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Human Machine Teaming Jobs in Michigan (NOW HIRING)

... from human feedback (RLHF), and instruction tuningDeploy ML models, LLMs, and AI agents into ... red teaming) for algorithm development and test various scenariosAutomate model deployment ...

Machine Learning Engineer

Dearborn, MI

$105K - $126K/yr

... human feedback (RLHF), and instruction tuning * Deploy ML models, LLMs, and AI agents into ... red-teaming) for algorithm development and test various scenarios * Automate model deployment ...

Designs and implement secure machine learning operations (MLOps) controls for datasets, features ... Leads offensive security activities for AI systems, including adversarial testing, AI red teaming ...

Designs and implement secure machine learning operations (MLOps) controls for datasets, features ... Leads offensive security activities for AI systems, including adversarial testing, AI red teaming ...

Human Machine Teaming information

What is Human Machine Teaming?

Human Machine Teaming refers to the collaboration between humans and artificial intelligence (AI) systems, robots, or other machines to achieve shared goals. This partnership leverages the complementary strengths of humans—such as creativity, judgment, and adaptability—and machines, which excel at processing large amounts of data quickly and performing repetitive tasks. The goal is to improve decision-making, efficiency, and outcomes in various industries, including defense, healthcare, manufacturing, and more. Effective human machine teaming requires thoughtful design of interfaces, clear communication protocols, and ongoing training for both humans and machines to work together seamlessly.

What are the key skills and qualifications needed to thrive as a Human-Machine Teaming Specialist, and why are they important?

To thrive as a Human-Machine Teaming Specialist, you need expertise in human factors engineering, systems integration, and data analysis, often supported by a background in computer science, engineering, or cognitive psychology. Familiarity with AI platforms, machine learning tools, and human-computer interaction (HCI) frameworks is typically required. Strong collaboration, problem-solving, and communication skills help bridge the gap between human users and advanced technologies. These capabilities are crucial to designing seamless interactions, ensuring safety, and optimizing the joint performance of human and machine teams.

What is a $900,000 AI job?

A $900,000 AI job typically refers to a high-level position in artificial intelligence, such as AI research director, chief AI officer, or senior machine learning executive, often requiring advanced skills in data science, programming, and leadership. These roles usually involve overseeing AI strategy, development, and deployment in large organizations and may include significant compensation packages with bonuses and stock options.

What jobs can I get with human computer interaction?

Human Machine Teaming professionals can pursue roles such as user experience designer, human factors specialist, usability analyst, or interface developer. These jobs often require skills in ergonomics, cognitive psychology, and proficiency with design tools and user research methods.

What jobs pay $2000 a day?

In the field of Human Machine Teaming, high-paying roles such as senior robotics engineers, AI specialists, or automation project managers can earn around $2,000 per day, especially with extensive experience, advanced certifications, and working in specialized industries like defense or aerospace. These positions often require strong technical skills, security clearances, and the ability to manage complex human-machine systems. Compensation varies based on industry, location, and level of expertise.

What job makes $10,000 a month without a degree?

In the field of Human Machine Teaming, roles such as specialized technicians, automation specialists, or robotics operators can potentially earn around $10,000 per month with relevant experience and technical skills. These jobs often require hands-on training, certifications, or expertise in operating and maintaining advanced systems, rather than a traditional degree.

What are some common challenges faced by professionals working in Human Machine Teaming, and how can they be addressed?

Professionals in Human Machine Teaming often encounter challenges such as balancing effective communication between humans and AI systems, ensuring trust in automated processes, and integrating new technologies into existing workflows. Addressing these challenges requires continuous learning, active collaboration with multidisciplinary teams, and clear communication of complex technical concepts to non-technical stakeholders. Regular training, user feedback loops, and staying updated on advancements in AI and human factors engineering can help professionals navigate and overcome these obstacles successfully.
What are popular job titles related to Human Machine Teaming jobs in Michigan? For Human Machine Teaming jobs in Michigan, the most frequently searched job titles are:
What job categories do people searching Human Machine Teaming jobs in Michigan look for? The top searched job categories for Human Machine Teaming jobs in Michigan are:
What cities in Michigan are hiring for Human Machine Teaming jobs? Cities in Michigan with the most Human Machine Teaming job openings:

