Machine Learning Engineer Remote Jobs in Michigan (US)
This page tracks remote machine learning engineer openings that are location-eligible for Michigan.
This page tracks remote machine learning engineer openings that are location-eligible for Michigan.
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• The Generative AI Developer Intern focuses on researching and developing advanced healthcare informatics, particularly in the realm of Generative AI and emerging Artificial General Intelligence. • The intern will assist and take a primary role in developing, researching, creating analytical materials, and collaborating with various teams to drive AI-related projects. • Gain hands-on experience in generative AI model development, architecture design, and database management. • Communicates technology-related updates and requirements to other departments and contributes to presentations for senior management. • Collaborates with research and development teams, product management, and strategic analysts to support ongoing projects. • Partners with Business Units to provide reliable intelligence, validated technology options, and insights on enterprise and industry trends. • Supports technology project teams by coordinating specific tasks, assisting with day-to-day operations, and contributing to the successful delivery of solutions. • Completes all responsibilities and goals outlined in the internship program. • Completes all special projects and other duties as assigned.
• Design, develop, and deploy AI/ML solutions to modernize core pharmacy platforms, with a focus on scalability, reliability, performance, and security • Leverage Generative AI, LLMs, and agentic AI frameworks to automate and enhance pharmacy workflows and decision-making processes • Collaborate with business and technical stakeholders to understand pharmacy domain requirements and translate them into robust AI/ML-driven technical solutions • Contribute to architecture and technical design of AI/ML pipelines, including model selection, data integration, and deployment patterns • Establish and maintain AI/ML engineering standards, best practices, and quality assurance processes • Conduct code reviews, provide constructive feedback, and mentor engineers on modern AI/ML engineering practices • Partner with cross-functional teams including data scientists, product managers, platform engineers, and pharmacy domain experts • Translate legacy system modernization needs into scalable AI applications that enhance products, workflows, and operational efficiency • Monitor and optimize AI/ML model performance, resource utilization, and platform reliability • Collaborate with DevOps and infrastructure teams to ensure secure, scalable deployment and operations of AI/ML solutions • Stay current with advancements in AI/ML frameworks, Generative AI, LLMs, and pharmacy technology modernization
Founded in 2004 and led by CEO Steve Chapman, Natera is a company in the biotechnology market that offers genetic testing and diagnostics on a global scale. Ope
Senior AI-Machine Learning Engineer US Remote Role Overview The Senior AI/ML Engineer is responsible for designing, building, and deploying Natera’s Generative AI and Machine Learning platforms. The role needs excellent hands-on engineering excellence to build robust, compliant, and efficient Generative AI and ML platform components. This role requires deep expertise in Generative AI and machine learning engineering at scale, with a passion for building robust, compliant, and high-performance systems that directly impact patient outcomes and clinical innovation. You will design, build, and scale enterprise-grade AI/ML systems that power internal workflows (R&D, Lab Ops, Clinical Trials, Billing, Patient/Provider engagement) and external-facing AI/ML platforms. You will design and build cutting-edge AI solutions leveraging agentic architecture, retrieval-augmented generation (RAG), vector search, feature stores, LLMOps, experimentation, observability, and compliance-first AI pipelines. You will be responsible for development of a production-ready Generative AI and MLOps platform with reusable components used to deploy multiple AI solutions across Natera’s business units. You will also develop clear standards and best practices established for AI/ML development across the organization. Key Responsibilities Platform Development - Design and implement foundational GenAI services: vector search, prompt tuning, agent orchestration, document extraction, context/memory services, model/endpoint registry, feature/embedding stores, guardrails, and evaluation pipelines - Build the underlying infrastructure for autonomous and semi-autonomous AI agents including support for agent collaboration, reasoning, and memory persistence, enabling continuous context-aware execution - Build standardized APIs/SDKs that make it easy for product teams to compose, deploy, and monitor Generative AI workloads. - Ensure platform components meet enterprise-grade requirements for scalability, latency, multi-region resilience, and cost efficiency Generative AI Enablement - Stand up LLM runtimes with token/rate governance, caching, and safe tool-use - Implement RAG at scale: ingestion pipelines, chunking/embedding policies, hybrid search, relevance/risk scoring, and feedback loops - Build agent orchestration (single & multi-agent) with planning, tool routing, shared/persistent memory, and inter-agent communication - Integrate tooling and APIs that allow agents to