Machine Learning Engineer Remote Jobs in Illinois (US)

This page tracks remote machine learning engineer openings that are location-eligible for Illinois.

Open jobs

2,518

Hiring companies this week

Salary sample

$50 - $175,000

Jobs added last hour

Strict location onlyShow closed jobs

Post Date

Minimum Salary

Experience

2518 Jobs

1370 Companies

Senior Manager, AI Lead – Semantic Layer

AAA

Proud to serve our 62+ million members, help travelers see the world and drive real change to improve road safety.

AI Engineer11 hours ago

Full Time Remote SeniorTeam 5,001-10,000Since 1902H1B Sponsor

Company Site LinkedIn

• Define and drive the vision, strategy, and roadmap for enterprise semantic data and data connectivity across business domains • Partner with business and IT leaders to identify high-value opportunities to connect and standardize data across systems • Position the semantic data product as a product, not just a technical asset, with clear customers, outcomes, adoption goals, and success metrics • Define and evolve the product’s value proposition, ensuring alignment to business priorities and measurable outcomes • Act as the general manager of the product, balancing user needs, technical feasibility, strategic priorities, operational constraints, and business impact • Define and manage data products aligned to key business domains, including clear ownership, users, and success metrics • Define target users and use cases for data products, ensuring alignment to specific decisions, workflows, and business outcomes • Partner closely with business customers to ensure data products support critical decisions, workflows, and operational outcomes • Drive adoption and usage of shared data assets across business units • Identify and prioritize use cases where connected data unlocks measurable business value (e.g., improved decisioning, automation, personalization) • Ensure solutions are aligned to real-world workflows and deliver tangible outcomes • Lead the development and evolution of enterprise ontologies, taxonomies, and data models that represent key business concepts and relationships • Establish a scalable semantic layer that enables reuse across analytics, AI, and operational use cases • Ensure alignment of definitions across domains to reduce fragmentation and duplication • Support both structured and unstructured data integration, enabling downstream AI and analytics applications • Ensure data is discoverable, understandable, and usable across teams through consistent definitions and relationships • Partner deeply with business domains to understand workflows, decision points, and data dependencies, acting as a product owner for how data supports those functions • Own the end-to-end product lifecycle from discovery and definition to delivery and iteration • Define product requirements, success metrics, and release plans • Continuously refine the semantic data product based on user feedback and evolving business needs • Define critical metrics to measure adoption, usability, and impact of the semantic data product • Supervise usage and continuously improve accessibility and value delivery • Evangelize the value of connected semantic data across the organization • Partner with data governance teams to establish standards, definitions, and data quality expectations • Promote consistency, reusability, and scalability of data assets across the enterprise

View details: Senior Manager, AI Lead – Semantic Layer

Alabama + 23 more

$168.3K - $224.3K / year

Apply

Senior AI Engineer

GitLab

Build software faster. The One DevOps Platform enables your entire org to collaborate around your code. We're hiring.

AI Engineer17 hours ago

Full Time Remote SeniorTeam 1,001-5,000Since 2014H1B No Sponsor

Company Site LinkedIn

• Diagnose business problems before building solutions • Own AI initiatives end-to-end, from stakeholder discovery and technical design through implementation, deployment, and iteration • Design, develop, and ship AI-powered solutions quickly • Improve organizational flow by building solutions that reduce bottlenecks, shorten lead times, and increase throughput • Integrate AI capabilities into existing systems and workflows using APIs, orchestration tools, and modern AI platforms • Be Customer Zero: leverage and showcase GitLab's AI offerings wherever possible • Partner closely with stakeholders across functions to understand the real constraints • Define and track success through business metrics, flow metrics, and feedback loops that make performance visible and actionable • Contribute to technical direction by evaluating tools, documenting patterns, and creating reusable foundations

GraphQL JavaScript Python TypeScript

View details: Senior AI Engineer

United States

$139.2K - $218.4K / year

Apply

Lead Agentic AI Engineer

Hexion Careers

We are an Equal Opportunity, Affirmative Action employer. All qualified applicants will receive consideration for employment without regard to gender, pregnancy, race, national origin, religion, age, sexual orientation, gender identity, veteran or military status, status as a qualified individual with a disability or any other characteristic protected by law. To be considered for this position candidates are required to submit an application for employment through our career site and be at least 18 years of age. Any offer of employment will be conditioned upon successful completion of a drug test and background investigation, as well as authorization for the Company to conduct additional periodic background checks as required by the Chemical Facility Anti-Terrorism Standards (CFATS) or regulations adopted by the department of Homeland Security or other regulatory agencies. A prior criminal record is not an automatic bar to employment, and the Company will conduct an individualized assessment and reassessment, consistent with applicable law, prior to making any final employment decision.