Machine Learning Engineer

Stefanini

Dearborn, MI

Other

Posted 9 days ago


Job description


Stefanini Group is hiring!
Stefanini is looking for a Machine Learning Engineer, Dearborn, MI (Onsite)
For quick apply, please reach out Saurabh Kapoor at /
You will be responsible for designing, building, deploying, and scaling complex self-running ML solutions - including Generative AI and Large Language Model (LLM) systems - in areas such as computer vision, perception, localization, natural language processing, and conversational AI. They automate and optimize the end-to-end ML and Gen AI model lifecycle using expertise in experimental methodologies, statistics, prompt engineering, and coding for tool building and analysis. Design and develop innovative ML models, Gen AI systems, and software algorithms - including LLM-based architectures (e.g., transformer models, RAG pipelines, fine-tuned foundation models) to solve complex business problems in both structured and unstructured environments
ResponsibilitiesDesign, build, maintain, and optimize scalable ML and Gen AI pipelines, architecture, and infrastructure, including vector databases, embedding stores, and LLM serving layersUse machine learning and statistical modeling techniques such as decision trees, logistic regression, Bayesian analysis, and deep learning methods, alongside prompt engineering, retrieval-augmented generation (RAG), and parameter-efficient fine-tuning (PEFT/LoRA) to develop and evaluate algorithms that improve product/system performance, quality, data management, and accuracy Adapt machine learning and Gen AI capabilities to domains such as virtual reality, augmented reality, object detection, tracking, classification, terrain mapping, intelligent document processing, and AI-powered agent workflowsTrain, fine-tune, and re-train ML models and LLMs as required, including supervised fine-tuning (SFT), reinforcement learning from human feedback (RLHF), and instruction tuningDeploy ML models, LLMs, and AI agents into production; run simulations and evaluations (including LLM evals and red teaming) for algorithm development and test various scenariosAutomate model deployment, training, re-training, and Gen AI pipeline orchestration, leveraging principles of agile methodology, CI/CD/CT, MLOps, and LLMOps - including guardrail integration, prompt versioning, and observability tooling Enable model management for model versioning, traceability, and governance - including responsible AI practices, bias evaluation, hallucination mitigation, and content safety controls - to ensure modularity and consistency across environments for both ML and Gen AI systems
Experience RequiredGoogle Cloud Platform - Experience deploying and managing services on Google Cloud Platform, including Compute Engine, Cloud Storage, IAM, and Cloud Functions. For example, designing and implementing a cloud-native application architecture using GKE (Google Kubernetes Engine) with Cloud SQL and Pub/Sub. Big Data - Experience working with large-scale data processing frameworks such as Apache Spark, Dataflow, or BigQuery. For example, building ETL pipelines that process terabytes of daily event data and transform it into downstream analytics. Data Warehousing - Experience designing and maintaining data warehouse solutions (e.g., BigQuery, Snowflake, Redshift). For example, modeling a star schema for a retail analytics platform that supports reporting on sales, inventory, and customer behaviorArtificial Intelligence & Expert Systems - Experience developing or integrating AI/ML models and rule-based expert systems. For example, building a classification model using Vertex AI to predict customer churn, or implementing a rule engine that automates underwriting decisions. API - Experience designing, building, and consuming RESTful or gRPC APIs. For example, developing a versioned REST API with OAuth 2.0 authentication that serves as the integration layer between a mobile application and backend microservices.
Experience PreferredStrong understanding of Generative AI principles and architectures, including Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG) systems.Proven experience in building and deploying RAG systems, including the use of Vector Databases. Proficiency in Python programming. Solid experience with SQL for data manipulation and querying. Hands-on experience with Google Cloud Platform (Google Cloud Platform) services relevant to AI/ML. Basic understanding and practical experience with Machine Learning model fine-tuning. Familiarity with data engineering concepts and practices. Expertise in prompt engineering techniques for interacting with LLMs. Experience with the OpenAI SDK. Experience developing robust APIs, preferably with FastAPI. Proficiency with **version control systems (e.g., Git). Experience with **containerization technologies (e.g., Docker).Google Cloud Platform - Familiarity with advanced Google Cloud Platform services beyond core compute and storage, such as Vertex AI, Dataflow, Cloud Composer (Airflow), and BigQuery ML. For example, using Cloud Composer to orchestrate scheduled data pipelines that feed into a BigQuery data warehouse.
**Listed salary ranges may vary based on experience, qualifications, and local market. Also, some positions may include bonuses or other incentives***
Stefanini takes pride in hiring top talent and developing relationships with our future employees. Our talent acquisition teams will never make an offer of employment without having a phone conversation with you. Those face-to-face conversations will involve a description of the job for which you have applied. We also speak with you about the process, including interviews and job offers.
About Stefanini Group
The Stefanini Group is a global provider of offshore, onshore and near shore outsourcing, IT digital consulting, systems integration, application, and strategic staffing services to Fortune 1000 enterprises around the world. Our presence is in countries like the Americas, Europe, Africa, and Asia, and more than four hundred clients across a broad spectrum of markets, including financial services, manufacturing, telecommunications, chemical services, technology, public sector, and utilities. Stefanini is a CMM level 5, IT consulting company with a global presence. We are a CMM Level 5 company.
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