Top Senior Machine Learning Jobs in Portland, ME

Develop applied AI research prototypes and systems for agentic marketing applications, focusing on generative image and video, brand asset creation, data workflows, and autonomous evaluation. The role combines probabilistic and quantitative reasoning, backend system architecture, product intuition, experimentation, and customer-focused product development. Responsibilities include building proofs of concept, improving AI-generated media realism and brand alignment, and architecting agentic systems.

Design and launch 0-to-1 AI-powered marketing tools for Hightouch’s Agents product suite. Responsibilities include rapid prototyping, customer research, synthesizing usage data and feedback, shaping product strategy, and collaborating with designers, engineers, and product managers. The role requires strong visual, UI, and interaction design skills, comfort with ambiguity, curiosity about AI, and rapid iteration in a remote-first environment.

Owns multistate U.S. property and casualty insurance product filings from drafting through approval, including AI model documentation, SERFF submissions, objection responses, and regulatory inquiries. The role supports carrier clients, monitors state requirements, develops scalable filing processes, and coordinates with data science, actuarial, product, counsel, regulators, and industry stakeholders. Requires strong technical writing, insurance regulatory expertise, independent judgment, and the ability to translate complex predictive modeling methods into regulator-acceptable documentation.

Manage complex customer issues, triage support activity, and coordinate escalations across Client Engagement, Product, Engineering, and project teams. Analyze tickets and feedback to identify trends, risks, process gaps, and product opportunities. Improve support operations through documentation, dashboards, workflows, routing logic, playbooks, and feedback loops. Serve as a customer advocate while supporting strategic initiatives and strengthening handoffs, prioritization, and operational decision-making.

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.

Leads the data science platform and machine learning strategy, building data marts, predictive models, MLOps infrastructure, and analytics capabilities. Analyzes healthcare claims, clinical, workflow, and operational data to risk-stratify patients, assess clinical program impact, support dashboards, and improve patient and provider engagement. Collaborates with clinical, operations, finance, product, executive, and technology teams while serving as a trusted analytics expert for a business workstream.

Design, build, and operate high-throughput, low-latency ad-serving infrastructure processing millions of requests per second. Optimize GPU-powered TensorRT inference, feature stores, model serving, benchmarking, experimentation tooling, and system reliability. Partner with machine learning engineers to productionize models and own systems through design, deployment, observability, capacity planning, incident response, and optimization.

Leads complex enterprise customer engagements as a technical authority for Databricks data and AI solutions. Drives architecture, platform adoption, consumption strategy, competitive positioning, and executive relationships. Designs large-scale data, streaming, migration, governance, and AI/ML solutions; orchestrates cross-functional teams; mentors other solutions architects; and contributes to technical thought leadership. Requires advanced coding, enterprise data architecture expertise, Databricks proficiency, strategic account leadership, and executive communication.

Lead product-focused data science and machine learning initiatives, including exploratory analysis, production model development, evaluation frameworks, generative AI, graph-based machine learning, recommendation systems, and personalization. Collaborate with engineering, product, and business teams to deliver high-impact solutions, influence strategy, and communicate insights to technical and non-technical stakeholders. The hybrid role requires four days per week in the Greater Boston area.

Senior legal executive leading institutional bank partnerships, complex commercial and program agreements, consumer lending regulatory strategy, data protection, intellectual property, and AI underwriting compliance. The role advises executive and cross-functional teams, manages bank due diligence and risk requirements, and translates legal and regulatory considerations into scalable business strategies.

Leads the creation and global scaling of an AI Data Operations practice. Responsibilities include defining service strategy, developing AI data and evaluation solutions, originating and closing client engagements, establishing quality and governance frameworks, building strategic partnerships, providing thought leadership, and enabling scalable managed-service delivery. The role combines AI expertise, consulting, sales, solution design, commercial leadership, and global operations.

Design and build a production-grade AI memory and retrieval engine for personalized, privacy-preserving LLM experiences. Responsibilities include developing multi-tier storage and hybrid retrieval pipelines, memory extraction and classification, adaptive updates, evaluation frameworks, and reliability safeguards. The role researches advanced memory architectures, supports agentic workflows and tool integrations, collaborates with product management, and owns production operations. Strong software engineering, Python, NLP/ML, vector database, LLM orchestration, privacy, and RAG expertise are required.

Design and build production-grade AI memory systems, including retrieval pipelines, storage tiers, privacy filtering, memory extraction, adaptive updates, hybrid search, and evaluation frameworks. Ship and maintain LLM-powered features, diagnose RAG and prompt failures, implement agentic workflows and tool integrations, and ensure reliable degradation and fallback strategies. Collaborate with product management to define feasible memory-system capabilities while owning production reliability.

