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Facebook Machine Learning Jobs (NOW HIRING)

Databricks

Sr. Machine Learning Engineer

San Francisco, CA · On-site

$164K - $205K/yr

What We're Looking For * 2-8 years of machine learning engineering experience in high-velocity ... To learn more, follow Databricks on Twitter, LinkedIn and Facebook. Benefits At Databricks, we ...

Databricks

Sr. Machine Learning Engineer

San Francisco, CA · On-site

$164K - $205K/yr

What We're Looking For * 2-8 years of machine learning engineering experience in high-velocity ... To learn more, follow Databricks on Twitter, LinkedIn and Facebook. Benefits At Databricks, we ...

Meta

Software Engineer, Machine Learning

Sunnyvale, CA · On-site

$183K - $257K/yr

When Facebook launched in 2004, it changed the way people connect. Apps like Messenger, Instagram and WhatsApp further empowered billions around the world. Now, Meta is moving beyond 2D screens ...

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How much do facebook machine learning jobs pay per hour?

As of Jun 4, 2026, the average hourly pay for facebook machine learning in the United States is $21.33, according to ZipRecruiter salary data. Most workers in this role earn between $18.75 and $22.84 per hour, depending on experience, location, and employer.

What are the key skills and qualifications needed to thrive as a Facebook Machine Learning Engineer, and why are they important?

To thrive as a Facebook Machine Learning Engineer, you need a strong background in computer science, statistics, and machine learning, usually backed by a relevant degree and experience in large-scale data analysis. Proficiency in Python, C++, TensorFlow or PyTorch, and experience with distributed computing systems are typically required. Strong problem-solving skills, collaboration, and effective communication help you work cross-functionally and drive innovative solutions. These skills enable the rapid development and deployment of impactful machine learning models at scale, which is crucial for Facebook's data-driven products and services.

How does a Facebook Machine Learning Engineer typically collaborate with product teams to deploy models into production?

As a Facebook Machine Learning Engineer, you will work closely with product managers, software engineers, and data scientists to integrate machine learning solutions into real-world products. This often involves participating in cross-functional meetings to understand product requirements, iterating on model prototypes, and ensuring smooth deployment and monitoring of models in production. Collaboration is key, as you will need to communicate technical insights to non-technical stakeholders and incorporate feedback to improve model performance. This dynamic environment provides opportunities to learn from experts across multiple domains and contribute directly to impactful, large-scale products.

What does a Facebook Machine Learning Engineer do?

A Facebook Machine Learning Engineer designs, builds, and deploys artificial intelligence models that power various features and products across Meta's platforms, such as Facebook, Instagram, and WhatsApp. Their work involves data preprocessing, model selection, training, evaluation, and optimization to improve user experiences like content recommendations, spam detection, and ad targeting. They also collaborate with product managers, researchers, and software engineers to integrate these models into scalable systems. The role requires strong programming skills, knowledge of machine learning algorithms, and experience with large-scale data processing.

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

AspectFacebook Machine LearningData Scientist
Required CredentialsDegree in Computer Science, Data Science, or related fields; experience with ML frameworksDegree in Statistics, Mathematics, Computer Science, or related fields; strong analytical skills
Work EnvironmentTech company, collaborative teams, focus on ML models and algorithmsVaried industries, data analysis, reporting, and insights generation
Employer & Industry UsagePrimarily in tech companies like Facebook, focusing on AI/ML productsAcross industries including tech, finance, healthcare, focusing on data analysis

Facebook Machine Learning specialists focus on developing and deploying machine learning models within Facebook's infrastructure, requiring strong programming and ML skills. Data Scientists analyze data to generate insights, often using statistical methods. While both roles require a background in data or computer science, Facebook Machine Learning roles are more technical and model-focused, whereas Data Scientists emphasize data analysis and interpretation.

More about Facebook Machine Learning jobs
What job categories do people searching Facebook Machine Learning jobs look for? The top searched job categories for Facebook Machine Learning jobs are:
Facebook Machine Learning jobs near you
Infographic showing various Facebook Machine Learning job openings in the United States as of May 2026, with employment types broken down into 13% As Needed, 61% Full Time, 13% Part Time, and 13% Contract. Highlights an 94% Physical, 1% Hybrid, and 5% Remote job distribution, with an average salary of $44,363 per year, or $21.3 per hour.