interact with internal systems, retrieve data securely, and take action under strict controls - Collaborate with research teams to prototype and productionize multi-agent architectures for workflow automation, report generation, and data synthesis. Infrastructure & Automation - Implement cloud-native infrastructure for large-scale model training and serving using Kubernetes, MLflow, Terraform, and AWS-native services - Automate data and model pipelines for RAG, LLM fine-tuning, and agent orchestration - Integrate observability tools (Datadog or equivalent) for real-time performance, drift detection and safety monitoring of AI outputs - Optimize compute and storage architecture to ensure cost-effective scaling of large models and multi-agent workloads - Partner with security, data governance, SRE, and application teams to productize platform capabilities Safety, Security & Compliance Integration - Embed compliance-by-design (HIPAA/CLIA/CAP/FDA/GDPR): PHI/PII handling, encryption, access controls, audit trails - Implement guardrails: input/output filters, prompt hardening, allow/deny policies for tool execution, policy-as-code in CI/CD - Bias/explainability hooks and automated evaluations for RAG/LLM/agents; drift and regression detection Technical Leadership & Mentorship - Establish golden paths (templates, examples, docs) and lead platform architecture reviews, code reviews, and design discussions - Partner with data scientists, AI researchers, and product engineers to deliver reliable and maintainable AI services - Mentor junior engineers in platform development, distributed systems, and agentic AI infrastructure concepts - Influence cross-functional roadmaps by partnering with Product and Engineering leadership to align delivery with business needs Qualifications Required: - 8+ years in software/ML engineering, with 5+ years in ML engineering at scale - Expertise in building production-grade ML/LLM systems on AWS tech stack (Python, TensorFlow/PyTorch, Spark, MLflow/Kubeflow, vector DBs) - Proven track record with GenAI/LLMs: fine-tuning, RAG, prompt orchestration, agentic systems, safety guardrails, monitoring, and cost optimization - Hands-on with RAG systems (embeddings, vector DBs, retrieval policies) and LLM runtime operations (caching, quotas, multi-model routing) - Experience building agentic AI platforms (LangChain, LlamaIndex, CrewAI, Semantic Kernel, or custom) - Deep knowledge of data-intensive systems, distributed architectures, and cloud-native development - Strong grounding in compliance-first engineering in healthcare, biotech, or diagnostics preferred - Track record building secure, compliant data/AI systems and automating policy checks. - Excellent ability to influence across teams, mentor engineers, and set technical standards Preferred: - Masters degree in Computer Science, AI/ML, engineering or related field - Experience in healthcare, pharma, diagnostics, or other regulated industries - Familiarity with AI governance frameworks, bias detection, explainability, and compliance (e.g., HIPAA, CLIA, FDA) The pay range is listed and actual compensation packages are based on a wide array of factors unique to each candidate, including but not limited to skill set, years & depth of experience, certifications and specific office location. This may differ in other locations due to cost of labor considerations. Remote USA $125,000 - $156,300 USD OUR OPPORTUNITY Natera™ is a global leader in cell-free DNA (cfDNA) testing, dedicated to oncology, women’s health, and organ health. Our aim is to make personalized genetic testing and diagnostics part of the standard of care to protect health and enable earlier and more targeted interventions that lead to longer, healthier lives. The Natera team consists of highly dedicated statisticians, geneticists, doctors, laboratory scientists, business professionals, software engineers and many other professionals from world-class institutions, who care deeply for our work and each other. When you join Natera, you’ll work hard and grow quickly. Working alongside the elite of the industry, you’ll be stretched and challenged, and take pride in being part of a company that is changing the landscape of genetic disease management. WHAT WE OFFER Competitive Benefits - Employee benefits include comprehensive medical, dental, vision, life and disability plans for eligible employees and their dependents. Additionally, Natera employees and their immediate families receive free testing in addition to fertility care benefits. Other benefits include pregnancy and baby bonding leave, 401k benefits, commuter benefits and much more. We also offer a generous employee referral program! For more information, visit www.natera.com. Natera is proud to be an Equal Opportunity Employer. We are committed to ensuring a diverse and inclusive workplace environment, and welcome people of different backgrounds, experiences, abilities and perspectives. Inclusive collaboration benefits our employees, our community and our patients, and is critical to our mission of changing the management of disease worldwide. All qualified applicants are encouraged to apply, and will be considered without regard to race, color, religion, gender, gender identity or expression, sexual orientation, national origin, genetics, age, veteran status, disability or any other legally protected status. We also consider qualified applicants regardless of criminal histories, consistent with applicable laws. If you are based in California, we encourage you to read this important information for California residents. Link: https://www.natera.com/notice-of-data-collection-california-residents/ Please be advised that Natera will reach out to candidates with a @natera.com email domain ONLY. Email communications from all other domain names are not from Natera or its employees and are fraudulent. Natera does not request interviews via text messages and does not ask for personal information until a candidate has engaged with the company and has spoken to a recruiter and the hiring team. Natera takes cyber crimes seriously, and will collaborate with law enforcement authorities to prosecute any related cyber crimes.
