Top Senior Machine Learning Jobs in Portland, ME

Drive MSSP partner growth across AMER by recruiting, onboarding, activating, and expanding strategic managed security service provider relationships. Develop territory recruitment plans, executive partnerships, joint business plans, and indirect routes to market. Help partners launch managed security services, generate pipeline, increase revenue, and accelerate time-to-revenue. Collaborate with sales, channel, marketing, services, and technical teams while managing forecasts, opportunities, executive reviews, and revenue objectives.

Lead AI-driven localization product initiatives from concept through production. Build strategic roadmaps, incubate machine-learning capabilities, partner with researchers and engineers, and deliver scalable tools for creators and content operations. Translate user needs into impactful products, communicate strategy to executives and nontechnical stakeholders, and use data, experimentation, and ML evaluation to prioritize work and measure outcomes.

Architect and operate North’s AWS and multi-cloud infrastructure, establishing IaC, CI/CD, security, observability, and disaster-recovery standards. Build AIOps capabilities including anomaly detection, predictive scaling, self-healing remediation, AI-assisted tooling, and cloud cost optimization. Collaborate with DevOps, SRE, Product Engineering, and InfoSec teams to deliver resilient, compliant, highly scalable platforms.

Own the end-to-end product direction and quality of an AI email application. Define AI and system requirements, translate model capabilities into product decisions, balance quality, cost, latency, reliability, safety, and user experience, and partner with ML, backend, and client engineers. Establish evaluation frameworks, quality standards, feedback loops, and failure recovery strategies while driving prioritization, execution, experimentation, and user trust in a zero-to-one environment.

Own the end-to-end product direction and quality of an AI-powered email application. Define AI and system requirements, evaluation frameworks, quality standards, and measurable outcomes. Partner closely with ML, backend, and client engineering teams to make trade-offs across quality, latency, cost, reliability, safety, and user experience. Drive prioritization, specifications, experimentation, failure-mode mitigation, and continuous iteration in a technical zero-to-one environment.

Own the end-to-end lead management lifecycle, including capture, enrichment, qualification, scoring, routing, sales handoff, disposition, and recycling. Establish governance, business rules, SLAs, controls, and performance metrics; optimize workflows and integrations; resolve data quality and lead-flow issues; and partner with Sales, Marketing, Data Science, IT, and Analytics. Manage and develop a small team while overseeing operational priorities, testing, documentation, and continuous improvement.

Lead the technical side of enterprise sales for autonomous systems and physical AI customers. Own discovery, architecture reviews, demonstrations, proofs of concept, evaluations, deployments, and expansions. Translate customer workflows into solutions, address technical objections, and collaborate with Product and Engineering. Build scalable sales engineering processes, technical content, evaluation frameworks, and operating models while supporting strategic automotive OEM and enterprise opportunities.

Lead product strategy, requirements, and roadmap development for Honeywell Forge cloud applications serving utilities. Own AI and analytics capabilities for grid operations, DER management, rate design, and utility programs. Conduct customer and market discovery, competitive analysis, and voice-of-customer research. Partner with solution architects, sales, customers, and engineering to prioritize and deliver B2B SaaS products that generate measurable utility operational value.

Lead product strategy, requirements, roadmap, and AI capabilities for Honeywell Forge cloud applications serving utilities. Conduct customer and competitive discovery, translate utility workflows into product investments, define commercial opportunities, and partner with engineering to prioritize and deliver cloud-native B2B software. The role focuses on AI for grid operations, DER management, rate design, and utility program performance while ensuring measurable operational value.

Own the global consumer engagement and personalization domain across digital channels. Assess current capabilities, define multi-year strategy and roadmap, recommend personalization infrastructure investments, and translate priorities into shipped products. Partner with engineering, data science, design, marketing, legal, and regional teams to deliver scalable experiences. Establish metrics, run cross-market experiments, optimize performance, and make global versus localized decisions. Influence executives, mentor product managers, and apply AI, identity resolution, and generative AI to personalization.

Manage the AI opportunity roadmap for Risk Services, prioritizing use cases, coordinating internal data science teams and external vendors, translating business needs into requirements, and supporting AI governance, compliance, explainability, auditability, implementation, adoption, and benefits realization. The role focuses on business outcomes and solution delivery rather than hands-on model development.

Own and develop SandboxAQ’s intellectual property function, including patent portfolio strategy, invention harvesting, prosecution, lifecycle management, outside counsel oversight, freedom-to-operate analyses, trademark clearance, and trade secret programs. Partner with scientists, engineers, and business teams to protect innovations, inform product strategy, support transactions, and build scalable IP processes in a fast-growing AI-focused company.

