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Ml Inference Jobs in California (NOW HIRING)

Apple

ML Framework (MetalLM) Engineer

Cupertino, CA · On-site

Work on cutting-edge ML inference framework project and optimize code for efficient and scalable ML inference using distributed compute strategies such as data, tensor, pipeline and expert ...

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Ml Inference information

What is ML inference?

ML inference refers to the process of using a trained machine learning model to make predictions or decisions based on new data. After a model has been trained on historical data, inference is the phase where that model is deployed and used in real-world applications, such as recognizing speech, detecting objects in images, or recommending products. The focus in ML inference is on speed, efficiency, and scalability to ensure quick predictions, often in real time. This process is critical for practical applications like mobile apps, web services, and embedded systems. Optimizing inference involves reducing latency, memory usage, and computational requirements.

What is the difference between Ml Inference vs Data Scientist?

AspectML InferenceData Scientist
Required CredentialsKnowledge of machine learning models, programming skillsDegree in data science, statistics, or related fields
Work EnvironmentDeploying models in production, real-time data processingData analysis, model development, research
Industry UsageAI product deployment, software companiesResearch institutions, tech firms, consulting

ML Inference focuses on deploying trained models to make predictions on new data, often in real-time. Data Scientists develop and analyze models, working primarily in research and development. While both roles require understanding of machine learning, ML Inference emphasizes deployment and operationalization, whereas Data Scientists focus on model creation and analysis.

What are some common challenges faced by ML inference engineers when deploying models to production?

ML Inference Engineers often encounter challenges such as optimizing model latency and throughput to meet production requirements, ensuring compatibility with diverse hardware environments, and managing model versioning and updates without disrupting service. Additionally, balancing resource utilization and inference accuracy while monitoring real-time performance metrics is crucial. Collaboration with data scientists, DevOps, and software engineers is typically essential to streamline deployment and maintain robust, scalable inference pipelines.

What are the key skills and qualifications needed to thrive in ML inference?

To thrive in ML Inference, you need a solid background in machine learning principles, programming (Python or C++), and experience with deploying models at scale, often supported by a degree in computer science or a related field. Familiarity with frameworks and tools such as TensorFlow, PyTorch, ONNX, and cloud platforms like AWS SageMaker or Google AI Platform is typically required. Strong problem-solving skills, attention to detail, and effective communication are crucial soft skills for collaborating with multidisciplinary teams and optimizing model performance. These skills ensure efficient, scalable, and reliable deployment of machine learning solutions in real-world applications.

Is ML inference a high paying job?

ML inference roles are generally well-paying, especially for those with skills in machine learning frameworks, programming, and cloud platforms. Salaries vary based on experience, location, and industry, but they tend to be higher than average for tech-related positions.

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What job categories do people searching Ml Inference jobs in California look for?

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What cities in California are hiring for Ml Inference jobs?

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Ml Inference jobs near you
Infographic showing various Ml Inference job openings in California as of August 2026, with employment types broken down into 90% Full Time, 4% Part Time, 2% Temporary, and 4% Contract. Highlights an 80% Physical, 6% Hybrid, and 14% Remote job distribution.

Distributed LLM Inference Engineer

Anyscale

San Francisco, CA • On-site

Full-time

Re-posted 12 days ago

Job description

Job Summary:
Anyscale is on a mission to democratize distributed computing and make it accessible to software developers. The Distributed LLM Inference Engineer will optimize systems for large-scale ML inference, working closely with product teams and the open-source community to deliver high-performance solutions.
Responsibilities:
• Iterate very quickly with product teams to ship the end to end solutions for Batch and Online inference at high scale which will be used by open-source Ray users and customers of Anyscale
• Work across the stack integrating Ray Data and LLM engine providing optimizations achieving low cost solutions for large scale ML inference
• Integrate with Open source software like vLLM, work closely with the community to adopt these techniques in Anyscale solutions, and also contribute improvements to open source
• Follow the latest state-of-the-art in the open source and the research community, implementing and extending best practices
Qualifications:
Required:
• Familiarity with running ML inference at large scale with high throughput and low latency
• Familiarity with deep learning and deep learning frameworks (e.g. PyTorch)
• Solid understanding of distributed systems, ML inference challenges
Preferred:
• ML Systems knowledge
• Experience using Ray
• Work closely with community on LLM engines like vLLM, TensorRT-LLM
• Contributions to deep learning frameworks (PyTorch, TensorFlow)
• Contributions to deep learning compilers (Triton, TVM, MLIR)
• Prior experience working on GPUs / CUDA
Company:
Anyscale develops a distributed computing platform that enables organizations to manage distributed workloads at scale. Founded in 2019, the company is headquartered in San Francisco, USA, with a team of 201-500 employees. The company is currently Growth Stage.

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