Role Description AI Architect is a technical leader reporting to the Director, Enterprise App Engineering. This role defines and drives Guidewire's enterprise AI strategy — translating business objectives across GTM, Finance, HR, Legal, and Engineering into production-grade AI systems that deliver measurable impact. The architect owns end-to-end design across ingestion, orchestration, agent frameworks, security, compliance, and delivery. - Define and own Guidewire's enterprise AI architecture vision — establishing technical direction, design standards, and reference implementations across all BizTech AI initiatives. - Architect and lead delivery of production AI systems: multi-agent orchestration platforms, RAG and GraphRAG knowledge systems, LLM-powered workflows, and enterprise context graph initiatives spanning all Systems of Record. - Own the full-stack AI architecture — from AWS infrastructure and data pipelines (Kafka, Iceberg, dbt, pgvector) through LLM gateway design, agent orchestration (LangGraph or equivalent), and user interfaces. - Help define enterprise AI security and governance standards — covering IAM, ABAC policy enforcement, PII handling, prompt injection defence, hallucination detection, and audit trail design for SOX and GDPR compliance. - Lead complex, multi-team AI programs end to end — from stakeholder alignment and architectural design through delivery, adoption, and measured business outcomes across concurrent initiatives. - Help drive technology evaluation and build-vs-buy decisions across the AI tooling portfolio. - Partner with Data Governance, Security, Legal, and Compliance teams to embed AI responsibly across the organisation. - Mentor senior engineers and foster a culture of technical excellence, pragmatic innovation, and accountable delivery across the BizTech organization. Qualifications - Bachelor's Degree in Computer Science, Engineering, or equivalent work experience. - 12+ years in enterprise software architecture, with at least 2 years designing and delivering production AI/ML or LLM-based systems at scale. - Proven track record leading large, complex enterprise AI programs — multi-year, multi-team initiatives with clear, measurable business outcomes. - AWS expertise: Bedrock, EKS, Lambda, RDS, S3, Kinesis, IAM, VPC. AWS Solutions. - Full-stack AI fluency: event ingestion and streaming pipelines, vector and graph databases, LLM orchestration and agent frameworks, API design, and front-end delivery. - Experience with AI observability and evaluation — trace instrumentation, eval frameworks, and production monitoring (Datadog LLM Observability, LangSmith, or equivalent). - iPaaS and integration architecture experience — Workato, MuleSoft, or equivalent for enterprise SoR integration and event-driven automation. - 5+ years of technical leadership — mentoring senior engineers, driving architecture strategy, and managing cross-functional stakeholder relationships. - Excellent communication and executive presence — able to present complex AI architecture decisions clearly to both engineering teams and senior business leadership. - Demonstrated experience shipping Generative AI systems in production enterprise environments, with measurable impact on business outcomes. Requirements - The US base salary range for this full-time position is $132,000 - $198,000. Your base pay will depend on your experience, skills, education, training, and location among other factors. - All full-time positions or part-time roles working 30 hours or more a week at Guidewire are eligible for benefits that support their health and well-being including health, dental, and vision insurance, paid time off, and a company sponsored retirement plan. - In addition, some roles may be eligible for the annual company bonus plan, commissions, and/or long term incentive awards which are contingent on a variety of factors including, but not limited to, company and employee performance. Benefits - Disability Accommodations and Guidewire’s Appeals Process. Guidewire provides accommodations to the hiring process to create a fair opportunity for candidates with disabilities to contend for open positions. - Accommodation requests should be directed to Accommodations@guidewire.com. - If things do not go as hoped, we invite you to use our appeals process. Guidewire promises to independently review any denied accommodation and any decision not to offer you the position. - The appeals process is the same in either case. Within five business days of receiving a notice of denial of an accommodation, or receiving a notice of your non-selection for a vacancy, e-mail Accommodations@guidewire.com to make an appeal. - Guidewire will assign a new decision-maker to review the request and/or hiring decision, who will then notify you in writing of a decision within 10 business days.