Manage a team of data scientists to develop fraud detection models, lead planning and collaboration with leadership, and communicate outcomes effectively while maintaining strong business intuition and technical leadership.

Manage paid media strategies and campaigns across search, social, display, and programmatic channels. Advise clients using integrated paid, organic, and analytics data; oversee campaign performance, keyword research, ad copy, landing page recommendations, and presentations. Build client relationships, mentor account associates, collaborate with SEO and data teams, and use AI to improve workflows and outcomes.

Develop and lead paid media strategies across search, social, display, and programmatic channels. Manage campaign strategy, performance analysis, testing, optimization, and integrated paid and organic recommendations. Consult with clients, present results, build relationships, mentor account associates, and identify data-driven growth opportunities. Use AI tools to improve workflows and contribute insights across the team.

Leads technical strategy and product adoption for major Retail and CPG enterprise accounts as Databricks’ primary technical voice. Partners with account executives, guides customers through data and AI transformations, builds technical champions, aligns solutions to business strategies, and mentors team members. Requires extensive pre-sales, big data, cloud, data science, programming, and production data systems experience.

Build and lead Nebius’s federal affairs function in Washington, D.C. Develop policy strategy across AI regulation, trade, energy, infrastructure, and national security; establish bipartisan relationships with Congress and executive agencies; represent the company in federal initiatives and coalitions; assess regulatory risks; coordinate with legal, communications, and infrastructure teams; manage outside lobbying partners; and hire staff as the function scales.

Lead research, architecture, and production deployment of large-scale algorithms for budget pacing, bid optimization, and marketplace balancing in Yahoo’s DSP. Apply control theory, optimization, and machine learning to real-time systems, while establishing AI-augmented research and engineering workflows. Own multi-quarter initiatives, define evaluation frameworks, guide technical strategy, collaborate with product and engineering leadership, mentor scientists and engineers, and communicate complex research and architectural decisions to executives.

Lead the vision, strategy, roadmap, backlog, delivery, adoption, and lifecycle management of an enterprise AI platform. Manage three direct reports and collaborate across technology, cybersecurity, risk, legal, compliance, data, and analytics. Ensure secure, scalable, governed, and responsible AI capabilities while translating business and technical priorities into measurable product outcomes. Drive stakeholder alignment, platform adoption, continuous improvement, executive communication, and team development in a highly regulated environment.

Leads and manages an applied machine learning team developing fraud detection and identity verification models. Responsibilities include people management, coaching, code review, production ML systems, model lifecycle ownership, experimentation, deployment, monitoring, fraud research, cross-functional prioritization, and communicating results. The role remains hands-on while partnering with Product, Engineering, Risk, and leadership to drive measurable business impact and shape future product strategy using machine learning and AI.

Lead the strategy, design, deployment, and scale-up of AI-powered health products across the Global South. Own product roadmaps, execution, government-system integration, stakeholder adoption, partnerships, grant development, and portfolio expansion. Collaborate with engineering, machine learning, program, and domain teams to deliver sustainable, measurable public-health impact.

Lead post-sales success for enterprise SaaS customers, drive adoption, monitor account health and model performance, manage escalations, run QBRs, partner cross-functionally to ensure renewals and expansions, and deliver measurable business outcomes while advising on identity verification and fraud detection solutions.

Performs advanced physical and remote insurance premium audits for business clients. Reviews business records, interviews owners and managers, investigates exposures, documents findings, prepares written audit reports, schedules assignments, communicates results, and submits completed work for quality review. The auditor must manage deadlines independently, correct returned audits, maintain accurate records, and use Microsoft Office applications. This remote role requires reliable transportation, a valid driver’s license, strong analytical and communication skills, and at least five years of premium insurance auditing experience.

Leads the company’s adoption of AI, analytics, automation, and business intelligence. Builds AI-powered workflows, Excel solutions, natural-language data interfaces, dashboards, forecasts, predictive analytics, and automated reporting using Snowflake, SQL, Tableau, and related tools. Partners across business functions to identify opportunities, improve data quality, automate repetitive processes, and deliver scalable decision-support solutions. Establishes responsible AI practices and advances an AI-first analytics culture.

Lead data engineering and machine learning initiatives by designing, architecting, developing, and supporting GCP data pipelines. Create information architecture documentation, estimate and plan technical work, collaborate with project teams and business analysts, and translate requirements into technical designs. Develop Python-based ELT pipelines, optimize BigQuery SQL, support CI/CD, and provide technical leadership to projects and teams.