$101K - $138K/yr
Contribute to model deployment, inference services, and production monitoring workflows * Improve data quality, lineage, provenance, and operational transparency across ML pipelines * Contribute to ...
$101K - $138K/yr
Contribute to model deployment, inference services, and production monitoring workflows * Improve data quality, lineage, provenance, and operational transparency across ML pipelines * Contribute to ...
Lead ML Ops Engineer
Milwaukee, WI · On-site
$101K - $133K/yr
... training, inference, evaluation, monitoring, retraining, and governance, including generative AI ... Leadership-level expertise in AI/ML platform engineering, spanning MLOps, LLMOps, and AIOps.
Lead ML Ops Engineer
Milwaukee, WI · On-site
$101K - $133K/yr
... training, inference, evaluation, monitoring, retraining, and governance, including generative AI ... Leadership-level expertise in AI/ML platform engineering, spanning MLOps, LLMOps, and AIOps.
Lead ML Ops Engineer
$101K - $133K/yr
Oversee enterprisescale AI platforms supporting model training, inference, evaluation, monitoring ... Leadershiplevel expertise in AI/ML platform engineering, spanning MLOps, LLMOps, and AIOps.
Lead ML Ops Engineer
$101K - $133K/yr
Oversee enterprisescale AI platforms supporting model training, inference, evaluation, monitoring ... Leadershiplevel expertise in AI/ML platform engineering, spanning MLOps, LLMOps, and AIOps.
Lead ML Ops Engineer
$96K - $126K/yr
Oversee enterprisescale AI platforms supporting model training, inference, evaluation, monitoring ... Leadershiplevel expertise in AI/ML platform engineering, spanning MLOps, LLMOps, and AIOps.
Lead ML Ops Engineer
$96K - $126K/yr
Oversee enterprisescale AI platforms supporting model training, inference, evaluation, monitoring ... Leadershiplevel expertise in AI/ML platform engineering, spanning MLOps, LLMOps, and AIOps.
Build scalable production ready ML ops pipeline and inference endpoints using AWS technologies e.g. SageMaker, Bedrock. Collaborate with Android developers to integrate AI features into client-side ...
Build scalable production ready ML ops pipeline and inference endpoints using AWS technologies e.g. SageMaker, Bedrock. Collaborate with Android developers to integrate AI features into client-side ...
Build scalable production ready ML ops pipeline and inference endpoints using AWS technologies e.g. SageMaker, Bedrock. Collaborate with Android developers to integrate AI features into client-side ...
Build scalable production ready ML ops pipeline and inference endpoints using AWS technologies e.g. SageMaker, Bedrock. Collaborate with Android developers to integrate AI features into client-side ...
Build scalable production ready ML ops pipeline and inference endpoints using AWS technologies e.g. SageMaker, Bedrock. Collaborate with Android developers to integrate AI features into client-side ...
Build scalable production ready ML ops pipeline and inference endpoints using AWS technologies e.g. SageMaker, Bedrock. Collaborate with Android developers to integrate AI features into client-side ...
... ML ops pipeline and inference endpoints using AWS technologies e.g. SageMaker, Bedrock. · Collaborate with Android developers to integrate AI features into client-side applications. · Create ...
Quick apply
... ML ops pipeline and inference endpoints using AWS technologies e.g. SageMaker, Bedrock. · Collaborate with Android developers to integrate AI features into client-side applications. · Create ...
... ML ops pipeline and inference endpoints using AWS technologies e.g. SageMaker, Bedrock. • Collaborate with Android developers to integrate AI features into client-side applications. • Create ...
... ML ops pipeline and inference endpoints using AWS technologies e.g. SageMaker, Bedrock. • Collaborate with Android developers to integrate AI features into client-side applications. • Create ...
Builds and operates scalable, secure ML infrastructure on cloud and container platforms (e.g., AWS/Azure/GCP, Docker, Kubernetes) to support batch and realtime inference workloads. * Implements CI/CD ...
Builds and operates scalable, secure ML infrastructure on cloud and container platforms (e.g., AWS/Azure/GCP, Docker, Kubernetes) to support batch and realtime inference workloads. * Implements CI/CD ...
Builds and operates scalable, secure ML infrastructure on cloud and container platforms (e.g., AWS/Azure/GCP, Docker, Kubernetes) to support batch and real‑time inference workloads. * Implements CI ...
Builds and operates scalable, secure ML infrastructure on cloud and container platforms (e.g., AWS/Azure/GCP, Docker, Kubernetes) to support batch and real‑time inference workloads. * Implements CI ...
Sr. Software Engineer
Glendale, WI · On-site
$114K - $150K/yr
This Senior AI/ML Engineer role sits at the center of that transformation. You will do two things ... Build and maintain data pipelines, model integration layers, and inference infrastructure for real ...