A venture-backed startup building a modern data platform for the real estate industry, enabling automation, analytics, and AI-powered workflows for real estate operators. The team includes engineers and leaders from companies such as major fintech, cloud, and consumer technology platforms, and is focused on solving complex infrastructure and data challenges in a large, underserved industry.
Role Description A fast-growing cloud-native professional services company is hiring Forward Deployed Engineers for a new AI-focused business unit built around enterprise LLM adoption. This is a startup-style team inside a larger, established consulting organization: small team, high autonomy, direct client ownership, and real delivery responsibility from day one. You’ll work directly with enterprise customers to take AI from idea to production. Engagements may focus on AI-assisted software development, agentic engineering workflows, RAG/LLM applications, or applied AI enablement for business teams. - Own client engagements end-to-end: discovery, scoping, architecture, delivery, enablement, and expansion. - Run stakeholder interviews, technical workshops, architecture reviews, and enablement sessions. - Build or guide production AI systems such as agents, RAG pipelines, AI-native apps, workflow automations, or AI-assisted SDLC tooling. - Translate ambiguous business problems into practical, deliverable technical solutions. - Work with both technical and non-technical stakeholders, from engineers and architects to VP/C-suite leaders. - Identify opportunities to expand accounts through strong delivery and proactive problem-solving. - Leave clients with durable capability: documented, maintainable systems, workflows, and enablement materials. Qualifications - 4–10 years of experience in client-facing technical delivery, consulting, systems integration, professional services, FDE, solutions engineering, or customer-facing software engineering. - Hands-on experience with LLM applications, AI agents, RAG pipelines, AI workflows, or AI-assisted development tools. - Comfortable with tools such as Claude, Claude Code, Cursor, Codex, AWS Bedrock, LangChain/LangSmith, Python, AWS, Kubernetes, Zapier, Power Automate, or similar. - Strong consulting instincts: able to run discovery, manage stakeholders, communicate clearly, and drive outcomes without heavy oversight. - Able to speak technically with engineers while also explaining business value to executives. - High tolerance for ambiguity and a bias toward action. Requirements - Two strong-fit profiles: - AI/Workflow Transformation FDE: Consulting, implementation, or enterprise SaaS delivery background with hands-on AI enablement experience. Strong fit for candidates who have helped business teams adopt AI tools, automate workflows, or transform knowledge-worker processes. - Technical / SDLC FDE: Software engineering background with client-facing delivery experience. Strong fit for candidates who have built production AI systems, agentic coding workflows, developer tooling, RAG apps, or engineering enablement programs. - Strong signals: - Prior consulting, professional services, systems integration, or implementation experience. - Experience leading workshops, enablement sessions, or technical discovery. - AWS/cloud consulting background. - Production AI/LLM project experience. - Account expansion or “sell-do” delivery experience. - Comfort working remotely with up to 50% travel depending on client needs.