AI Engineer18 hours ago

Full Time Remote LeadTeam 1,001-5,000

Role Description Serve as the engineering lead for Agentic AI delivery across Supply Planning and Manufacturing — owning the design, development, and deployment of production-grade AI agent solutions. - Architect and build multi-agent AI systems using Azure AI Agent Service, AutoGen, Semantic Kernel, and/or LangChain/LangGraph — including orchestrator-executor patterns, tool calling, memory management, and agent coordination. - Implement the MCP to surface enterprise data as structured context for AI agents operating in supply chain and manufacturing workflows. - Build and deploy generative AI solutions on Azure AI Foundry — RAG-based knowledge agents, decision support for forecasting and capacity planning, and document intelligence for maintenance work orders and recipes. - Design and deliver AI copilots and topic-based agents using Microsoft Copilot Studio — enabling Supply Planning and Manufacturing teams to access insights and take action directly from Teams and Outlook. - Act as the AI delivery owner for agentic use cases — scoping business problems with stakeholders, defining agent capabilities and tool surfaces, prioritizing the roadmap, and driving adoption. - Apply emerging agentic AI patterns — including ReAct, Plan-and-Execute, reflection, and human-in-the-loop — for supply chain and operational use cases. - Partner with Supply Chain leadership, Demand Planning, Process Engineering, Maintenance Ops, and Plant teams to identify, scope, and deliver AI use cases that influence operational decisions. - Define and maintain AI agent governance — prompt versioning, tool auditing, evaluation frameworks, observability, and safety guardrails for production deployments. - Develop on Azure Databricks — PySpark and SQL against gold/platinum Delta tables, notebooks for transformation and feature work, and orchestration via Workflows. - Build and maintain Power BI reports and semantic models that serve as grounding data for AI agents and executive dashboards across Supply Planning and Manufacturing. - Own Supply Chain AI metrics alignment cadence — keeping priorities, status, and roadblocks visible to Supply Chain and Manufacturing leadership. - Mentor analysts and engineers on agentic AI design patterns, MCP, and AI delivery best practices. Qualifications - Master’s degree in Mathematics, Computer Science, Data Science, Information Systems, Engineering, or a related field with 5+ years of relevant analytics / AI experience, OR - Bachelor’s degree in Chemical, Industrial, Computer Science, or related fields with 8+ years of relevant analytics / AI experience. Requirements - Hands-on experience with the MCP — building or consuming MCP servers/clients; ability to expose enterprise data sources (databases, APIs, SharePoint, ERP) as MCP tools for AI agents. - Hands-on experience with multi-agent system design — designing and implementing multi-agent architectures; orchestrator-executor patterns, tool calling, memory management, and agent coordination using AutoGen, Semantic Kernel, LangChain/LangGraph, or Azure AI Agent Service. - Strong Python engineering skills — building production-grade AI agents and pipelines, including REST API integration, prompt versioning, evaluation frameworks, and observability for LLM-based systems. - Compulsory — must have hands-on experience with two or more of the following: - Azure AI Foundry (RAG pipelines, prompt flows, agent service) - Microsoft Copilot Studio (agents, topics, actions, Power Automate integration) - Microsoft 365 Copilot extensibility (plugins, connectors, Graph APIs) - Microsoft Power BI (DAX, semantic modeling, performance tuning) - Strong proficiency in Databricks (Python, SQL, Delta Lake, PySpark, notebooks). - Strong functional understanding of Supply Planning (S&OP, demand/supply planning, inventory, order management) and/or Manufacturing (plant maintenance, capacity planning, OEE). - Experience with SAP ECC / S/4HANA supply chain and manufacturing modules (MM, PP, SD, PM). - Ability to translate business problems into agentic AI solutions and communicate clearly to technical and executive audiences. - Strong collaboration and stakeholder management skills in cross-functional environments. Preferred Qualifications - Experience deploying AI agents in production — evaluation frameworks, safety guardrails, logging, and human-in-the-loop workflows. - Familiarity with agentic design patterns (ReAct, Plan-and-Execute, reflection, structured tool outputs). - Familiarity with knowledge graphs or graph databases (e.g., Neo4j) for agent reasoning and grounding. - Strong Power BI experience — semantic modeling, performance optimization, executive dashboard design. - Experience in chemicals, manufacturing, or process industries. - Experience with Palantir Foundry (pipelines, ontology, Workshop, AIP). - Exposure to MLOps on Azure (Azure ML, MLflow, Databricks Asset Bundles, CI/CD for analytics). - Experience designing operational KPI frameworks (MAPE, OTIF, service level, OEE, downtime). - Experience with containerization (Docker), version control (Git), and modern software engineering practices. Other We are an Equal Opportunity, Affirmative Action employer. All qualified applicants will receive consideration for employment without regard to gender, pregnancy, race, national origin, religion, age, sexual orientation, gender identity, veteran or military status, status as a qualified individual with a disability or any other characteristic protected by law. To be considered for this position candidates are required to submit an application for employment through our career site and, be at least 18 years of age. Any offer of employment will be conditioned upon successful completion of a drug test and background investigation, as well as authorization for the Company to conduct additional periodic background checks as required by the Chemical Facility Anti-Terrorism Standards (CFATS) or regulations adopted by the department of Homeland Security or other regulatory agencies. A prior criminal record is not an automatic bar to employment, and the Company will conduct an individualized assessment and reassessment, consistent with applicable law, prior to making any final employment decision.

