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Ml Inference Jobs (NOW HIRING)

Xforia, Inc.

ML engineer

Optimize trained models for inference, ensuring they are ready for deployment. * Work closely with ... Serve as domain experts in ML inference, working closely with both internal teams and external ...

Reveille Technologies

ML Ops Engineer

Jersey City, NJ · On-site

ML Ops Engineer Location: Iselin NJ Experience Required Experience building production Al/ML systems at scale Deploying real-time ML inference pipelines processing millions of records at high ...

Third Way Health

AI / ML Engineer

Cambridge, MA · On-site

Develop predictive, real-time analytics systems that combine streaming data, ML inference, and event-driven triggers to surface insights and automate actions at scale. * Implement and maintain end-to ...

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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 Jul 16, 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 a $900000 AI job?

A $900,000 AI job typically refers to high-level roles in artificial intelligence, such as senior machine learning engineers or AI research directors, often involving advanced skills in deep learning, data modeling, and programming with tools like Python and TensorFlow. These positions usually require extensive experience, specialized knowledge, and may include leadership responsibilities or strategic decision-making.

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 engineer makes $500,000 a year?

Senior machine learning engineers with extensive experience, advanced skills in deep learning, and expertise in deploying large-scale models can earn salaries approaching or exceeding $500,000 annually, especially in high-cost-of-living areas or top tech companies. Compensation often includes base salary, bonuses, and stock options, reflecting their specialized knowledge and impact on product development.

Which 3 jobs will survive AI?

Jobs involving Ml Inference, such as data scientists, machine learning engineers, and AI system architects, are likely to persist as they require specialized expertise in developing, deploying, and maintaining AI models. These roles demand critical thinking, domain knowledge, and skills in programming and data analysis that are less easily automated. Continuous learning and staying updated with AI tools and frameworks are essential for these professions to remain relevant.

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.

Will MLE be replaced by AI?

Machine Learning Engineers (MLEs) design, develop, and optimize AI models and systems. While AI automation tools can assist with certain tasks, MLEs are essential for building, tuning, and maintaining complex models, making complete replacement unlikely in the near term. Their expertise in data handling, model deployment, and system integration remains critical in AI development environments.

What are the key skills and qualifications needed to thrive in ML Inference, and why are they important?

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.
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Ml Inference jobs near you
Infographic showing various Ml Inference job openings in the United States as of July 2026, with employment types broken down into 81% Full Time, 18% Part Time, and 1% Contract. Highlights an 71% Physical, 2% Hybrid, and 27% Remote job distribution, with an average salary of $122,738 per year, or $59 per hour.
Manager, Software Engineering, ML Inference

Manager, Software Engineering, ML Inference

Snap, Inc.

Palo Alto, CA • On-site

Full-time

Medical

Posted 15 days ago

Job description

Snap Inc is a technology company. We believe the camera presents the greatest opportunity to improve the way people live and communicate. Snap contributes to human progress by empowering people to express themselves, live in the moment, learn about the world, and have fun together.


The Company operates Snapchat, a visual messaging app that enhances your relationships with friends, family, and the world, and Specs Inc., a wholly-owned subsidiary dedicated to making computing more human, in addition to Bitmoji, Saturn, and other digital services.


Snap Engineering teams build fun and technically sophisticated products that reach hundreds of millions of Snapchatters around the world, every day. We're deeply committed to the well-being of everyone in our global community, which is why our values are at the root of everything we do. We move fast, with precision, and always execute with privacy at the forefront.

We're looking for a Manager, Software Engineering, ML Inference to join Snap Inc.!

