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Graduate Machine Learning Jobs (NOW HIRING)

Photon

Machine Learning Engineer - NJ

Addison, TX

$54 - $71.50/hr

We are seeking a Machine Learning Engineer to design and develop robust analytics models using ... Undergraduate or Graduate degree in Computer Science, Mathematics, Physics, or related fields. A ...

Photon

Machine Learning Engineer - NJ

Addison, TX · On-site

$54 - $71.50/hr

We are seeking a Machine Learning Engineer to design and develop robust analytics models using ... Undergraduate or Graduate degree in Computer Science, Mathematics, Physics, or related fields. A ...

Apple

Senior Machine Learning Engineer

Austin, TX · On-site

$121K - $160K/yr

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

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How much do graduate machine learning jobs pay per year?

As of Jul 10, 2026, the average yearly pay for graduate machine learning in the United States is $42,584.00, according to ZipRecruiter salary data. Most workers in this role earn between $32,500.00 and $46,000.00 per year, depending on experience, location, and employer.

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

To thrive as a Graduate Machine Learning professional, you need a solid understanding of statistics, data analysis, machine learning algorithms, and programming languages such as Python or R, typically supported by a relevant degree. Familiarity with machine learning frameworks (e.g., TensorFlow, PyTorch), data visualization tools, and version control systems like Git is common. Strong problem-solving abilities, communication skills, and a collaborative mindset will help you stand out in team-based environments. These competencies are vital for effectively building, analyzing, and refining models to address real-world business challenges.

What is a Graduate Machine Learning job?

A Graduate Machine Learning job is an entry-level role designed for recent graduates with a background in machine learning, data science, or a related field. It typically involves working on data-driven projects, developing machine learning models, and assisting in research or engineering tasks. Graduates may collaborate with data scientists, software engineers, and business teams to design algorithms, optimize models, and deploy AI solutions. This role helps build practical experience in applying ML techniques to real-world problems while contributing to the organization's AI initiatives.

What does the typical career progression look like for a Graduate Machine Learning professional?

As a Graduate Machine Learning professional, you will usually begin your career by working on smaller projects or supporting senior scientists with data preparation, model training, and performance evaluations. Over time, as you gain experience and demonstrate technical proficiency, you’ll be given more complex, independent projects and may specialize in areas like natural language processing, computer vision, or deep learning. Many organizations provide opportunities for mentorship, professional development, and advanced certifications, paving the way for roles such as Machine Learning Engineer, Data Scientist, or Research Scientist. This path offers significant opportunities for growth, both in terms of technical expertise and leadership potential.

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Graduate Machine Learning jobs near you
Machine Learning Engineer, Next-Generation Recommendation Systems

Machine Learning Engineer, Next-Generation Recommendation Systems

Unity

Manhattan, NY • On-site

Full-time

Posted 7 days ago

New

Job description

Job Summary:
Unity's Vector AI team builds machine learning systems for ad targeting across billions of users. They are seeking a Machine Learning Engineer to develop next-generation recommendation systems that leverage advanced techniques such as reinforcement learning and large language models.
Responsibilities:
• Design, build, and evaluate next-generation ranking and recommendation models that incorporate LLMs, RLHF, and preference learning to improve ad relevance and user experience.
• Develop user understanding systems — conversion prediction, behavioral modeling, and value estimation — that operate across billions of impressions.
• Apply reinforcement learning and optimization techniques to bidding strategy, auction dynamics, and real-time ad delivery.
• Design and run rigorous experiments using causal inference, A/B testing, and offline evaluation frameworks to measure and improve model quality.
• Partner with engineering to bring research ideas into production, working across the full pipeline from training data to deployed model.
• Communicate findings clearly to technical and non-technical stakeholders across engineering, product, and business teams.
Qualifications:
Required:
• PhD in Computer Science, Machine Learning, Statistics, or a related field (graduating 2026 or recent graduate).
• Strong research foundations in one or more of: recommendation systems, reinforcement learning, LLM post-training or alignment, human-AI collaboration, probabilistic modeling, or optimization.
• Experience working with large-scale data and ML systems, whether through research or industry internships.
• Fluency in Python; familiarity with ML frameworks such as PyTorch or TensorFlow.
• A track record of rigorous, high-quality research — publications at top venues (NeurIPS, ICML, ICLR, KDD, RecSys, ACL, WWW, or similar) are a strong signal.
• Strong written and verbal communication skills — able to make complex ideas accessible across technical and non-technical audiences.
Preferred:
• Industry experience in ads, recommendation, or user understanding systems (internship experience counts).
• Hands-on experience with production ML pipelines — training at scale, feature engineering, or experimentation infrastructure.
• Experience applying LLMs or generative models to ranking, retrieval, or structured prediction problems.
• Familiarity with agentic AI approaches — multi-step reasoning, tool use, or human-AI collaboration frameworks.
• Exposure to causal inference, uplift modeling, or A/B testing at scale.
• Genuine curiosity about applied research and the drive to see ideas through to impact.
Company:
Unity [NYSE: U] offers a suite of tools to create, market, and grow games and interactive experiences across all major platforms from mobile, PC, and console, to extended reality. Founded in 2004, the company is headquartered in San Francisco, USA, with a team of 5001-10000 employees. The company is currently Late Stage.

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