Founded in 2004 and led by CEO Steve Chapman, Natera is a company in the biotechnology market that offers genetic testing and diagnostics on a global scale. Ope
• Lead the technical design and deployment of multi-agent systems capable of autonomous hypothesis generation and tool use, including genomic variant calling, LLM fine-tuning, and clinical trial matching pipelines • Incorporate and advance Natera’s transformer-based foundation model by integrating DNA, RNA, and H&E imaging modalities for multi-step biological reasoning and tool use • Implement advanced LLM reasoning frameworks, such as ReAct and Chain-of-Thought, alongside reinforcement fine-tuning (RFT) to ensure agents provide accurate, explainable clinical rationales • Architect systems that autonomously translate complex, multi-modal data into diagnostic and therapeutic insights with human-verifiable reasoning and tracing • Own the technical strategy and product roadmap for agentic workflows across the Biopharma Solutions and Therapeutics Discovery division, converting complex clinical challenges into scalable AI systems • Establish production-grade machine learning engineering standards and reproducible architectures across the AI team to ensure absolute model transparency and scientific auditability • Drive cross-functional alignment and technical consensus by defending agentic architectures and biological reasoning frameworks in rigorous peer reviews
• Pipeline Automation: Design, implement, and manage automated CI/CD and Continuous Training (CT) pipelines for machine learning model development, evaluation, and delivery. • Model Deployment: Containerize, deploy, and scale machine learning models as high-availability microservices or batch processing workflows. • Observability & Monitoring: Establish unified logging, alerting, and monitoring solutions to track model inference performance, system latency, resource utilization, data drift, and concept drift. • Infrastructure Management: Provision and optimize cloud-based ML infrastructure (including GPU/CPU computing clusters) utilizing Infrastructure as Code (IaC) paradigms. • Cross-Functional Collaboration: Work intimately with product development teams to drive infrastructure adoption and efficiency gains through SDK/API development, automation and efficient ML system maintenance. • Governance & Compliance: Implement robust lineage tracking for data, code, and model artifacts to ensure compliance, reproducibility, and security across the entire ML lifecycle. • Data Infrastructure & Tooling: Work with data engineering to improve the data ecosystem, ensuring robust, scalable pipelines for experimentation and ML (including streaming tools like Kafka and Flink for low-latency online inference). • Thought Leadership: Act as a mentor and thought leader, helping to define best practices in machine learning engineering, scalable ML service ops, and agentic AI (AI-Native) best practices.
Transforming cities through autonomous technology to create a safer, greener, more accessible world.
• Architect and operate data and training pipelines across cloud and cluster environments. • Build and maintain distributed training and orchestration tooling. • Design and maintain the data and metadata stores that back our training and evaluation workflows
Role Description We are looking for an Application Architect with strong AI engineering experience to design and build intelligent, agentic applications on Google Cloud Platform. This role sits within an application engineering team and focuses on architecting AI-enabled systems using the Google Agentic Development Kit (ADK), Gemini, and Vertex AI — integrated into enterprise Java/Python backends and cloud-native microservices. You are equally comfortable defining application architecture, designing agentic workflows, writing production-quality code, and translating AI capabilities into practical, mission-aligned solutions for federal stakeholders. This role is remote with a preference for candidates located in Virginia, Maryland, or Washington DC. Key Responsibilities - Architect and implement AI-enabled application systems on GCP, with a focus on agentic workflows using Google ADK and Gemini Pro. - Design human-in-the-loop agentic systems — defining agent roles, tool use, orchestration patterns, and guardrails for responsible, auditable AI behavior. - Integrate AI/ML capabilities (Vertex AI, Gemini APIs, embeddings, RAG) into enterprise Java and Python applications via well-designed APIs and microservices. - Lead application-layer design decisions: data flow, context management, session handling, and state management within agentic architectures. - Collaborate with Data Engineers (BigQuery, Dataform) and Cloud Architects to ensure AI application solutions are grounded in reliable, governed data. - Conduct architectural reviews, define coding standards for AI-integrated applications, and mentor engineers on agentic design patterns. - Evaluate AI use cases for feasibility, risk, and mission fit; prototype and validate