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Ml Inference Jobs (NOW HIRING)

Apple

ML Software Engineer

Seattle, WA

$142K - $263K/yr

Our team builds ML-inference applications and services on Apple Silicon in the datacenter, specifically focusing in recent years on generative AI as part of the Private Cloud Compute component of ...

PICTOR LABS INC

$89K - $123K/yr

About the Role We are seeking an experienced Senior ML Inference Engineer to join our team, focusing on optimizing and deploying our production virtual staining models at scale. The ideal candidate ...

E-Space

AI / Embedded ML Engineer

Saratoga, CA · On-site

$145K - $190K/yr

... inference latency • Use frameworks including TensorFlow Lite Micro, Edge Impulse, ONNX Runtime, and ExecuTorch • Integrate ML inference into embedded firmware written in C, C++, or Rust • ...

Apple

ML Framework (MetalLM) Engineer

Cupertino, CA

$150K - $225K/yr

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 ...

E-Space

AI / Embedded ML Engineer

Saratoga, CA · On-site

$145K - $190K/yr

... inference latency • Use frameworks including TensorFlow Lite Micro, Edge Impulse, ONNX Runtime, and ExecuTorch • Integrate ML inference into embedded firmware written in C, C++, or Rust • ...

E-Space

AI / Embedded ML Engineer

Saratoga, CA · On-site

$145K - $190K/yr

... inference latency • Use frameworks including TensorFlow Lite Micro, Edge Impulse, ONNX Runtime, and ExecuTorch • Integrate ML inference into embedded firmware written in C, C++, or Rust • ...

E-Space

AI / Embedded ML Engineer

Saratoga, CA · Hybrid

$145K - $190K/yr

... inference latency Use frameworks including TensorFlow Lite Micro, Edge Impulse, ONNX Runtime, and ExecuTorch Integrate ML inference into embedded firmware written in C, C++, or Rust Profile and ...

iTech US, Inc.

AI/ML Engineer (MLops)

Woodbridge, NJ · On-site

Deploying real-time ML inference pipelines processing millions of records at high throughput * Experience building end to end automated MLOps capabilities along with model and feature drift ...

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Ml Inference information

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$37.5K

$122.7K

$196.5K

How much do ml inference jobs pay per year?

As of Aug 6, 2026, the average yearly pay for ml inference in the United States is $122,738.00, according to ZipRecruiter salary data. Most workers in this role earn between $98,500.00 and $136,000.00 per year, depending on experience, location, and employer.

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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Ml Inference jobs near you
Infographic showing various Ml Inference job openings in the United States as of August 2026, with employment types broken down into 93% Full Time, 2% Part Time, and 5% Contract. Highlights an 82% Physical, 4% Hybrid, and 14% Remote job distribution, with an average salary of $122,738 per year, or $59 per hour.

Staff Software Engineer - GenAI inference

Databricks

San Francisco, CA

Other

Re-posted 2 days ago

Job description

P-1285

About This Role

As a staff software engineer for GenAI inference, you will lead the architecture, development, and optimization of the inference engine that powers Databricks Foundation Model API.. You'll bridge research advances and production demands, ensuring high throughput, low latency, and robust scaling. Your work will encompass the full GenAI inference stack: kernels, runtimes, orchestration, memory, and integration with frameworks and orchestration systems.

What You Will Do
  • Own and drive the architecture, design, and implementation of the inference engine, and collaborate on model-serving stack optimized for large-scale LLMs inference
  • Partner closely with researchers to bring new model architectures or features (sparsity, activation compression, mixture-of-experts) into the engine
  • Lead the end-to-end optimization for latency, throughput, memory efficiency, and hardware utilization across GPUs, and accelerators
  • Define and guide standards to build and maintain instrumentation, profiling, and tracing tooling to uncover bottlenecks and guide optimizations
  • Architect scalable routing, batching, scheduling, memory management, and dynamic loading mechanisms for inference workloads
  • Ensure reliability, reproducibility, and fault tolerance in the inference pipelines, including A/B launches, rollback, and model versioning
  • Collaborate cross-functionally on Integrating with federated, distributed inference infrastructure - orchestrate across nodes, balance load, handle communication overhead
  • Drive cross-team collaboration: with platform engineers, cloud infrastructure, and security/compliance teams
  • Represent the team externally through benchmarks, whitepapers, and open-source contributions
What We Look For
  • BS/MS/PhD in Computer Science, or a related field
  • Strong software engineering background (6+ years or equivalent) in performance-critical systems
  • Proven track record of owning complex system components and driving architectural decisions end-to-end
  • Deep understanding of ML inference internals: attention, MLPs, recurrent modules, quantization, sparse operations, etc.
  • Hands-on experience with CUDA, GPU programming, and key libraries (cuBLAS, cuDNN, NCCL, etc.)
  • Strong background in distributed systems design, including RPC frameworks, queuing, RPC batching, sharding, memory partitioning
  • Demonstrated ability to uncover and solve performance bottlenecks across layers (kernel, memory, networking, scheduler)
  • Experience building instrumentation, tracing, and profiling tools for ML models
  • Ability to lead through influence - work closely with ML researchers, translate novel model ideas into production systems
  • Excellent communication and leadership skills, with a proactive and ownership-driven mindset
  • Bonus: published research or open-source contributions in ML systems, inference optimization, or model serving

Apply