Top Senior Machine Learning Jobs in Portland, ME

Own strategic partnerships with CPG customers, driving measurable outcomes, platform adoption, user expansion, and retention. Analyze usage data and business context to identify risks and opportunities, develop success plans, lead executive and operational relationships, and advocate for customers internally. Collaborate with product, sales, marketing, and support teams while contributing to repeatable playbooks, thought leadership, and customer best practices.

Leads the vision, roadmap, and development of an AI-enabled Service Catalog platform. Combines product leadership with hands-on full-stack engineering across web applications, APIs, integrations, workflows, and AI features. Manages engineering and product strategy, defines success metrics, partners with architects and stakeholders, prototypes capabilities, and ensures scalable, secure, responsible solutions with human oversight.

Conduct applied research on scalable optimization and resource-allocation algorithms for large communication networks. Apply machine learning, reinforcement learning, integer optimization, graph-based methods, and metaheuristics to improve solution quality and efficiency. Collaborate with software engineers to integrate algorithms into production aerospace networking systems, evaluate performance at realistic scale, publish research, support patent applications, and document novel methods.

Manage the operational integrity of an automated group health insurance underwriting workflow. Review and approve RFPs, validate broker and external data, correct errors, monitor funnel and risk metrics, analyze quote-to-bind performance, triage inquiries, coordinate with sales and health plan operations, and maintain underwriting dashboards and reporting.

Design and productionize machine learning, deep learning, LLM, personalization, recommendation, search, and retrieval systems. Collaborate with product, engineering, and analytics teams to deploy AI solutions that improve customer experiences, business growth, and operational efficiency. The role requires driving projects from ideation through deployment and delivering measurable business impact in a fast-moving fintech environment.

Lead product marketing for Temporal’s AI initiatives, owning positioning, messaging, competitive intelligence, launches, GTM strategy, content, sales enablement, and market insights. Translate complex AI infrastructure, workflow orchestration, agent, LLM, and human-in-the-loop capabilities into compelling value for technical and business audiences. Partner with product, engineering, sales, demand generation, developer relations, and communications to drive adoption and shape product direction.

The Cloud Inference Engineer is responsible for building and maintaining a scalable LLM inference platform, focusing on distributed systems and machine learning technologies while collaborating across teams to optimize performance.

Leads AI/ML engineering teams across platform and delivery pods, managing employees and contractors while enforcing code, agent, review, and production-readiness standards. Partners on reusable templates, pipelines, and evaluation processes, ensures adoption across teams, manages contractor performance and conversion, and removes delivery blockers. The role requires hands-on credibility reviewing agent architectures, prompts, and evaluation suites, with experience operating as a player-coach in fast-moving organizations.

Owns the lifecycle of AI-powered analytics products supporting clinicians, from requirements and discovery through production and maintenance. Partners with data scientists, engineers, clinicians, and executives to define roadmaps, prioritize trade-offs, establish data contracts and SLAs, validate model results, track performance, and demonstrate ROI. Leads Agile scrum teams and drives cross-functional delivery in complex healthcare environments.

Develop and optimize machine learning and statistical models for fraud detection and anti-money laundering. Analyze large datasets to identify fraudulent patterns, build scalable data pipelines and features, evaluate model performance, and collaborate with engineering, product, and operations teams. Communicate insights to technical and non-technical stakeholders while maintaining data privacy and security compliance. The role offers significant ownership in building Oscilar’s early machine learning stack within a remote-first startup.

Own LanceDB’s product direction by shaping the roadmap with technical leadership, researching users and competitors, gathering continuous user insight, and driving cross-functional prioritization. The role partners closely with engineering as a technically respected peer, defines product changes for AI and data-platform users, uses metrics and experiments to guide decisions, and operates with high ownership in an ambiguous environment.

Advise enterprise clients on AI and data strategies, transformation roadmaps, operating models, governance, privacy, technology rationalization, and business cases. Lead discovery workshops, executive engagements, and art-of-the-possible sessions; prioritize use cases and connect technical recommendations to business outcomes. Build the advisory practice, develop offerings, manage multidisciplinary teams, mentor staff, strengthen client and partner relationships, and support successful distributed project delivery.

Develop new enterprise and upper mid-market business, manage client accounts, and serve as a trusted consulting advisor. Prospect and qualify leads, collaborate with technical teams to create proposals and close SOW contracts, identify business improvement opportunities, build channel partnerships, maintain CRM records, and support localized marketing activities. The role is remote within specified target markets, includes up to 10% travel, and has no direct reports.

