AI Research Scientist

• Build, validate, deploy, and scale AI/ML models for high-impact manufacturing engineering, research and development applications.
• Lead technical work across a broad set of AI domains, including classical machine learning, deep learning, GenAI, Large Language Models, multimodal AI, agentic AI, World Models, and physics-based AI.
• Translate business and engineering problems into practical AI/ML solution strategies and executable technical plans.
• Serve as a technical authority on model development, deployment architectures, data strategy, evaluation methods, and responsible AI practices.
• Generate new concepts and ideas while conducting research programs that apply the latest AI/ML tools.
• Drive technical assignments in a resourceful and timely manner with minimal supervision.
• Work well with fellow researchers, centers, operational groups, plant personnel and universities.
• Maintain state-of-art technical skills and knowledge.
• Develop working relationships with internal and external subject matter experts.
• Effectively communicate and detail results through internal and external publication.
• Collaborate with global technical teams and lead discussions as an expert in manufacturing AI/ML.

Senior AI/ML Researcher

AI Engineer

• Build automation workflows using n8n.io and Python as the primary platform.
• Integrate APIs, databases, and AI services to create functional, scalable automation pipelines.
• Work with tools like Claude Code, Codex GitHub Copilot to build agents, streamline development, and generate value from LLMs.
• Write and test prompts that enable AI to complete meaningful tasks—text classification, summarization, chatbot functionality, and more.
• Build task-specific AI agents or multi-step assistants powered by APIs and large language models.
• Take abstract product ideas and build working MVPs quickly, with clarity and minimal oversight.
• Partner with engineering leads and other Augusto team members to deliver meaningful, production-ready solutions.
• Explain technical designs and limitations clearly to both technical and non-technical stakeholders.

AI Workflow Engineer, Developer

• Partner with AI Solution Engineers, Architects, and senior technical teammates
• Design, build, and deploy AI-powered solutions for legal customers
• Translate legal workflows and business needs into technical solutions
• Contribute to technical discovery, solution delivery, and prototyping
• Assist with demonstrations, proofs of concept, and early-stage prototypes
• Develop working knowledge of legal terminology and processes
• Design and implement AI-powered solutions for legal workflows
• Collaborate with senior engineers and architects to ensure solutions are scalable
• Evaluate LLM outputs for legal accuracy and relevance

Software Engineer – AI, CoCounsel Forward Deployed Engineering

Machine Learning Engineer

• Design, develop, and deploy production machine learning models supporting Torc's perception systems.
• Own end-to-end model development for scoped perception problem areas, from data preparation and training through evaluation and deployment.
• Write production-quality ML code to support scalable training, evaluation, and inference workflows.
• Analyze model performance, identify failure modes, and iterate to improve robustness, accuracy, and generalization across diverse driving environments.
• Develop and optimize perception models leveraging multi-modal sensor data, with an emphasis on camera-based and 3D perception systems.
• Collaborate with software engineers, infrastructure teams, and autonomy engineers to integrate perception models into production autonomy software.
• Support improvements to training pipelines, data workflows, and tooling that accelerate model iteration and deployment.
• Contribute to model architecture discussions and technical decision-making within the team.
• Mentor junior engineers on implementation, experimentation, and machine learning best practices.

Senior Machine Learning Engineer – 3D Perception

• Creating the future of smart mobility requires the highly intelligent use of data, metrics, and analytics. That’s where you can make an impact as part of our Global Data Insight & Analytics (GDIA) team.
• Responsible for designing, developing, testing and maintaining software applications and products to meet customer needs both on-prem and cloud native.
• Involved in the entire software development lifecycle including designing software architecture, writing code, testing for quality and deploying the software to meet customer requirements.
• Full-stack software engineering roles, who can develop all components of software including user interface and server side also fall within this job function.

AI/ML Engineer

• Ship and debug code on a live, real-time voice pipeline
• Design control systems around LLMs: guardrails, budgets, watchdogs
• Build and operate LLM evaluation and batch-analysis pipelines
• Own traditional ML workflows from data to scheduled production inference
• Trace production issues from a metric anomaly to root cause

Senior Software Engineer – Applied AI

• Own stakeholder relationships across Medbridge’s executive team, working with them to find the problems and AI enabled solutions that can expedite, or remove, the current blockers in the business.
• Design, build, and ship AI-powered features across our internal teams, from the user interface to the backend services and AI workflows behind it.
• Build full-stack applications using AI-native engineering workflows and tools such as Claude, Cursor, Codex, and OpenAI and Anthropic APIs.
• Develop APIs, backend services, and automation pipelines that support intelligent product capabilities and production-ready deployments.
• Apply software engineering, DevOps, and MLOps best practices to the testing, deployment, monitoring, evaluation, and optimization of AI solutions.
• Partner closely with our Product and Engineering teams to turn ambiguous goals into scalable internal products.
• Use AI tooling throughout your workflow (coding, code review, testing, and planning) and help push the team’s adoption forward.
• Research emerging AI technologies, LLM implementation patterns, and modern product engineering frameworks, and bring what works back to the team.
• Instrument what you ship and use usage data and evidence to decide what to build, fix, or kill next.

