Top Entry Level Machine Learning Jobs in Minneapolis, MN

Analyze malware, binary files, customer detection tickets, and machine learning detections to improve detection efficacy and reduce false positives. Review product detections, assess binary legitimacy, address internal detection questions, and provide insights to data science teams. The role collaborates with technical account managers, incident response teams, data scientists, and malware researchers. Required capabilities include malware analysis, reverse engineering, Python scripting, binary fundamentals, threat assessment, research methods, operating systems, and AI-assisted workflow improvement.

Tenure-track Assistant Professor of Mathematics responsible for teaching introductory and upper-level undergraduate courses, advising students, conducting an active research program, mentoring undergraduate research, and contributing departmental service. Preferred research areas include applied mathematics, machine learning, artificial intelligence, optimization, operations research, and numerical methods. The position requires excellence in teaching, potential for sustained research, and commitment to an inclusive academic environment.

Defines and delivers scalable AI products, platforms, and capabilities across the enterprise. Establishes product vision, roadmaps, success metrics, and priorities while partnering with Technology, Data, Risk, Security, and business teams. Drives product discovery, agile delivery, adoption, governance, observability, operational effectiveness, and continuous optimization using analytics, OKRs, platform telemetry, and stakeholder feedback.

Build and deploy AI applications, agents, intelligent workflows, document-processing solutions, predictive analytics, and automation. The role spans AI use-case assessment, solution design, prototyping, evaluation, testing, production deployment, monitoring, governance, and responsible-AI controls. The engineer will integrate AI with enterprise systems, APIs, workflows, and data platforms while partnering with engineering, cybersecurity, governance, and mission teams in a federal environment.

Drives growth by identifying strategic partnerships with enterprise AI and machine learning companies. Develops business development strategies, builds relationships with senior decision-makers, negotiates complex deals, and collaborates with internal teams to implement partnerships. Requires strong AI/ML industry knowledge, executive communication skills, strategic thinking, and adaptability in a fast-paced startup environment.

Conduct hands-on research with frontier AI labs to evaluate model behavior, identify failure modes, and design interventions that improve reliability. Responsibilities include designing evaluations, building data pipelines and fine-tuning workflows, collaborating with internal and external stakeholders, and addressing challenges involving long-context reasoning, tool use, and real-time feedback loops.

Manages strategic client accounts for an AI data platform, serving as the primary customer contact and trusted advisor. Oversees data projects, SLAs, quality, and deliverables while coordinating engineering, operations, and project teams. Builds strong client relationships, addresses concerns, translates AI and NLP requirements into actionable plans, and ensures solutions meet customer goals.

Lead customer-centric product development and strategic initiatives for an AI platform. Responsibilities include understanding customer pain points, overseeing product and operations execution, managing accounts, building scalable systems and processes, coordinating with engineering, analyzing performance data, and driving product insights through market and competitive research.

Design, develop, and implement AI/ML models; analyze large datasets; integrate AI solutions into existing systems; apply MavLink for autonomous systems; collaborate across teams and improve ML development processes.

Designs, deploys, and optimizes GenAI data solutions for enterprise clients. Responsibilities include translating client needs into scalable workflows, creating annotation guidelines, delivering high-quality datasets, building validation and automation pipelines, and ensuring data accuracy, reliability, observability, and readiness for model development.

Advise the Department of Veterans Affairs on responsible AI strategy, governance, risk mitigation, lifecycle management, vendor evaluation, and operational adoption across healthcare use cases. Develop policies, playbooks, briefings, white papers, and decision memos for technical and executive audiences. Assess issues including bias, explainability, privacy, model drift, and patient safety while supporting federal AI compliance and enterprise transformation initiatives.

Run Discovered Labs’ founder-led content operation across LinkedIn and YouTube, from topic selection and editorial development through publication and performance analysis. Responsibilities include shaping founder content, coordinating external partners, repurposing long-form material, improving AI-supported content systems, and using engagement and pipeline data to guide editorial decisions. The role requires strong editorial judgment, hands-on execution, social content expertise, and the ability to work autonomously in an AI-forward, remote environment.

