Staff ML Engineer
Zionsville, IN · On-site
... and operating inference infrastructure at scale * CI/CD for ML : Building ML pipelines with ... SageMaker Pipelines, Kubeflow, Airflow, or Dagster; automated model testing, validation gates, and ...
Staff ML Engineer
Zionsville, IN · On-site
... and operating inference infrastructure at scale * CI/CD for ML : Building ML pipelines with ... SageMaker Pipelines, Kubeflow, Airflow, or Dagster; automated model testing, validation gates, and ...
Staff ML Engineer
Zionsville, IN · On-site
... and operating inference infrastructure at scale * CI/CD for ML : Building ML pipelines with ... SageMaker Pipelines, Kubeflow, Airflow, or Dagster; automated model testing, validation gates, and ...
Staff ML Engineer
Zionsville, IN · On-site
... and operating inference infrastructure at scale * CI/CD for ML : Building ML pipelines with ... SageMaker Pipelines, Kubeflow, Airflow, or Dagster; automated model testing, validation gates, and ...
Software Engineer III (AI/ML)
Indianapolis, IN · On-site
$57 - $62/hr
Software Engineer III (AI/ML) Location: Remote - EST preferred Duration: Contract - 12 months Pay ... Hands-on experience with fine-tuning, inference, and metrics implementation for LLMs or MLLMs ...
Software Engineer III (AI/ML)
Indianapolis, IN · On-site
$57 - $62/hr
Software Engineer III (AI/ML) Location: Remote - EST preferred Duration: Contract - 12 months Pay ... Hands-on experience with fine-tuning, inference, and metrics implementation for LLMs or MLLMs ...
$183K - $286K/yr
Experience integrating AI/ML inference services and data pipelines through governed integration patterns * Background working across organizational boundaries, integrating systems owned by separate ...
$183K - $286K/yr
Experience integrating AI/ML inference services and data pipelines through governed integration patterns * Background working across organizational boundaries, integrating systems owned by separate ...
Gen AI Engineer
Indianapolis, IN · Hybrid
... for inference optimization; RAG architecture design and implementation. * Advanced cloud infrastructure (AWS EKS/ECS, GCP GKE, Azure AKS) knowledge. * Containerization strategies for ML workloads;
Gen AI Engineer
Indianapolis, IN · Hybrid
... for inference optimization; RAG architecture design and implementation. * Advanced cloud infrastructure (AWS EKS/ECS, GCP GKE, Azure AKS) knowledge. * Containerization strategies for ML workloads;
Gen AI Engineer
Indianapolis, IN · Hybrid
... for inference optimization; RAG architecture design and implementation. * Advanced cloud infrastructure (AWS EKS/ECS, GCP GKE, Azure AKS) knowledge. * Containerization strategies for ML workloads;
Gen AI Engineer
Indianapolis, IN · Hybrid
... for inference optimization; RAG architecture design and implementation. * Advanced cloud infrastructure (AWS EKS/ECS, GCP GKE, Azure AKS) knowledge. * Containerization strategies for ML workloads;
Senior Software Engineer Applied AI
Indianapolis, IN · Remote
$117K - $154K/yr
End-to-end ML pipelines: feature engineering, model training, and scheduled inference * Imbalanced, messy real-world data; calibration and explainability for non-technical consumers * Turning ...
New
Senior Software Engineer Applied AI
Indianapolis, IN · Remote
$117K - $154K/yr
End-to-end ML pipelines: feature engineering, model training, and scheduled inference * Imbalanced, messy real-world data; calibration and explainability for non-technical consumers * Turning ...
New
Explore and evaluate new AI/ML techniques, tools, and methodologies, applying relevant innovations ... and inference efficiency to minimize cost and latency while preserving accuracy. * MLOps ...
New
Explore and evaluate new AI/ML techniques, tools, and methodologies, applying relevant innovations ... and inference efficiency to minimize cost and latency while preserving accuracy. * MLOps ...
New
Google AI Lead Architect
Indianapolis, IN · On-site
$52.75 - $72.50/hr
Integrate and fine-tune Large Language Models (LLMs) and other AI/ML models into enterprise applications. Develop and implement strategies for model deployment, inference, and monitoring, with an ...
Google AI Lead Architect
Indianapolis, IN · On-site
$52.75 - $72.50/hr
Integrate and fine-tune Large Language Models (LLMs) and other AI/ML models into enterprise applications. Develop and implement strategies for model deployment, inference, and monitoring, with an ...