View details: Lead Agentic AI Engineer

United States

Apply

Architect / Principal, AI ML Engineer

3Pillar Global

Building digital businesses, together.

Machine Learning Engineer19 hours ago

Full Time Remote LeadTeam 1,001-5,000H1B Sponsor

Company Site LinkedIn

• Define end-to-end architecture for AI/ML and Gen AI, Agentic AI, MCP systems including data pipelines, model training/inference, and MLOps • Serve as a strategic advisor / consultant to clients, leading solution design discussions • Architect scalable solutions using cloud-native AI tools (Azure ML, AWS SageMaker, or GCP Vertex AI) • Lead the integration of Generative AI into components / features leveraging LLMs into enterprise applications using APIs • Design retrieval-augmented generation (RAG) systems with vector databases • Guide teams on MLOps frameworks for CI/CD, model versioning, monitoring, and automated retraining • Evaluate emerging technologies and trends in AI, ML, Gen AI, Agentic AI space • Mentor technical teams and guide solution architects, AI/ML engineers • Ensure ethical and responsible AI practices

AWS Azure Cloud Docker Google Cloud Platform Kubernetes Microservices Python PyTorch Scikit-Learn Tensorflow

View details: Architect / Principal, AI ML Engineer

United States

Apply

Senior ML Engineer - Kimchi (LLM Inference Optimization

CAST AI

LLM Engineer23 hours ago

Full Time Remote Senior

Role Description Throughput. Latency. KV cache utilization. Move those three numbers in the right direction, and two things happen: customers get faster, cheaper inference, and our margins improve. That's the entire thesis of this role. Every kernel you tune, every quantization scheme you ship, every scheduler tweak you land shows up directly in a customer's p99 and on our P&L. This is a high-impact seat. It is also a high-autonomy seat as you'll be given the room to lead the technical direction of inference optimization at Kimchi, not execute someone else's roadmap. The problem: running LLMs in production is a moving target. The "right" model and serving configuration for a workload depend on: - Traffic shape - Sequence-length distribution - Batch dynamics - GPU SKU - Memory bandwidth - Quantization tolerance - A dozen other variables that shift week to week Most teams pick a model once, over-provision GPUs, and absorb the cost. Kimchi is the system that makes that decision automatically - continuously matching workloads to the most cost-efficient, best-performing LLM and serving configuration on a customer's infrastructure. We're building the optimization layer between the model and the hardware, and we need engineers who understand both sides deeply. Qualifications - 5+ years building real ML systems, with a portfolio that shows depth in inference or training infrastructure (not just model training notebooks). - Strong Python - production services, not scripts. - Hands-on experience with at least one of vLLM, SGLang, or TensorRT-LLM, and a working mental model of why an inference engine performs the way it does on a given GPU. - Fluency with quantization tradeoffs - you've measured quality regressions, not just compression ratios. - Comfort with distributed systems: collective communication, sharding strategies, and the practical failure modes of multi-GPU and multi-node setups. - A bias toward measurement. You instrument before you optimize, and you can tell the difference between a real win and a benchmark artifact. - Self-direction. This role comes with a wide mandate; you should be excited by that, not unsettled by it. Requirements - Push throughput. Continuous batching, speculative decoding, chunked prefill, kernel-level tuning across vLLM, SGLang, and TensorRT-LLM. Find the ceiling on each GPU SKU, then raise it. - Cut latency. Attack TTFT and TPOT separately. Profile, identify the actual bottleneck (compute, memory bandwidth, scheduling, networking), and fix it - not the bottleneck you assumed. - Get more out of the KV cache. Paged attention, prefix caching, eviction policies, cache reuse across requests, quantized KV. This is where a lot of the unrealized throughput lives, and it's an area you'll own. - Quantize without regressing quality. INT8, INT4, FP8 across weights, activations, and KV. Empirical work: measure quality on real workloads, not just perplexity benchmarks. - Shrink cold starts and memory footprint. Faster init, smarter weight loading, tighter memory accounting - the difference between a model that scales and one that doesn't. - Scale across nodes. Distributed inference topologies, network-aware placement, checkpointing strategies that don't bottleneck on storage or interconnect. - Set the technical direction. Decide