Product Engineer – AI

• Build statistical models and production systems to balance advertiser performance with business goals.
• Tune optimization parameters, measure internal competition, and model dynamic environments.
• Design and run experiments to validate theories underpinning the mobile ad tech economy.
• Develop applications in the areas of advertiser budget retention and growth, optimal margin allocation, and bidding innovations.
• Collaborate with a team of world-class engineers with diverse backgrounds as well as peers across the broader company (e.g. Operations, GTM).
• Use strong communication skills (verbal and written) to explain statistical and machine learning concepts to both technical and non-technical audiences.
• Be part of an “engineering excellence” culture through state-of-the-art tools, risk-driven testing, explainable systems, and design/code review.

• Own technical direction of the ML Platform — feature store, model hosting and serving, experimentation, training infrastructure — driving architectural decisions around scalability, reliability, latency, and cost
• Lead design and delivery of large-scope platform initiatives from conception through production, coordinating across ML, data, and infrastructure teams
• Identify and resolve systemic technical challenges: online/offline feature parity, model deployment friction, experimentation velocity, GPU utilization, cross-team dependencies
• Set and maintain a high engineering quality bar through hands-on code contributions, design reviews, and mentorship of platform and ML-adjacent engineers
• Partner with ML engineering, data science, product, and platform leadership to translate ML strategy into technical roadmaps
• Define the paved paths ML teams use to ship models safely — from feature registration through canary rollout, monitoring, and rollback
• Leverage AI-augmented development tools to increase development velocity and code quality

Staff Machine Learning Engineer

• Own the offline dataset pipeline — design, implement, test, and deploy Cloud-based pipelines that convert logged multi-sensor data into VLM/VLA training datasets, spanning geometric labels (3D/2D detection, tracking, segmentation, depth) through semantic, scenario-level, and action/trajectory-grounded annotations.
• Build VLM-assisted auto-labeling — develop open-vocabulary detection, dense captioning, semantic enrichment, and scene/scenario description generation that move beyond closed-set bounding boxes, using foundation models to scale annotation and cut manual labeling cost.
• Generate reasoning-grounded labels — produce language-grounded reasoning and chain-of-causation style annotations, temporally aligned to ego-motion and trajectories, to support VLA training and explainable driving behavior.
• Mine and curate the long tail — surface rare, difficult, and high-uncertainty scenarios, and build curated datasets that measurably improve downstream VLM/VLA model metrics rather than simply adding volume.
• Close the data flywheel — define dataset schemas, quality metrics, and validation; track auto-labeling quality against model requirements; route model failures back into re-labeling and retraining loops.
• Partner with the end-to-end model team — co-define dataset specifications with VLM/VLA model developers, own the quality bar and delivery cadence, and operationalize a continuous dataset delivery loop into their training pipelines.
• Scale on cloud infrastructure — build distributed, reproducible pipelines using columnar data formats and distributed compute, with disciplined software practices, version control, and documentation.
• Lead and mentor — serve as project lead, guide less-experienced engineers, run design reviews, set coding and annotation standards, and drive alignment across team interfaces to the rest of the organization.
• Stay current — track the latest advances in multimodal models, auto-labeling, and end-to-end autonomous driving, and translate relevant research into production data systems.

Senior ML Engineer – VLM

Computer Vision Engineer

• Design, train, and evaluate deep learning models for segmentation, landmark detection, registration, and 3D anatomical modeling
• Develop AI supporting image-guided interventions including surgical planning, navigation, and tumor ablation
• Build image processing pipelines for DICOM/NIfTI volumetric data across X-ray, CT, and MRI
• Analyze large clinical imaging datasets and improve data quality
• Design reproducible experiments and evaluate performance with appropriate statistical methods
• Write production-quality, well-tested code and collaborate with engineering on integration
• Support V&V activities for regulated medical device development

