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Phd Machine Learning Jobs (NOW HIRING)

Poesis

Machine Learning Engineer

San Francisco, CA · On-site

$200K - $280K/yr

About the Role At Poesis, machine learning and artificial intelligence open the door to improved ... Skill leveraging Claude Code, Codex, or other coding agents * BS/MS/PhD in Computer Science or a ...

Apple

Machine Learning Engineer

Austin, TX · On-site

We are looking for a passionate, highly motivated, and hands-on applied Machine Learning Engineer ... Qualifications PhD or Graduate degree with research/work experience utilizing data science ...

MatrixSpace

Machine Learning Engineer

Burlington, MA · Remote

$165K - $200K/yr

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 ...

Motorola

Machine Learning Engineer

Los Angeles, CA · On-site

Minimum of 3 years of experience in machine learning, with demonstrated application to real-world problems; 1 year of machine learning experience with a PhD. * Strong foundation in supervised and ...

Visa

Director, Machine Learning

Austin, TX · Hybrid

We are currently looking for a Director of Machine Learning who will take the lead and manage ... Masters, MBA, JD, MD) or 4 years of work experience with a PhD, OR 13+ years of relevant work ...

Qualcomm

Machine Learning Researcher

San Diego, CA · On-site

OR PhD in Computer Engineering, Computer Science, Electrical Engineering, or related field. • 6+ months of academic and/or work experience developing and/or optimizing machine learning models ...

Nace AI

Machine Learning Engineer

Palo Alto, CA · On-site

As a Machine Learning Engineer, you will play a central role in translating cutting-edge machine ... MS/PhD in CS or related technical field. * Familiarity with data processing stacks such as Spark ...

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Phd Machine Learning information

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How much do phd machine learning jobs pay per hour?

As of Jul 14, 2026, the average hourly pay for phd machine learning in the United States is $22.82, according to ZipRecruiter salary data. Most workers in this role earn between $19.71 and $25.48 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, and why are they important?

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.

What cities are hiring for Phd Machine Learning jobs? Cities with the most Phd Machine Learning job openings:
What states have the most Phd Machine Learning jobs? States with the most job openings for Phd Machine Learning jobs include:
Phd Machine Learning jobs near you
Infographic showing various Phd Machine Learning job openings in the United States as of July 2026, with employment types broken down into 1% As Needed, 74% Full Time, 23% Part Time, 1% Temporary, and 1% Contract. Highlights an 89% Physical, 1% Hybrid, and 10% Remote job distribution, with an average salary of $47,468 per year, or $22.8 per hour.

Machine Learning Engineer

Poesis

San Francisco, CA • On-site

$200K - $280K/yr

Full-time

Medical, Dental, Vision

Posted 26 days ago

Job description

About Poesis
Whoever builds the leading intelligence for finance will create far more than returns. Poesis is the AI-native investment firm running autonomous agents that predict markets, construct portfolios, and manage risk. Our founders managed institutional capital at Capital Group ($3T AUM) and led enterprise ML at Goldman Sachs and Amazon. We're building a new type of firm, where live capital is the training ground for an intelligence that compounds with every signal.
About the Role
At Poesis, machine learning and artificial intelligence open the door to improved alpha discovery, higher quality decision-making and intelligent risk management. We're looking for an exceptional Machine Learning Engineer to help build the systems that make this possible. In this role, you'll develop models, signals and evaluation frameworks that power investment decision-making across the platform. You'll work across the full machine learning lifecycle, from experimentation and model and agent development to deployment and iteration, with significant ownership over both research and production outcomes.
Responsibilities
  • Rapidly implement and iterate on machine learning models, signals and research ideas
  • Design and run experiments to evaluate and improve model and agent performance and investment impact
  • Build reproducible workflows for feature generation, training, validation and evaluation
  • Work with large-scale financial, fundamental and alternative datasets to identify predictive signals and improve model performance

Required Competencies
  • 5+ years experience as a Machine Learning Engineer, or related role
  • Prior experience at a frontier AI lab, agentic startup, leading hedge fund, big tech company, or similar
  • Strong Python and SQL skills, with experience working with large-scale datasets
  • Experience developing, evaluating and deploying machine learning models in production environments
  • Success building reproducible research workflows and experimentation frameworks
  • Familiarity with modern AI systems, including LLMs, evaluation frameworks, and agent workflows
  • Skill leveraging Claude Code, Codex, or other coding agents
  • BS/MS/PhD in Computer Science or a related field, or equivalent practical experience

Preferred Competencies
  • Experience developing ML and AI systems using financial, fundamental, alternative, or time-series datasets
  • Familiarity with quantitative investing, portfolio construction, or risk management
  • Experience with PyTorch or TensorFlow, and AI workflows for parsing financial documents (filings, transcripts)

Location
Hybrid: 3 days per week on-site at our office in Menlo Park, CA. Relocation allowance available.
Benefits
We offer excellent medical, dental, and vision coverage, alongside a strong benefits package that includes catered lunches in our Menlo Park office, commuter benefits, and more.
Current legal authorization to work in the US required; continuing work visa sponsorship available for full-time employees.
Working at Poesis
As an early team member, you'll help shape not just the product, but how the company operates. Your decisions will have lasting impact across the business. You'll build from first principles, with no legacy systems, or entrenched processes slowing you down. Our team is made up of people from elite companies and universities who are low ego, collaborative, and excited to build together.

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