what we benchmark, what we adopt, and what we build ourselves. Bring the team along with strong writeups and reproducible experiments. Benefits - Competitive salary (depending on the level of experience). - Enjoy a flexible, remote-first global environment. - Collaborate with a global team of cloud experts and innovators, passionate about pushing the boundaries of Kubernetes technology. - Equity options. - Get quick feedback with a fast-paced workflow. Most feature projects are completed in 1 to 4 weeks. - Spend 10% of your work time on personal projects or self-improvement. - Learning budget for professional and personal development - including access to international conferences and courses that elevate your skills. - Annual hackathon to spark new ideas and strengthen team bonds. - Team-building budget and company events to connect with your colleagues. - Equipment budget to ensure you have everything you need. - Extra days off to help maintain a healthy work-life balance. Hiring process - Screening call with Recruiter - Hiring Manager interview - Technical interview (system design) - Live coding - Culture Check interview with an executive As part of our standard hiring process, we would like to inform you that a background check may be conducted at the final stage of recruitment through our third-party provider, Checkr. Please note that Cast AI does not provide any form of visa sponsorship/work permit.

View details: Senior ML Engineer - Kimchi (LLM Inference Optimization

Worldwide

Apply

Staff AI Engineer

Syndesus

AI Engineer1 day ago

Full Time Remote LeadTeam 11-50

Role Description A well-funded seed-stage startup building the next generation of autonomous trading technology. You are building the intelligence layer on top of a purpose-built execution system for AI agents operating with real capital around the clock. What You'll Own - Learning System & RL Loop (~70%): - Design and implement the pipeline that connects live trade outcomes back to strategy improvement — signal quality, position sizing, timing, risk parameters. - Build the evaluation framework that separates genuine predictive signal from noise across agents, market conditions, and configurations. - Automate the strategy generation and testing cycle — the system should explore new configurations, validate them against real fleet data, and surface deployment candidates. - Detect regime shifts in market conditions and adapt fleet behavior accordingly. - Decompose every trade into its component drivers — signal quality, execution efficiency, exit timing — and wire those attributions back into strategy design. - Manage fleet-level coordination: concentration risk, capital allocation, and the exploration vs. exploitation balance. - Build the telemetry and data capture layer that makes all of the above possible. - Model & Inference Infrastructure (~30%): - Own the build-vs-buy decision on model hosting — evaluate proxied external APIs versus fine-tuned models on owned infrastructure and execute the chosen path. - Determine whether domain-specific training on trading data meaningfully outperforms prompted general-purpose models — then build the pipeline to act on that answer. - Optimize inference for the specific demands of a large autonomous agent fleet: concurrent agents, structured outputs, cost efficiency at scale. - Build the agent telemetry layer capturing every decision, signal score, and evaluation across the fleet. Qualifications - A production closed-loop system — model outputs drove real-world actions, outcomes were measured, and that feedback automatically improved the next decision. - Practical RL or online learning experience — you understand the challenges of learning from real-world feedback rather than static datasets. - Full-stack ML ownership — you build the pipeline, deploy the model, and own the outcome; Python primary, comfortable with Go or TypeScript in production services. - High-stakes sequential decision-making domain experience — finance preferred but not required; robotics, autonomous vehicles, game AI, ad bidding, and supply chain all transfer. Nice to Have - LLM fine-tuning and open-source model serving in production (vLLM, TGI, PEFT/LoRA). - Multi-agent system design. - Financial ML — signal generation, execution optimization, portfolio construction. - Onchain or DeFi experience. Interview Process - Fast — target first call to offer within two weeks. - Intro call with founders (60 min) — fit, motivation, your closed-loop experience. - Technical deep-dive (60 min) — open-ended system design, no right answer, evaluating how you think. - Paid trial project (1 week, part-time) if needed — real problem, compensated.