Postdoctoral Fellow in Biostatistics and Health Data Science
Kokomo, IN · On-site
$42K - $58K/yr
Apply causal inference and discovery frameworks to clinical questions. * Translate proposed methods ... Strong ML/deep learning foundation plus expertise in at least one of: multimodal learning, time ...
Postdoctoral Fellow in Biostatistics and Health Data Science
Kokomo, IN · On-site
$42K - $58K/yr
Apply causal inference and discovery frameworks to clinical questions. * Translate proposed methods ... Strong ML/deep learning foundation plus expertise in at least one of: multimodal learning, time ...
Postdoctoral Fellow in Biostatistics and Health Data Science
Bloomington, IN · On-site
$45K - $61K/yr
Apply causal inference and discovery frameworks to clinical questions. * Translate proposed methods ... Strong ML/deep learning foundation plus expertise in at least one of: multimodal learning, time ...
Postdoctoral Fellow in Biostatistics and Health Data Science
Bloomington, IN · On-site
$45K - $61K/yr
Apply causal inference and discovery frameworks to clinical questions. * Translate proposed methods ... Strong ML/deep learning foundation plus expertise in at least one of: multimodal learning, time ...
Data Engineer
$135K - $155K/yr
The position requires working across departments to build, operate, and optimize highly available data pipelines that feed analytics, ML training and inference, and retrieval-augmented generation ...
Data Engineer
$135K - $155K/yr
The position requires working across departments to build, operate, and optimize highly available data pipelines that feed analytics, ML training and inference, and retrieval-augmented generation ...
Advanced computational and data-science methods, including modern ML/AI workflows for pattern recognition, clustering, anomaly detection, and inference in large and noisy datasets. Exploratory work ...
Advanced computational and data-science methods, including modern ML/AI workflows for pattern recognition, clustering, anomaly detection, and inference in large and noisy datasets. Exploratory work ...
Senior AI Architect
Carmel, IN · On-site
Responsibilities : • Architect end-to-end AI/ML systems including data pipelines, feature stores, training infrastructure, and inference services. • Lead architectural planning for advanced use ...
Senior AI Architect
Carmel, IN · On-site
Responsibilities : • Architect end-to-end AI/ML systems including data pipelines, feature stores, training infrastructure, and inference services. • Lead architectural planning for advanced use ...
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 ...
Ml Inference information
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?
| 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.
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.
What are popular job titles related to Ml Inference jobs in Indiana? For Ml Inference jobs in Indiana, the most frequently searched job titles are:
What job categories do people searching Ml Inference jobs in Indiana look for? The top searched job categories for Ml Inference jobs in Indiana are:
What cities in Indiana are hiring for Ml Inference jobs? Cities in Indiana with the most Ml Inference job openings:
Full-time
Medical, Dental, Vision, Life, Retirement
Re-posted 5 days ago
Group1001 rating
9.5
Based on 8 frontline employees who took The Breakroom Quiz
9th of 281 rated insurance
Job description
Group 1001is a consumer-centric, technology-driven family of insurance companies on a mission to deliver outstanding value and operational performance by combining financial strength and stability with deep insurance expertise and a can-do culture. Group1001's culture emphasizes the importance of collaboration, communication, core business focus, risk management, and striving for outcomes. This goal extends to how we hire and onboard our most valuable assets - our employees.
*Please note, this position requires an in-person interview.
Why This Role Matters:
We're building AI&ML-powered products that will transform how Group 1001 approaches pricing optimization, claims automation, and risk intelligence. To do this at scale, we need robust ML infrastructure-not just great models.
As a Staff ML Engineer, you'll focus on the MLOps and infrastructure layer that makes ML production-ready: model serving, feature pipelines, experiment tracking, and CI/CD for ML. You'll help shape our ML platform architecture, working alongside Platform Engineering teams to ensure ML workloads run reliably on our modern stack: Snowflake, Dagster, Coalesce, Palantir and AWS SageMaker.
This role is for engineers who are as passionate about infrastructure, deployment, and operationalizing ML as they are about the models themselves
*Please note, this position requires an in-person interview.