What you'll do:

  • Lead and mentor a team of ML Infrastructure engineers responsible for building and scaling the systems that power Snap's model training, inference, and data pipelines

  • Set the strategy, build a roadmap, create measurable goals, and lead your team to deliver high-impact ML infrastructure initiatives

  • Evaluate the technical tradeoffs of key decisions and serve as a strong technical mentor across the team

  • Perform design and code reviews to continuously raise the technical excellence bar

  • Collaborate with ML engineers, product teams, and cross-functional stakeholders to understand requirements, evaluate tradeoffs, and deliver solutions at scale

  • Hire, grow, and retain high-performing engineers by creating growth opportunities, giving regular feedback, and managing performance

  • Advocate for and apply best practices when it comes to availability, scalability, operational excellence, and cost management

  • Utilize AI tools and high velocity engineering workflows to design and ship scalable services while upholding rigorous standards for code correctness, security, and production-ready quality

Knowledge, Skills & Abilities:

  • Strong understanding of ML infrastructure systems including model training platforms, inference serving, feature stores, and data pipelines

  • Background building high availability, mission-critical systems at significant scale

  • Experience setting technical direction for teams whose work directly enables ML engineers and production ML systems

  • Strong management and mentorship skills, with the ability to lead and grow senior engineers

  • Excellent verbal and written communication skills, with high attention to detail

  • Ability to collaborate with internal and external stakeholders at all levels

  • Skilled at managing ambiguous problems and driving clarity across complex, multi-team initiatives

  • Proficiency in, or a strong aptitude for, leveraging AI tools to streamline development, paired with the critical judgment to audit generated output for architectural integrity, performance bottlenecks, and security risks

  • Adaptability in learning and applying evolving AI systems and tools to remain at the forefront of engineering trends and modern development practices

Minimum Qualifications:

  • Bachelor's degree in a technical field such as computer science or equivalent years of experience

  • 9+ years of post-Bachelor's software engineering experience; or a Master's degree in a technical field + 8+ years of post-grad experience; or a PhD in a related technical field + 5+ years of post-grad experience

  • 1+ year(s) of experience managing an engineering team

  • Experience with distributed systems and large-scale ML infrastructure

Preferred Qualifications:

  • Advanced degree in a related technical field

  • Experience working with ML training platforms, inference infrastructure, or feature serving systems

  • Familiarity with ML frameworks such as TensorFlow, PyTorch, Caffe2, Spark ML, or related frameworks

  • Experience with Spark, Flink, Ray, or other big data processing technologies

  • Experience with key infrastructure technologies including Kubernetes, NoSQL, Memcache/Redis, Kafka, Google Cloud, or AWS services

  • Track record of delivery in rapidly changing, highly collaborative, multi-stakeholder environments

  • Experience with MLOps and managing production machine learning lifecycle

If you have a disability or special need that requires accommodation, please don't be shy and provide us some information.

"Default Together" Policy at Snap: At Snap Inc. we believe that being together in person helps us build our culture faster, reinforce our values, and serve our community, customers and partners better through dynamic collaboration. To reflect this, we practice a "default together" approach and expect our team members to work in an office 4+ days per week.

At Snap, we believe that having a team of diverse backgrounds and voices working together will enable us to create innovative products that improve the way people live and communicate. Snap is proud to be an equal opportunity employer, and committed to providing employment opportunities regardless of race, religious creed, color, national origin, ancestry, physical disability, mental disability, medical condition, genetic information, marital status, sex, gender, gender identity, gender expression, pregnancy, childbirth and breastfeeding, age, sexual orientation, military or veteran status, or any other protected classification, in accordance with applicable federal, state, and local laws. EOE, including disability/vets.

We are an Equal Opportunity Employer and will consider qualified applicants with criminal histories in a manner consistent with applicable law (by example, the requirements of the San Francisco Fair Chance Ordinance and the Los Angeles Fair Chance Initiative for Hiring, where applicable).

Our Benefits: Snap Inc. is its own community, so we've got your back! We do our best to make sure you and your loved ones have everything you need to be happy and healthy, on your own terms. Our benefits are built around your needs and include paid parental leave, comprehensive medical coverage, emotional and mental health support programs, and compensation packages that let you share in Snap's long-term success!

Compensation

In the United States, work locations are assigned a pay zone which determines the salary range for the position. The successful candidate's starting pay will be determined based on job-related skills, experience, qualifications, work location, and market conditions. The starting pay may be negotiable within the salary range for the position. These pay zones may be modified in the future.

Zone A (CA, WA, NYC):

The base salary range for this position is $229,000-$343,000 annually.


Zone B:

The base salary range for this position is $218,000-$326,000 annually.

Zone C:

The base salary range for this position is $195,000-$292,000 annually.This position is eligible for equity in the form of RSUs.

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