Designs and maintains high-volume HL7 and FHIR ingestion pipelines, secure Epic and hospital integrations, data validation systems, and compliant healthcare data exchanges. Builds enterprise SaaS microservices using ReactJS, Python, and AWS services; optimizes distributed and hybrid-cloud environments; leads testing, code reviews, production troubleshooting, architecture documentation, and customer communications. Preferred experience includes Epic App Orchard, healthcare integration engines, clinical workflows, GenAI/ML, DevOps, data pipelines, and NoSQL.

Own and improve the end-to-end AI engine powering real-time voice drive-thru agents. Design conversational and order-taking logic, address turn-taking and noisy audio challenges, build evaluation infrastructure, and ensure accurate recommendations, substitutions, pricing, and POS integrations across restaurant brands. The role involves researching and deploying new AI techniques, training and productionizing machine learning models, and converting ambiguous quality issues into measurable improvements.

Tune AML and fraud detection models using customers’ live transaction data. Investigate missed cases, alert quality, pattern drift, and false positives; validate changes through backtesting and sample review; document regulatory rationale; and present model performance and business value to compliance, risk, and data science stakeholders. Partner with customer teams on account strategy and relay customer-specific or systemic findings to product and model functions.

Drive net-new enterprise customer acquisition from lead generation through contract closure. Build C-level relationships, develop account strategies, manage CRM pipelines and forecasts, negotiate complex contracts, and close large AI, machine learning, and data talent engagements. Partner with marketing, customer success, and talent teams to deliver customer outcomes and expand Factored AI’s enterprise footprint.

Consult as a fractional data scientist for hedge funds, private equity firms, and family offices. Lead data science initiatives: build infrastructure, develop and deploy models, create portfolio dashboards, monitor post-deployment performance, and partner with investment teams to turn data into actionable investment insights. Advise on vendor selection, data quality, technology stacks, KPIs, and governance.

Coordinates data platform incident response, triage, escalation, root cause analysis, problem management, post-incident reviews, and operational improvements. Maintains runbooks, documentation, metrics, dashboards, and stakeholder communications. Implements and monitors security controls, supports security incident response, vulnerability remediation, compliance audits, regulatory reporting, disaster recovery exercises, and cross-functional coordination across data, cloud, infrastructure, DevSecOps, application, and governance teams.

Develop and deploy predictive and causal models for personalization, retention, monetization, and product strategy. Design experimentation and measurement frameworks, analyze user and marketing behavior, quantify business impact, and translate complex findings into strategic decisions. Collaborate with Product, Engineering, Marketing, and Data Product teams to build scalable data solutions. Mentor peers, promote rigorous model validation and governance, and apply Python, SQL, Snowflake, dbt, Airflow, Spark, and AWS to production-ready data science initiatives.

Lead the design and implementation of large-scale AI and machine learning systems for Yahoo Search across multiple teams. Define data strategies, evaluation frameworks, and scalable production-serving approaches. Drive technical alignment among engineering, product, design, and research teams; guide adoption of large language models, vision models, generative AI, and agentic AI; and oversee data analysis informing strategic product decisions.

Drive enterprise sales growth across North American banking, credit card, and financial services accounts. Develop strategic account and business development plans, build customer and partner relationships, close complex multi-million-dollar technology deals, and engage C-level stakeholders. Represent the company at industry events, collaborate with commercial and leadership teams, manage pipeline forecasting and reporting, and apply MEDDPICC methodology. The role focuses on cybersecurity, fraud prevention, authentication, and SaaS or cloud solutions, with 50% domestic and international travel.

Own the technical side of deal progression for airline deployments: qualify opportunities, run demos and POCs, author integration and deployment architectures (PSS, APIs, data feeds, DNS, identity, networking), respond to RFPs/security questionnaires, scope SOWs and NFRs, and build reusable demo/reference assets to drive adoption and expansion while liaising between sales, engineering, and customers.

Lead the design, development, deployment, and governance of advanced analytics, machine learning, and AI solutions using large healthcare datasets. Responsibilities include data preparation, statistical and predictive modeling, model monitoring, explainability, visualization, stakeholder communication, and translating findings into clinical and operational improvements. The role also mentors data professionals, advises leaders, manages initiative priorities and risks, and promotes responsible AI and Agile practices.

You will tackle AI challenges, develop scalable software and collaborate with teams to enhance healthcare navigation using AI solutions.