AI Staff Machine Learning Engineer -Gen AI,Machine Learning,Graph ML,Big Data(10030)

Extreme Networks

San Jose, CA • On-site

$170K - $240K/yr

Full-time

Posted 29 days ago

Job description

Over 50,000 customers globally trust our end-to-end, cloud-driven networking solutions. They rely on our top-rated services and support to accelerate their digital transformation efforts and deliver unprecedented progress. With double-digit growth year over year, no provider is better positioned to deliver scalable outcomes than Extreme.

Inclusion is one of our core values and in our DNA. We are committed to fostering an inclusive workplace that embraces our differences and creates an atmosphere where all our employees thrive because of their differences, not in spite of them.

Become part of Something big with Extreme! As a global networking leader, learn why there's no better time to join the Extreme team.

Position : AI Staff Machine Learning Engineer -Gen AI,Machine Learning,Graph ML,Big Data
Experience : 5 to 14 Years
Hybrid/Remote
 
Our AI Core group is pioneering platforms and solutions for Generative AI, including AI Agents, RAG, Knowledge Bases, Data Mining, Anomaly Detection, and LLM fine-tuning. These innovations power flagship Extreme products while enabling entirely new offerings. Together, we are driving a fundamental shift in how businesses manage networks by building intelligent, high-performance multi-agent systems that perceive, learn, and act in real time. At Extreme, innovation is not just encouraged, it is expected. Advance with us and help shape the future of network intelligence. 
About the position
  • Be a thought leader and forward thinker, help drive an innovative vision for our various products and platforms, design and launch strategic machine learning (ML) solutions and drive business-wide innovation.
  • Take the lead in the end-to-end software development lifecycle, encompassing design, testing, deployment, and operations, lead technical discussions and strategy, and participate hands-on in design reviews, code reviews, and implementation.
  • Craft high-performance, high-scale microservices architectures, including synchronous and asynchronous web services.
  • Develop real-time online inferencing for highly complex models using Triton, TensorRT and mixed precision computing.
  • Mentor and develop other engineers on the team, establish technical direction and foster team culture.
  • Uphold the highest standards of technical rigor in engineering and operational excellence, build highly resilient and scalable systems, and champion operational and process improvements.
Basic Qualifications:
  • Degree in mathematics/computer science or related discipline.
  • 5 to 10 years of experience in the complete software development lifecycle including design, coding, code reviews, testing, build processes, deployments and operations.
  • 5 to 10 years of experience in Python with an in-depth knowledge of its advanced features and libraries.
  • Expertise in designing RESTful APIs with hands-on experience with technologies such as FastAPI.
  • Proficient in Docker, Kubernetes, and modern CI/CD practices.
  • 3+ years of experience in leading the design and architecture of large distributed systems preferably on cloud platforms (e.g., AWS, Azure, Google Cloud).
  • Experience as a mentor, tech lead or leading an engineering team.
Preferred Qualifications:
  • MS or PhD in Computer Science or equivalent experience in ML.
  • Experience working with ML technologies (PyTorch, Sagemaker, Triton, TensorRT, etc.).
  • Experience with NoSQL and document databases.
  • Proven ability to handle big data, optimize workflows, and improve system performance.
  • Come work with a team of highly talented engineers, and advance with us to achieve new heights every day!
 
  • Salary based on qualifications, experience and region up to USD 170 k to 240 K plus benefits.
$170,000 - $240,000 a year
Extreme Networks, Inc. (EXTR) creates effortless networking experiences that enable all of us to advance. We push the boundaries of technology leveraging the powers of machine learning, artificial intelligence, analytics, and automation. Over 50,000 customers globally trust our end-to-end, cloud-driven networking solutions and rely on our top-rated services and support to accelerate their digital transformation efforts and deliver progress like never before. For more information, visit Extreme's website or follow us on Twitter, LinkedIn, and Facebook.

We encourage people from underrepresented groups to apply. Come Advance with us! In keeping with our values, no employee or applicant will face discrimination/harassment based on: race, color, ancestry, national origin, religion, age, gender, marital domestic partner status, sexual orientation, gender identity, disability status, or veteran status. Above and beyond discrimination/harassment based on "protected categories," Extreme Networks also strives to prevent other, subtler forms of inappropriate behavior (e.g., stereotyping) from ever gaining a foothold in our organization. Whether blatant or hidden, barriers to success have no place at Extreme Networks.
We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.
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