployment!

MatrixSpace develops AI-enabled radar and sensing systems that help people understand what's happening in the world around them. By combining advanced radar, edge computing, and AI, we deliver situational awareness in environments where traditional sensing solutions struggle.
We're looking for a hands-on Machine Learning Engineer who enjoys turning cutting-edge ML research into production-ready software. You'll partner closely with our Data Scientists, taking new algorithms and implementing them in performant, maintainable, and scalable production systems. You'll also help build the ML infrastructure and tooling that accelerates future research, while ensuring our AI solutions are reliable enough for real-world deployment.

If you're technically curious, highly collaborative, and motivated by solving complex real-world problems, we'd love to talk.

What You'll Do

  • Partner with Data Scientists to transform research algorithms into robust, production-quality software.
  • Implement machine learning algorithms in high-performance C++ and Python with a focus on maintainability, scalability, and real-time performance.
  • Build and improve machine learning infrastructure, tooling, and training pipelines that enable faster experimentation and more efficient model development.
  • Design and implement AI agents, agentic workflows, and LLM-powered applications.
  • Deploy and maintain AI workloads across edge, near-edge, and cloud environments.
  • Collaborate across engineering and research teams to transition prototypes into production systems.

What We're Looking For

This position requires working directly or indirectly with the US Government in restricted environments. Candidates must be legally authorized to work in the United States without employer sponsorship and may be required to obtain and maintain a U.S. government security clearance in the future.'

This is NOT a fully remote position!

Required

  • BS, MS, or PhD in Computer Science, Electrical Engineering, Applied Mathematics, Machine Learning, AI, Robotics, or a related field.
  • Strong hands-on programming experience in C++ and Python.
  • 3-5 years of experience developing and deploying machine learning systems in production environments.
  • Experience building AI agents, LLM-based applications, or intelligent automation systems.
  • Strong problem-solving skills and ability to work across the full development lifecycle.
  • Excellent written and verbal communication and collaboration skills.

Someone Who Will Thrive in This Role

  • Enjoys solving difficult technical challenges that span algorithms, software, and deployment.
  • Enjoys bridging the gap between research and production, finding practical engineering solutions that make advanced ML usable in real-world products.
  • Takes ownership and drives projects from concept through production.
  • Continuously explores new AI, ML, and agentic technologies.
  • Works effectively across multidisciplinary teams.
  • Balances research innovation with practical product delivery.
  • Builds side projects, experiments with emerging AI tools, or enjoys hands-on technical exploration.

Bonus Points

  • Experience with radar, RF sensing, sensor fusion, computer vision, robotics, or autonomous systems.
  • Experience with LangChain, LangGraph, LlamaIndex, AutoGen, Semantic Kernel, or similar frameworks.
  • Experience optimizing models for edge deployment usingTensorRT, ONNX,OpenVINO, TVM, or similar tools.
  • Experience with embedded systems, GPUs, NPUs, FPGAs, or hardware acceleration.
  • Familiarity withMLOps, CI/CD, model monitoring, and large-scale production systems.

At MatrixSpace, Machine Learning Engineering is where advanced AI research becomes real-world capability. This is an engineering-heavy ML role focused on productionizing algorithms created by Data Scientists, with some ownership of the ML infrastructure that helps those Data Scientists move faster.