New
Sr. Software Engineer
Glendale, WI · On-site
$114K - $150K/yr
This Senior AI/ML Engineer role sits at the center of that transformation. You will do two things ... Build and maintain data pipelines, model integration layers, and inference infrastructure for real ...
New
Some of the core areas of focus for our team include pricing, online advertising, uplift and long term value modeling, and general causal inference. Search & Discovery ML : The Search and Discovery ...
Some of the core areas of focus for our team include pricing, online advertising, uplift and long term value modeling, and general causal inference. Search & Discovery ML : The Search and Discovery ...
Some of the core areas of focus for our team include pricing, online advertising, uplift and long term value modeling, and general causal inference. Search & Discovery ML : The Search and Discovery ...
Some of the core areas of focus for our team include pricing, online advertising, uplift and long term value modeling, and general causal inference. Search & Discovery ML : The Search and Discovery ...
Some of the core areas of focus for our team include pricing, online advertising, uplift and long term value modeling, and general causal inference. Search & Discovery ML : The Search and Discovery ...
Some of the core areas of focus for our team include pricing, online advertising, uplift and long term value modeling, and general causal inference. Search & Discovery ML : The Search and Discovery ...
Some of the core areas of focus for our team include pricing, online advertising, uplift and long term value modeling, and general causal inference. Search & Discovery ML : The Search and Discovery ...
Some of the core areas of focus for our team include pricing, online advertising, uplift and long term value modeling, and general causal inference. Search & Discovery ML : The Search and Discovery ...
Some of the core areas of focus for our team include pricing, online advertising, uplift and long term value modeling, and general causal inference. Search & Discovery ML : The Search and Discovery ...
Some of the core areas of focus for our team include pricing, online advertising, uplift and long term value modeling, and general causal inference. Search & Discovery ML : The Search and Discovery ...
Some of the core areas of focus for our team include pricing, online advertising, uplift and long term value modeling, and general causal inference. Search & Discovery ML : The Search and Discovery ...
Some of the core areas of focus for our team include pricing, online advertising, uplift and long term value modeling, and general causal inference. Search & Discovery ML : The Search and Discovery ...
Some of the core areas of focus for our team include pricing, online advertising, uplift and long term value modeling, and general causal inference. Search & Discovery ML : The Search and Discovery ...
Some of the core areas of focus for our team include pricing, online advertising, uplift and long term value modeling, and general causal inference. Search & Discovery ML : The Search and Discovery ...
Technology Architect
Brookfield, WI · On-site
Experience integrating AI/ML capabilities into enterprise applications (model inference, AI services, decisioning systems) * Strong focus on scalability, resiliency, and high availability , including ...
Technology Architect
Brookfield, WI · On-site
Experience integrating AI/ML capabilities into enterprise applications (model inference, AI services, decisioning systems) * Strong focus on scalability, resiliency, and high availability , including ...
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?
| Aspect | ML Inference | Data Scientist |
|---|---|---|
| Required Credentials | Knowledge of machine learning models, programming skills | Degree in data science, statistics, or related fields |
| Work Environment | Deploying models in production, real-time data processing | Data analysis, model development, research |
| Industry Usage | AI product deployment, software companies | Research 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.
Which 3 jobs will survive AI?
For ML Inference roles, jobs that require complex problem-solving, creativity, and emotional intelligence are more likely to persist, such as data scientists, AI ethics specialists, and machine learning engineers. These roles involve tasks that are difficult to automate and often require specialized skills, domain knowledge, and critical thinking. Continuous learning and expertise in AI tools and programming languages like Python or TensorFlow can also enhance job security in this field.
What engineers make $500,000?
Senior machine learning engineers with extensive experience, specialized skills in deep learning, and strong industry demand can earn $500,000 or more annually, especially in high-cost-of-living areas or within top tech companies. Achieving this level typically requires advanced degrees, certifications, and a proven track record of impactful projects.
What is a $900,000 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 requiring advanced skills in deep learning, data science, and experience with tools like TensorFlow or PyTorch. These positions usually involve leadership responsibilities, strategic planning, and may require multiple years of specialized experience or advanced degrees.
Is ML a high paying job?
Machine Learning (ML) inference roles are generally well-paid due to the specialized skills required, such as knowledge of algorithms, programming, and data analysis. Salaries vary based on experience, location, and industry, but they tend to be higher than average for tech positions. Advanced roles often require proficiency with tools like TensorFlow or PyTorch and may include certifications or advanced degrees.
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, 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.