AI/ML Engineer I – Medical Image Computing

• Design, develop, and deploy machine learning models and AI-powered applications
• Collaborate with cross-functional teams to identify and implement AI/ML solutions for client needs
• Build and maintain data pipelines, model training workflows, and inference systems
• Evaluate and integrate large language models (LLMs), generative AI tools, and emerging AI frameworks
• Monitor model performance, conduct testing, and iterate on improvements
• Document technical architectures, methodologies, and best practices
• Stay current with AI/ML research and translate advancements into practical applications

• Define and evolve enterprise AI architecture, standards, reference models and implementation guidelines to support consistent solution delivery across business functions
• Align AI initiatives to enterprise strategies, roadmaps, and business priorities
• Identify and prioritize AI opportunities that drive student success and operational efficiency
• Design scalable AI platforms and solutions across data, application, and technology domains
• Provide guidance on how AI, ML, and automation will improve current and future-state architectures
• Lead design and implementation of AI-powered solutions (e.g., LLMs, agents, automation, and analytics)
• Integrate AI capabilities with enterprise applications and data platforms
• Guide evaluation and selection of AI/ML models, frameworks, and vendors
• Establish reusable patterns, APIs, and integration approaches
• Establish governance practices covering model oversight, responsible AI usage, security controls, compliance expectations, and lifecycle management
• Establish AI governance, including responsible AI, security, privacy, and compliance (FERPA, HIPAA, GDPR)
• Establish lifecycle management practices (MLOps/LLMOps, model monitoring, drift detection, retraining)
• Implement performance, bias, and risk monitoring frameworks
• Ensure secure AI architecture (e.g., data protection, isolation, adversarial defense)
• Partner with business, product, data, and engineering teams to provide AI solutions
• Provide architectural guidance to architects and delivery teams
• Facilitate workshops, POCs, demos and AI adoption projects identifying viable opportunities where AI can be a positive differentiator
• Mentor teams on AI best practices, patterns, and frameworks

Enterprise AI Architect

• Manage a team of Applied Scientists and Research Engineers, ensuring a high-performance culture.
• Build, mentor, and grow a high-performing team of applied scientists.
• Translate state-of-the-art AI research into scalable, production-ready solutions.
• Partner with Product and Engineering leaders to define and execute the AI-driven product roadmap.
• Oversee scaling of experiment and LLMOps platforms, balancing reusability and innovation.
• Explore emerging technologies, foster experimentation, and share impact and ideas to both technical and non-technical audiences.

Manager, AI and Applied Research

• Transform the Integrated Services Product Team Operating Model: Identify, redesign, and scale AI-enabled workflows across discovery, customer research, Product Requirement Document (PRD) roadmaps, prioritization, launch readiness, executive reviews, analytics, PMO, go-to-market, and post-launch learning.
• Build AI Agents, Automation and Infrastructure: Create, configure, test, and maintain AI agents, tools, workflows, and integrations using APIs, retrieval, orchestration, agent instructions, approved AI platforms, enterprise systems, and related technical patterns.
• Prototype, Pilot and Scale: Rapidly prototype with real users, validate workflow fit, identify failure modes, and move successful solutions from idea to prototype to pilot to scaled adoption, working through technical, operational, security, privacy, and change-management barriers.
• Create Reusable Capabilities and Playbooks: Build repeatable agents, templates, standards, workflows, and best practices for common product, PMO and GTM work, including customer insight synthesis and journey mapping, requirement generation, PRD review, competitive analysis, roadmap tradeoffs, launch planning and execution, risk reviews, business case development, and executive communication.
• Measure Impact: Define and track outcomes tied directly to business and customer value creation, including adoption, time saved, cycle-time reduction, decision speed, employee experience, quality improvements, rework reduction, revenue generation and business or customer outcomes from AI enabled workflows.
• Drive Adoption and Behavior Change: Train teams, coach leaders, create champions, document best practices, and make AI-enabled workflows easy, safe and useful enough to become the default way of working.
• Establish Quality, Governance and Trust: Partner with security, legal, privacy, IT, PMO, go-to-market and engineering to ensure workflows are secure, compliant, auditable, explainable, and appropriate for enterprise use.
• Design Human-In-The-Loop Systems: Define where AI should act independently, where humans must review or approve, and how teams should manage risk, quality and accountability in AI-enabled workflows.
• Increase Leverage: Identify and automate repetitive, manual, duplicative, or low-value work so teams can spend more time on customer insight, product judgment, and execution.
• Evaluate Emerging AI Patterns: Stay current on AI agent frameworks, tooling, security models, governance practices, workflow automation approaches, and enterprise patterns, then translate the most practical opportunities into Ford use cases.