View details: Staff AI Engineer

Northern America + 2 more

$175K - $250K / year

Apply

Full Stack AI Engineer

CES Family of Companies

The CES Family of Companies is a collection of strong brands and businesses providing food equipment, supplies, service.

AI Engineer1 day ago

Full Time Remote SeniorTeam 51-200H1B No Sponsor

Company Site LinkedIn

• Design and implement AI/GenAI features across applications and SDLC workflows • Build AI solutions using platforms like Azure AI Foundry and LLM APIs • Develop agent-based workflows using frameworks such as LangChain, LangGraph, or Semantic Kernel • Implement RAG-based solutions and prompt engineering strategies • Leverage GitHub Copilot for AI-assisted development and productivity • Build full-stack applications using Python, JavaScript/TypeScript, and/or C# (.NET) • Develop APIs, backend services, and integrate with databases (SQL/NoSQL) • Ensure application quality through testing, monitoring, and observability • Collaborate with cross-functional teams (product, UX, data science) • Troubleshoot and optimize AI models, pipelines, and integrations

Azure JavaScript NoSQL Python SDLC SQL TypeScript .NET

View details: Full Stack AI Engineer

United States

Apply

AI Integration Developer

Apex Systems

Apex Systems, an IT staffing and workforce solutions firm, provides recruiting and staffing services to large and small companies alike. Founded in 1995 by thre

AI Engineer1 day ago

Full Time Hybrid Senior

Company Site

Job Description: Location: Lemont, Illinois (Hybrid) Employment Type: Contract Duration: 6+ month contract w/extension Role Overview An opportunity is available for an AI Integration Developer to design, enhance, and maintain AI-assisted workflows supporting scientific data. This role involves collaborating with scientific domain experts to understand data structures, implement dataset taxonomies, and orchestrate the migration of datasets for computational analysis using approved LLMs. The position focuses on automation, orchestration, and data structuring to build impactful solutions for research scientists. Key Responsibilities - Collaborate with scientific domain experts to understand datasets, locations, and desired outcomes. - Design and implement AI-driven data workflows, including data preparation, metadata structuring, and LLM-enabled orchestration. - Filter, sanitize, normalize, and prepare data for long-term storage and sharing. - Package data with AI-enabling metadata into HDF5 format. - Create mechanisms to migrate and inventory data for easy retrieval. - Build accessible and user-friendly interfaces and workflows for research staff. - Develop solutions using revision control and adhere to cybersecurity and IT standards. - Deliver a minimum viable product (MVP) within the first 90 days, followed by further refinement and feature additions. Required Qualifications Education: An Associate or bachelor’s degree in computer science, information technology, system administration, or a closely related field, or equivalent experience is required. Experience: A minimum of two years of experience in software development and/or system administration is required. Technical Skills: - Strong understanding of AI, LLMs, and prompt engineering. - Proficiency in Python scripting for automation and workflow development. - Experience building data pipelines or ETL-like processes, including data cleansing, normalization, and transformation. - Working knowledge of REST APIs. - Familiarity with Git or other revision control systems. - Experience with CI/CD pipelines (e.g., GitLab/GitHub). - Strong skills in Linux (RHEL/Debian) command-line administration. - Familiarity with relational databases such as MySQL or Oracle. Preferred Qualifications - Experience with AI workflow orchestration tools like Dify. - Familiarity with GUI development using Python or web-based applications. - Understanding of data workflows within scientific or research environments. - Exposure to LLM-based data processing or automation use cases. Work Environment This is a full-time, 40-hour per week contract position. The work schedule is primarily remote, with a requirement to be on-site between 20-40% of the time. The role involves working with government-furnished equipment and adhering to all computer protection program policies and security requirements. Compensation The pay rate for this position ranges from $50.00 to $80.00 per hour. Compensation may vary based on factors including but not limited to experience, qualifications, and geographic location. We are an equal opportunity employer and welcome applications from all qualified candidates regardless of race, color, religion, sex, sexual orientation, gender identity, national origin, disability, or veteran status.