What are popular job titles related to Ml Inference jobs in Wisconsin? For Ml Inference jobs in Wisconsin, the most frequently searched job titles are:
What job categories do people searching Ml Inference jobs in Wisconsin look for? The top searched job categories for Ml Inference jobs in Wisconsin are:
What cities in Wisconsin are hiring for Ml Inference jobs? Cities in Wisconsin with the most Ml Inference job openings:
$101K - $138K/yr
Full-time
This job post has expired today. Applications are no longer accepted.
Autodesk rating
9.5
Based on 5 frontline employees who took The Breakroom Quiz
7th of 191 rated software companies
Job description
Job Requisition ID #
POSITION OVERVIEW
The work we do at Autodesk touches nearly every person on the planet. By creating software tools for making buildings, machines, and even the latest movies, we influence and empower some of the most creative people in the world to solve problems that matter.
Autodesk is seeking a Senior ML Engineer, ML Systems and Infrastructure to design and scale the systems that enable machine learning across research and product development. You will help build the infrastructure behind large-scale data pipelines, distributed training systems, evaluation frameworks, and production ML workflows that support foundation models and ML-powered product features.
This role is ideal for an engineer who is deeply interested in scalable systems and production-grade ML infrastructure. You will operate independently across multiple parts of the stack and help define strong engineering practices for reliability, performance, and maintainability.
This role is fully remote-friendly, with team members distributed across the US and Canada.
Location: US or Canada Remote, East Coast
RESPONSIBILITIES
Design and build scalable systems for ML training, evaluation, deployment, and monitoring
Develop and improve data pipelines that process large-scale structured and semi-structured technical datasets
Optimize distributed workflows for performance, reliability, resource utilization, and cost efficiency
Build platform capabilities such as experiment tracking, model versioning, checkpointing, reproducibility, and observability
Contribute to model deployment, inference services, and production monitoring workflows
Improve data quality, lineage, provenance, and operational transparency across ML pipelines
Contribute to architecture and design discussions across the team
Identify and resolve bottlenecks in data, compute, orchestration, and observability layers
Mentor engineers through code reviews, design guidance, and knowledge sharing
Collaborate closely with researchers, product engineers, and platform partners to turn ML workflows into robust engineering systems
MINIMUM QUALIFICATIONS
Bachelor's or Master's degree in Computer Science, Engineering, or a related field, or equivalent industry experience
At least 3 to 4 years of industry experience building and operating production software, ML systems, distributed infrastructure, or large-scale data pipelines
Strong experience in software engineering, distributed systems, backend systems, or ML infrastructure
Strong proficiency in Python and experience delivering production-quality systems
Experience designing and operating scalable data or compute pipelines
Experience with cloud platforms such as AWS, Azure, or GCP
Familiarity with containers, CI/CD, observability, and release quality practices
Ability to independently drive technical execution on complex work with limited oversight
PREFERRED QUALIFICATIONS
Experience building data pipelines for large-scale structured and semi-structured technical datasets
Experience with data lineage, provenance, governance, and responsible data usage in ML systems
Experience with distributed data processing and orchestration systems such as Ray, Airflow, Spark, or similar platforms
Experience with model deployment, inference services, monitoring, and observability for production ML systems
Experience building ML-ready representations for geometry, graph, hierarchical, or multimodal data
Experience with distributed ML frameworks such as PyTorch, Lightning, DeepSpeed, FSDP, Megatron, or similar
Familiarity with AEC workflows, design data, BIM/CAD formats, or Autodesk products
THE IDEAL CANDIDATE
Thinks like a systems engineer and executes like a strong software developer
Can balance short-term delivery with long-term platform health
Brings strong technical judgment and ownership
Improves team effectiveness through mentoring and engineering rigor
Enjoys solving scaling, performance, and reliability challenges
At Autodesk, we're building a diverse workplace and an inclusive culture to give more people the chance to imagine, design, and make a better world. Autodesk is proud to be an equal opportunity employer and considers all qualified applicants for employment without regard to race, color, religion, age, sex, sexual orientation, gender, gender identity, national origin, disability, veteran status or any other legally protected characteristic. We also consider for employment all qualified applicants regardless of criminal histories, consistent with applicable law.
Are you an existing contractor or consultant with Autodesk? Please search for open jobs and apply internally (not on this external site). If you have any questions or require support, contact Autodesk Careers.
About Autodesk
Sourced by ZipRecruiter
Autodesk is changing how the world is designed and made. Our technology spans architecture, engineering, construction, product design, manufacturing, media, and entertainment, empowering innovators everywhere to solve challenges big and small. From greener buildings to smarter products to more mesmerizing blockbusters, Autodesk software helps our customers to design and make a better world for all. For more information visit autodesk.com or follow @autodesk.
Industry
Software development
Company size
10,000+ Employees
Headquarters location
San Rafael, CA, US
Year founded
1982