Director, AI Transformation Architect

• Design and implement enterprise semantic models and certified KPI layers to ensure trusted, reusable business metrics.
• Build AI-safe data abstraction layers that prevent metric recomputation and ensure consistency across analytics and AI use cases.
• Develop and enforce data contracts, metadata standards, and semantic governance frameworks across domains.
• Engineer scalable batch and real-time streaming data pipelines in a modern cloud environment (GCP preferred).
• Collaborate with AI/ML teams to design reliable grounding strategies for AI applications and agents.
• Implement metadata management capabilities including cataloging, lineage, observability, and automated data quality checks.
• Apply DataOps principles including CI/CD, automated testing, and deployment automation for data products.
• Support domain-oriented and microservices-based data architecture patterns.
• Mentor engineers and promote best practices in semantic modeling, governance, and AI-ready platform design.

Senior AI Context Engineer

• Develop and train machine learning models for learned behavior systems, including approaches such as behavioral cloning, imitation learning, and reinforcement learning.
• Implement production-quality ML code to support model training, evaluation, and inference within the autonomy stack.
• Analyze model performance, identify failure modes, and propose improvements to increase robustness and generalization across different scenarios.
• Contribute to model training pipelines and data workflows, organizing behavior datasets from simulation, fleet logs, and vehicle data.
• Collaborate with simulation, validation, and autonomy engineering teams to test and evaluate learned behavior models across diverse driving environments.
• Help integrate learned behavior models into simulation and test workflows to enable faster iteration and more comprehensive validation.
• Support the development of tools and infrastructure that improve experimentation speed, repeatability, and model iteration.
• Contribute to technical discussions regarding model architectures and training strategies within the team.

Machine Learning Engineer II

• Identify engineering pain points and propose/design/implement solutions that are reliable, scalable, and maintainable
• Influence the team’s technical roadmap
• Evaluate new tools and technologies through PoCs
• Ship improvements to our AV development toolchains and services which have a measurable and direct impact on engineering productivity and our core company metrics
• Drive software engineering best practices within your team, and create tooling which encourages these
• Help steer the engineering culture on the team
• Guide the team to find the right balance between delivering impact and addressing technical debt
• Mentor and grow engineers on the team
• Set the example for high levels of accountability
• Execute and deliver impact both individually and through the team
• Set strong boundaries when selecting external requests and pushing back on requests that do not align with our team vision

Staff AI/ML Engineer, Developer Productivity

• Build and maintain shared infrastructure: secure web app hosting infrastructure, MCP/API standards, secret/key management, model selection, guardrails, and a centralized pattern library that all builders across the organization can pull from.
• Own the review, tracking, and advisory process for all corporate AI and automation builds — MCP servers and tool configurations, Claude Skills, API integrations, and agentic workflows — before deployment.
• Partner directly with AI specialists across departments to identify, scope, and build AI and automation solutions that meet high standards for cost, security, and scalability security before they ship.
• Define and maintain the secure intake path for AI and automation requests across the organization, replacing ad-hoc builds and shadow IT with a governed, cost-effective, and scalable process.
• Contribute to the organization’s AI cost efficiency by monitoring, alerting, and setting thresholds on AI spend across usage-based tools and platforms.

AI & Automations Lead

• Integrate conversational AI assistant features into software applications, owning the experience end to end
• Design, implement, and evaluate tool/function calling so AI assistants can reliably invoke backend capabilities and return correct, useful results
• Write, test, and refine prompts; evaluate and improve the accuracy and effectiveness of LLM and tool-calling behavior
• Build, train, and evaluate machine learning models for classification, prediction, and simulation tasks
• Collaborate with software engineers to integrate AI capabilities into applications and services
• Use Git for version control and contribute to shared team repositories
• Document your work and communicate results clearly to technical and non-technical stakeholders
• Support deployment, testing, and iteration of AI capabilities alongside the broader engineering team

Machine Learning Engineer jobs in Missouri

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Senior AI/ML Researcher

AI Workflow Engineer, Developer

Software Engineer – AI, CoCounsel Forward Deployed Engineering

Senior Machine Learning Engineer – 3D Perception

AI/ML Engineer

Senior Software Engineer – Applied AI

Product Engineer – AI

Machine Learning Engineer

Staff Machine Learning Engineer

Senior ML Engineer – VLM

AI/ML Engineer I – Medical Image Computing

AI Engineer

Enterprise AI Architect

Manager, AI and Applied Research

Director, AI Transformation Architect

Senior AI Context Engineer

Machine Learning Engineer II

Staff AI/ML Engineer, Developer Productivity

AI & Automations Lead

AI Engineer