View details: AI Integration Developer

Illinois

$50 - $80 / hour

Apply

Senior ML Engineer - Kimchi (LLM Inference Optimization)

CAST AI

LLM Engineer1 day ago

Full Time Remote Senior

View details: Senior ML Engineer - Kimchi (LLM Inference Optimization)

Worldwide

Apply

Senior NLP/LLM Engineer

Social Discovery Group

Top world’s largest social discovery company uniting 70+ brands with 500M+ users

LLM Engineer1 day ago

Full Time Remote SeniorTeam 1,001-5,000Since 20 yearsH1B No Sponsor

Company Site LinkedIn

• Conducting experiments with LLMs: Explore and analyze the effectiveness of different architectures and techniques (SFT, RLHF, Adapters, etc.) to enhance the capabilities of AI models. • Developing and implementing evaluation methodologies: Implement and maintain robust frameworks to assess the performance, accuracy, and user satisfaction of AI bots, including offline and online metrics. • Optimizing models for inference: Improve the efficiency and speed of AI models during inference to ensure they meet the performance and scalability requirements for production environments. • Collaborating with cross-functional teams: Work closely with data scientists, software engineers, and product managers to integrate AI solutions into the overall product pipeline.

Numpy Pandas Python PyTorch Scikit-Learn

View details: Senior NLP/LLM Engineer

Worldwide

Apply

2,508more opportunities are still waiting for you.Log in now and take your next shot before someone else does.

Top Stack For This Role + State

Python, JavaScript, TypeScript, Azure, PyTorch, Scikit-Learn

Eligibility Signals

Senior Manager, AI Lead – Semantic Layer at AAARemote listing with US location signal: Alabama, Arizona, California, Colorado, Connecticut, District Of Columbia, Florida, Idaho, Illinois, Iowa, Kansas, Kentucky, Louisiana, Maine, Montana, Maryland, Massachusetts, Michigan, Minnesota, Mississippi, Missouri, South Carolina, Virginia, Washington.
Senior AI Engineer at GitLabRemote listing with US location signal: United States.
Lead Agentic AI Engineer at Hexion CareersRemote listing with US location signal: United States.
Architect / Principal, AI ML Engineer at 3Pillar GlobalRemote listing with US location signal: United States.
Senior ML Engineer - Kimchi (LLM Inference Optimization at CAST AIRemote listing with US location signal: Worldwide.
Staff AI Engineer at SyndesusRemote listing with US location signal: Northern America, Americas, Latin America (LATAM).
Full Stack AI Engineer at CES Family of CompaniesRemote listing with US location signal: United States.
AI Integration Developer at Apex Systems

Related Role + State Pages

All US Remote Jobs Machine Learning Engineer Remote Jobs (US)US Remote Salary Guide Remote Interview Prep Remote Resume Tips US Remote Jobs by State (2026)US Remote Tech Stack Trends (2026)US Remote Job Market Weekly Snapshot Machine Learning Engineer Jobs in California Machine Learning Engineer Jobs in Texas Machine Learning Engineer Jobs in New York Machine Learning Engineer Jobs in Florida

Lead Agentic AI Engineer

Hexion Careers

Eligibility Signals

Senior Manager, AI Lead – Semantic Layer at AAARemote listing with US location signal: Alabama, Arizona, California, Colorado, Connecticut, District Of Columbia, Florida, Idaho, Illinois, Iowa, Kansas, Kentucky, Louisiana, Maine, Montana, Maryland, Massachusetts, Michigan, Minnesota, Mississippi, Missouri, South Carolina, Virginia, Washington.

Senior AI Engineer at GitLabRemote listing with US location signal: United States.

Lead Agentic AI Engineer at Hexion CareersRemote listing with US location signal: United States.

Architect / Principal, AI ML Engineer at 3Pillar GlobalRemote listing with US location signal: United States.

Senior ML Engineer - Kimchi (LLM Inference Optimization at CAST AIRemote listing with US location signal: Worldwide.

Staff AI Engineer at SyndesusRemote listing with US location signal: Northern America, Americas, Latin America (LATAM).

Full Stack AI Engineer at CES Family of CompaniesRemote listing with US location signal: United States.

AI Integration Developer at Apex Systems