approaches before committing to full builds. - Contribute to responsible AI practices: explainability, human oversight, auditability, and alignment with federal AI governance requirements. - Stay current on the Google AI ecosystem (Gemini, ADK, Vertex AI Agent Builder) and inform team and leadership on strategic direction. Qualifications - 5–8 years of software or application engineering experience, with demonstrated focus on AI-integrated or intelligent application design. - Hands-on experience with Google ADK or comparable agentic frameworks (LangGraph, LangChain, AutoGen); Google ADK strongly preferred. - Proficiency in Python for AI/ML integration; Java experience a plus in application team context. - Experience integrating LLM APIs (Gemini, OpenAI, or equivalent) into production application workflows. - Solid understanding of agentic design patterns: tool use, multi-agent orchestration, retrieval-augmented generation (RAG), memory and context management. - Experience with GCP services: Vertex AI, Cloud Run, GKE, BigQuery, Pub/Sub. - Familiarity with REST API design, microservices architecture, and CI/CD pipelines (Harness preferred). - Understanding of responsible AI principles: human-in-the-loop design, auditability, bias awareness, and federal AI governance. Desired Qualifications - Experience with Vertex AI Agent Builder, Gemini Code Assist, or Gemini CLI in a development workflow context. - Familiarity with GCP-native data tooling: BigQuery, Dataform, Looker. - Experience on federal or large-scale enterprise modernization programs. - Exposure to FedRAMP/FISMA requirements and security-compliant AI deployment practices. - Experience with DevSecOps pipelines (Checkmarx, Invicti, or equivalent SAST/DAST tooling). Additional Information - Successful completion of a client-required background investigation and suitability determination will be required. - The ability to obtain and maintain a federal security clearance may be required based on engagement. - Bachelor's degree in Computer Science, Software Engineering, or a related field; advanced degree a plus. - Google Cloud Professional Cloud Architect or Professional Machine Learning Engineer certification preferred. - Security+ desirable. Benefits - Health Care Plan (Medical, Dental & Vision) - Retirement Plan (401k, IRA) - Life Insurance (Basic, Voluntary & AD&D) - Paid Time Off (Vacation, Sick & Public Holidays) - Family Leave (Maternity, Paternity) - Short Term & Long Term Disability - Training & Development - Work From Home - Wellness Resources - Employee Bonus Programs
Cohere Health is a Software-as-a-Service (SaaS) company focused on improving the patient journey by enhancing the quality of care at lower costs, as well as emp
Role Description As an intern you will get a front row seat in a fast growing company which will undoubtedly advance your career and give the right candidate an accelerated career path. In this role, you’ll work with our growing team of world-class engineers, statisticians, and clinical experts to develop and deploy machine learning algorithms that help automate burdensome administrative clinical practices. This is a unique opportunity to join a new engineering team with great ambition and building on modern technology with zero legacy technical debt. Last but not least: People who succeed here are empathetic teammates who are candid, kind, caring, and embody our core values and principles. We believe that diverse, inclusive teams make the most impactful work. Cohere is deeply invested in ensuring that we have a supportive, growth-oriented environment that works for everyone. What you will do: - Work on reliable and scalable production machine learning systems - Contribute to feature design, development, testing, and delivery of our machine learning models - Work cross-functionally across diverse stakeholders, including product managers, statisticians, EHR data specialists and physicians - Actively participate in development of machine learning models Qualifications - You are passionate about building quality products and have end-to-end machine learning experience, leading with the right design and development principles - Experience developing in Python, required (NLP/PyTorch experience preferred) - You have familiarity with common software development practices such as version control, unit testing, and CI/CD - You are a team player and are interested in working at a fast-paced startup environment - You are enrolled in a Bachelor’s or higher (MS/PhD) degree program in computer science, machine learning, computational linguistics, statistics, mathematics or similar field - Prior experience in healthcare and life sciences is a plus, but is not required Equal Opportunity Statement Cohere Health is an Equal Opportunity Employer. We are committed to fostering an environment of mutual respect where equal employment opportunities are available to all. To us, it’s personal.
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Cloud, AWS, Python, Azure, Distributed Systems, PyTorch