How You'll Contribute:Partner with Data & Platform Engineering to define how ML workloads integrate with our Snowflake-Dagster-Palantir ecosystem
Evaluate and recommend tooling for the ML stack-balancing build vs. buy decisions against our scale and compliance needs
Contribute to platform roadmap discussions, advocating for infrastructure investments that accelerate ML delivery
Establish CI/CD pipelines for ML: automated testing, model validation, staged deployments, and rollback capabilities using SageMaker Pipelines, Step Functions, or similar orchestration
Implement model monitoring and observability: drift detection, performance degradation alerts, and automated retraining triggers
Architect ML workloads on AWS: SageMaker (Training Jobs, Processing, Endpoints), EC2/EKS for custom serving, S3 for artifact storage, and IAM for secure access patterns
Optimize for cost and performance-right-sizing instances, spot instance strategies, auto-scaling endpoints, and efficient GPU utilization
Integrate ML infrastructure with our Dagster orchestration layer for end-to-end pipeline visibility
Mentor senior ML engineers and technical leads, developing the next generation of ML engineering leadership
Technical Skills:
MLOps & Model Serving: Hands-on experience with model serving frameworks (SageMaker Endpoints, Seldon Core, BentoML, Ray Serve, or TensorFlow Serving); building and operating inference infrastructure at scale
CI/CD for ML: Building ML pipelines with SageMaker Pipelines, Kubeflow, Airflow, or Dagster; automated model testing, validation gates, and deployment automation
AWS & Cloud Infrastructure: Strong AWS experience-SageMaker, EKS/ECS, Lambda, Step Functions, S3, IAM; infrastructure-as-code (Terraform, CDK, CloudFormation)
Monitoring & Observability: Model monitoring, drift detection, alerting; tools like Evidently, WhyLabs, SageMaker Model Monitor, or custom solutions
Core ML Fundamentals: Working knowledge of Python, ML frameworks (PyTorch, TensorFlow, scikit-learn), and model evaluation-enough to partner effectively with data scientists
Feature Engineering Infrastructure: Experience with feature stores (SageMaker Feature Store, Feast, Tecton, or similar); designing feature pipelines for both batch and real-time serving
Experiment Tracking & Registry: MLflow, Weights & Biases, SageMaker Experiments, or similar; establishing reproducibility and governance across ML projects
Nice to Have: Palantir Foundry, Kubernetes, Bedrock, cost optimization strategies for ML workloads
Education:
Bachelor's degree in Computer Science, Data Science, Engineering, or related field
Master's degree or equivalent experience preferred
Experience:
6-10 years in ML engineering, MLOps, or platform engineering with a focus on productionizing ML systems
Demonstrated experience building ML infrastructure that others build upon-serving layers, feature stores, or MLOps tooling
Track record of improving ML delivery velocity through infrastructure and automation
Proven ability to work cross-functionally with data scientists, platform engineers, and stakeholders
Experience mentoring and developing senior engineers and technical leaders
Strong executive presence with ability to influence stakeholders at all levels of the organization
Preferred Qualifications:
Experience in insurance or financial services with deep understanding of industry challenges
Recognized expertise through conference presentations, publications, or industry speaking engagements
Experience with enterprise-scale systems and complex technical environments
Proven ability to build consensus and drive alignment across multiple teams and stakeholders
Competencies and Soft Skills:
Executive presence with ability to influence senior leadership and drive organizational change
Strategic vision with ability to define long-term technical direction aligned with business goals
Strong leadership skills with proven ability to develop and mentor senior technical talent
Exceptional communication skills with ability to articulate technical strategy to executive audiences
Political acumen with ability to navigate complex organizational dynamics and build consensus
Compensation:
Our compensation reflects the cost of labor across several U.S. geographic markets. The base pay for this position ranges from $190,000/year in our lowest geographic market up to $215,000/year in our highest geographic market. Pay is based on factors such as market location, job-related skills, and experience.
Benefits Highlights:
Employees who meet benefit eligibility guidelines and work 30 hours or more weekly, have the ability to enroll in Group 1001's benefits package. Employees (and their families) are eligible to participate in the Company's comprehensive health, dental, and vision insurance plan options. Employees are also eligible for Basic and Supplemental Life Insurance, Short and Long-Term Disability. All employees (regardless of hours worked) have immediate access to the Company's Employee Assistance Program and wellness programs-no enrollment is required. Employees may also participate in the Company's 401K plan, with matching contributions by the Company.
Group 1001, and its affiliated companies, is strongly committed to providing a supportive work environment where employee differences are valued. Diversity is an essential ingredient in making Group 1001 a welcoming place to work and is fundamental in building a high-performance team. Diversity embodies all the differences that make us unique individuals. All employees share the responsibility for maintaining a workplace culture of dignity, respect, understanding and appreciation of individual and group differences.
What Group1001 employees say
Pay
Benefits
Hours and flexibility
Workplace
Get the full story on Breakroom
About Group1001
Sourced by ZipRecruiter
Company size
201 - 500 Employees
Headquarters location
Indianapolis, IN, US
Year founded
2013