Designs and manages AWS cloud infrastructure and Amazon EKS platforms using GitLab CI/CD and Infrastructure as Code. Builds ETL workflows and scalable data pipelines for analytics and AI workloads, supports MLOps/AIOps and FinOps capabilities, troubleshoots Linux systems, and improves platform performance, security, scalability, and availability.

Provides senior functional and technical leadership for NetSuite and connected financial systems. Partners with Finance, Accounting, and technology teams to design processes, configure NetSuite, manage integrations, protect close-cycle continuity, support production incidents, implement controls, coordinate releases, and deliver testing and documentation. The role also leads automation initiatives, vendor engagement, solution lifecycle delivery, and mentorship while supporting accounting, compliance, reporting, and multi-entity financial operations.

Staff Software Engineer responsible for setting technical direction, architecting scalable distributed systems, shipping high-impact features, and building AI-powered tools for a virtual healthcare platform. The role includes mentoring engineers, partnering with product and clinical leadership, translating user needs into technical solutions, and guiding frontend and backend application development in a fast-paced startup environment.

Lead mainframe modernization and migration architecture for large global clients. Conduct discovery workshops, assess legacy environments, develop transformation and re-platforming solutions, present technical business cases, and advise executives. Partner with sales, consulting, operations, product management, and clients to create solutions, influence technology roadmaps, drive stability and innovation, and support complex opportunities. The role requires deep mainframe expertise across legacy languages, databases, operating systems, infrastructure, and modernization technologies.

Leads enterprise evaluation, governance, and implementation planning for emerging mental health therapeutics, including digital therapeutics, AI-enabled tools, neuromodulation, pharmacotherapies, and technology-enabled care models. Develops clinical protocols, workflows, provider training, safety criteria, outcome measurement, and adoption support. Partners with clinical, technology, research, quality, operations, and growth teams; evaluates vendors and evidence; monitors regulatory developments; and advances responsible innovation across the organization.

Leads end-to-end customer onboarding and implementation programs, translating customer goals into project plans, milestones, dependencies, risks, and success metrics. Coordinates Sales, Product, Engineering, Support, Customer Success, trainers, and subject-matter experts without performing hands-on technical implementation or training. Establishes scalable operational frameworks, manages cross-functional accountability, oversees technical readiness, tracks adoption, facilitates stakeholder communications, and ensures customers are prepared for go-live.

Lead the roadmap and delivery of an AI-native L1 IT operations agent. Partner with customers, design partners, engineering, and go-to-market teams to ship enterprise-scale capabilities, improve self-service adoption, and build trust through transparent agent experiences and feedback loops. Drive product strategy from discovery through launch in a fast-paced, ambiguous environment.

Develop client AI/ML solution architectures, assess technical feasibility, define requirements, estimate work, identify risks, and prepare proposal content. Design deployable inference solutions across FPGA, adaptive SoC, GPU, and AI accelerator targets while evaluating latency, throughput, power, memory, data pipelines, and production requirements. Present solutions, support business development, contribute to product and R&D initiatives, and undertake project work. Travel within North America for up to six weeks annually.

Shape company-wide machine learning strategy across credit, fraud, marketing, and growth. Build and improve consumer credit models, establish model governance and responsible modeling controls, and advance experimentation, causal inference, and business measurement. Provide technical leadership, mentor scientists, evaluate AI-assisted workflows, and partner with Legal, Compliance, Risk, Engineering, Product, and Finance to support reliable, compliant, and impactful financial decision systems.

Lead the design, development, deployment, monitoring, and improvement of interconnected customer lifetime value models covering conversion, retention, premiums, and claim losses. Serve as a hands-on technical leader, guide modeling strategy, evaluate experiments and business impact, partner with engineering and business teams, support production ML systems, establish technical standards, and coach other data scientists.

Develop and validate machine learning models, training pipelines, inference systems, and numerical methods using Python. Debug algorithmic and system-level issues, optimize memory usage and performance, design systematic tests, and build reproducible workflows. Review challenging ML engineering tasks, use coding agents in development, document technical findings, and deliver high-quality work independently within deadlines.

Develop and deploy machine-learned interatomic potentials for PFAS substitution. Responsibilities include generating and curating first-principles training data, building reproducible cloud and HPC workflows, training and validating ML force fields, running molecular dynamics and quantum chemistry campaigns, predicting physical properties, extending models to condensed-phase and process chemistries, and communicating model uncertainty and domain limitations.