Annotate and label data accurately according to project guidelines, conduct quality checks, provide feedback on annotation tools and processes, collaborate with project teams, maintain efficiency, and identify issues for resolution. The role requires strong attention to detail, critical thinking, adaptability, communication skills, and comfort with repetitive computer-based work.

Review, label, and QA datasets for AI/ML training; maintain trackers, databases, and dashboards; follow annotation guidelines and SOPs; prepare and clean data; document processes; collaborate to resolve data queries while ensuring confidentiality and timely delivery.

Annotates, labels, categorizes, validates, and quality-checks structured and unstructured data for AI model training. Maintains dataset accuracy, consistency, documentation, and version control while collaborating with AI engineers, data scientists, and quality teams. The role also supports annotation workflow management, guideline development, calibration exercises, error identification, and continuous process improvement.

Designs, implements, optimizes, and administers generative and agentic AI solutions. Translates business requirements into AI workflows, oversees model deployment and performance, analyzes implementation data using Power BI, and coordinates responsible AI governance. Collaborates with engineering, security, data science, sales, and reporting teams while supporting users and maintaining AI platforms, cloud services, and low-code tools.

Evaluates and compares AI-generated responses in English and French for accuracy, relevance, reasoning, clarity, safety, cultural appropriateness, and overall usefulness. Applies detailed annotation guidelines consistently across varied content types, documents evidence-based decisions, meets daily productivity expectations, and participates in training, calibration, qualification, and quality reviews.

Lead technical project success for enterprise AI infrastructure engagements. Collaborate with engineers, clients, and account leaders to capture requirements, decisions, dependencies, risks, and tradeoffs. Track constraints involving security, compliance, data sovereignty, procurement, and restricted environments. Maintain project visibility, align deliverables with business objectives, resolve issues, support implementation-to-operations handoffs, and identify expansion and renewal opportunities. Translate complex technical concepts for engineering, business, and executive stakeholders.

Own the full B2B sales cycle from prospecting and qualification through negotiation, closing, account expansion, renewals, and post-sale relationship management. Build trusted customer partnerships, conduct site visits, represent the company at industry events, and collaborate with leadership, product, and design teams to incorporate customer feedback. The role is remote, focused on U.S. time zones, and requires significant domestic and international travel.

Two-year rotational leadership development program for recent data science graduates. Work across four six-month rotations applying data analytics, predictive modeling, ML, MLOps, and AI to solve business problems, deploy models, and translate insights with cross-functional teams. Program includes mentorship, development, networking, and potential transition to full-time D&IT roles.

Build and operate production AI capabilities across enterprise platforms, including agent runtimes, RAG, model routing, tool calling, guardrails, evaluation, monitoring, and cost optimization. Ship reliable, safe, measurable AI features; develop evaluation frameworks and quality controls; monitor production behavior; and collaborate with client-facing product, engineering, platform, backend, and security teams to deliver practical AI solutions.

Build and deploy generative AI solutions for creative enterprise workflows across image, video, 3D, vector graphics, and design. Partner with customers to identify opportunities, prototype integrations, adapt multimodal models, integrate AI into production systems, diagnose technical issues, evaluate solution quality, and develop reusable deployment patterns. Collaborate with research, product, engineering, and design teams to translate customer needs into scalable technical solutions.

Research and improve real-time conversational AI for voice drive-thru ordering. Responsibilities include advancing speech, intent, turn-taking, voice activity detection, and order-accuracy models; curating and generating data for rare scenarios; building statistically rigorous benchmarks; evaluating audio and conversational quality under real-time constraints; and translating research into production improvements with applied AI and backend teams.

Analyze large datasets, identify meaningful patterns, develop machine learning models, and deliver actionable insights for business decisions. Collaborate with cross-functional teams to understand data needs, communicate findings clearly to stakeholders, and stay current with advances in data science and AI technologies.

Design and deploy production-ready LLM pipelines and agentic workflows, automate large-scale multimodal dataset pipelines, monitor and improve agent performance, and integrate AI agents with 3D scene composition. The role also involves process documentation, incident response, and continuous improvement, with collaboration across the engine team and company founders.