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Ml Inference Jobs (NOW HIRING)

eTeam

Machine Learning Platform Engineer

Richardson, TX · On-site

Responsibilities : • Should have 7 years of experience with a strong foundation in ML inference, deployment, and quality validation. Should be capable of end-to-end ownership from model deployment ...

Jobgether

$88K - $106K/yr

You will combine deep ML expertise, systems engineering, and performance analysis to deliver faster, more efficient AI experiences. * Optimize machine learning inference systems to improve latency ...

SimpliSafe

Staff Embedded ML Engineer, Edge AI

Boston, MA

$142K - $187K/yr

As a key contributor, you will lead the on-device inference and performance optimization of ML models powering outdoor monitoring in the home security space. This role is less about inventing new CV ...

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

$150K - $225K/yr

Software Embedding and Systems Integration ◦ Write clean, well-tested embedded software that integrates ML inference into real-time systems ◦ Work with RTOS environments such as FreeRTOS and ...

SimpliSafe

Staff Embedded ML Engineer, Edge AI

Boston, MA

$142K - $187K/yr

As a key contributor, you will lead the on-device inference and performance optimization of ML models powering outdoor monitoring in the home security space. This role is less about inventing new CV ...

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 • ...

SimpliSafe

Staff Embedded ML Engineer, Edge AI

Boston, MA · On-site

$142K - $187K/yr

As a key contributor, you will lead the on-device inference and performance optimization of ML models powering outdoor monitoring in the home security space. This role is less about inventing new CV ...

Mimecast

Senior ML Ops Engineer

Columbus, OH · On-site

$100K - $138K/yr

Own the reliability and scalability of ML inference infrastructure. Design and tune autoscaling policies against real production traffic patterns, implement rate limiting and backpressure mechanisms ...

Mimecast

Senior ML Ops Engineer

Columbus, OH

$100K - $138K/yr

Own the reliability and scalability of ML inference infrastructure. Design and tune autoscaling policies against real production traffic patterns, implement rate limiting and backpressure mechanisms ...

Co-Sourcing Partners

GCP AI/ML Engineer

Chicago, IL · On-site

Design automated workflows for data ingestion, feature engineering, model training, evaluation, and inference. * Orchestrate ML workflows using Python, Vertex AI, BigQuery, and Cloud Storage.

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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.
More about Ml Inference jobs
What cities are hiring for Ml Inference jobs? Cities with the most Ml Inference job openings:
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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.

Machine Learning Platform Engineer

eTeam

Richardson, TX • On-site

Full-time

Re-posted 25 days ago

Job description

Job Summary:
eTeam is seeking a Machine Learning Platform Engineer to work onsite in Richardson, TX. The role requires strong expertise in ML inference, deployment, and quality validation, with responsibilities including end-to-end ownership from model deployment to user impact.
Responsibilities:
• Should have 7 years of experience with a strong foundation in ML inference, deployment, and quality validation. Should be capable of end-to-end ownership from model deployment to user impact, with the ability to quickly adapt to new technologies.
• Should have strong expertise in ML benchmarking and collaboration, along with hands-on experience deploying models on cloud platforms, preferably GCP. Familiarity with Java/JVM-based systems for model integration, streaming data architectures, and hybrid (on-prem and cloud) environments is essential.
• Must possess solid system design and distributed systems knowledge for troubleshooting, and hands-on experience with ML frameworks such as TensorFlow, PyTorch, or JAX.
Qualifications:
Required:
• 7 years of experience with a strong foundation in ML inference, deployment, and quality validation.
• Capability of end-to-end ownership from model deployment to user impact.
• Ability to quickly adapt to new technologies.
• Strong expertise in ML benchmarking and collaboration.
• Hands-on experience deploying models on cloud platforms, preferably GCP.
• Familiarity with Java/JVM-based systems for model integration.
• Experience with streaming data architectures.
• Experience in hybrid (on-prem and cloud) environments.
• Solid system design and distributed systems knowledge for troubleshooting.
• Hands-on experience with ML frameworks such as TensorFlow, PyTorch, or JAX.
Company:
eTeam is a staffing agency that also provides payrolling services. Founded in 1999, the company is headquartered in Somerset, USA, with a team of 501-1000 employees. The company is currently Late Stage.

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