Top Machine Learning Jobs in Portland, ME

Develop and refine machine learning models using Python, analyze datasets with MongoDB, and integrate preprocessing and data pipelines. Evaluate models, tune hyperparameters, benchmark performance, document experiments, and deliver data-driven recommendations. Collaborate remotely with cross-functional contributors to improve and operationalize machine learning solutions in cloud or enterprise environments.

Conduct coding-heavy machine learning research involving model development, training pipelines, inference systems, numerical methods, performance optimization, and validation. Build reproducible workflows in Python and command-line tools, implement model and data pipeline components, debug system failures, and verify correctness and performance. The contract is fully remote, flexible, and approximately 15 hours per week, with output-based compensation.

Evaluate AI model outputs on data science, statistics, machine learning, experimentation, and quantitative reasoning problems. Create expert prompts, datasets, and reference solutions; identify statistical and methodological errors; and provide structured feedback to improve model performance. The role requires Python, machine learning, statistics, data analysis, experimentation, Pandas, and data modeling expertise.

Lead system safety engineering for defense-oriented autonomous ground vehicles. Perform hazard identification, risk analysis, FMEA, FTA, and related assessments; define safety architectures, requirements, concepts, and verification activities; support testing and technical reviews; apply safety standards such as MIL-STD-882E; lead safety working groups; mentor engineers; and collaborate across hardware, software, AI, robotics, and systems teams.

Lead the strategy, roadmap, and execution of an AI-driven B2B SaaS platform for government contracting. Translate RFP and customer needs into prioritized product enhancements, collaborate with AI research and cross-functional teams, apply agile methodologies using Jira, and drive successful product launches and adoption.

Lead technical pre-sales for large Oil, Gas & Energy accounts, define and implement Databricks-based data and ML/AI strategies, build technical champions, mentor teammates, and drive enterprise adoption through technical leadership and value selling.

Own G2’s research agenda for evaluating AI agents in realistic business workflows. Design benchmarks, task environments, scoring systems, automated and human grading methods, and reliability analyses. Collaborate with engineering and product teams to ship evaluation capabilities, communicate findings to customers and researchers, mentor technical teams, and establish repeatable standards. The role requires hands-on LLM and agent evaluation experience, original research, statistical judgment, product delivery, and technical leadership.

Evaluate AI model outputs on data science, statistics, machine learning, experimentation, and quantitative problems. Develop expert prompts, datasets, and reference solutions; identify statistical and methodological errors; and provide structured feedback to improve model performance. The role requires Python, machine learning, statistics, data analysis, experimentation, Pandas, and data modeling skills.

Lead product strategy, roadmap execution, and measurable outcomes for an AI-enabled refinance title decisioning platform. Translate strategy into requirements, user stories, and acceptance criteria; prioritize initiatives against OKRs; and partner with engineering, data science, operations, sales, and marketing. Monitor product health, accuracy, costs, service levels, and performance through metrics and dashboards. Manage stakeholders, customer onboarding, continuous improvement, and automation opportunities across a regulated real estate workflow.

Own and scale Tilt’s credit card acquisition through affiliate partnerships, paid search, and paid social. Lead channel strategy, partner launches, targeting, experimentation, and optimization while collaborating with credit, data science, product, engineering, and marketing. Use SQL and Python to evaluate funnel performance, unit economics, approvals, activation, spend, and credit outcomes. Shape acquisition policies and application experiences to drive profitable growth.

Leads the strategy, technical direction, and delivery of insurance data science across pricing, underwriting, cost modeling, demand modeling, and growth. Oversees predictive modeling standards, model lifecycle governance, engineering collaboration, regulatory alignment, and production deployment. Builds and develops a high-performing data science organization while partnering with actuarial, product, underwriting, pricing, and executive leadership to connect analytical work with profitability and business outcomes.

Lead the strategy, roadmap, launch, adoption, and ongoing improvement of an AI-powered clinical data insights platform. Partner with Data Science, Engineering, and application teams to create reusable capabilities, define platform metrics, prioritize investments, evaluate build-versus-buy decisions, and align senior stakeholders across clinical, technical, and business objectives.

Designs and governs enterprise data integration and digital platform architecture. The role establishes scalable ingestion, transformation, interoperability, cloud, governance, security, and data quality frameworks for customer data. It integrates healthcare and enterprise systems using HL7, FHIR, DICOM, APIs, and modern data architectures, while enabling analytics, machine learning, and decision intelligence. The architect provides cross-functional technical leadership, advises customer onboarding initiatives, partners with Product and Engineering, and mentors technical teams. The position requires 50% travel.

Designs, deploys, and maintains AI and machine learning solutions, including generative AI, LLM, RAG, and conversational AI applications. Owns agentic engineering infrastructure, application guardrails for patient-care workflows, data pipelines, analytics, model governance, monitoring, and cloud deployment. Provides architectural leadership, improves engineering throughput, ensures privacy and compliance, and translates business needs into scalable AI solutions.

Lead product positioning, go-to-market strategy, launches, customer insights, competitive intelligence, messaging, sales enablement, and thought leadership for AI-driven cybersecurity and authentication products. Translate complex AI and machine learning technologies for technical and business audiences, manage marketing plans and budgets, support customer case studies, and mentor a high-performing product marketing team. Partner closely with Product, Engineering, Sales, Marketing, and executive leadership.

Coordinates strategic AI programs across engineering, product, legal, risk, compliance, security, HR, and business teams. Develops execution plans, governance frameworks, status reporting, risk registers, and executive communications. Supports AI platform and agent deployment initiatives, AI policy development, enterprise training, stakeholder alignment, and adoption. Requires strong program management, AI technology understanding, organizational skills, and experience managing complex cross-functional initiatives.

Leads the scientific direction and technical roadmap for a permanent magnet research vertical. Responsibilities include guiding computational materials discovery and AI modeling, developing manufacturable magnet formulations, building and mentoring multidisciplinary teams, managing intellectual property, coordinating national laboratory and industry partnerships, and connecting simulation with experimental validation. The role also supports commercial licensing, external scientific communications, publications, and federally funded research programs.

Build production-ready agentic AI capabilities focused on monitoring, guardrails, evaluation, security, quality, and human-agent collaboration. Perform machine learning tasks including exploratory data analysis, feature engineering, and model fine-tuning. Translate research into scalable software, rapidly prototype features, collaborate cross-functionally, and explore emerging agentic technologies.

Own the strategy, roadmap, and success metrics for AI security capabilities across the Pathfinder platform. Serve as the internal expert on agentic AI threats, identity security, and emerging attack techniques. Define requirements for securing AI agents, MCP servers, and orchestration frameworks; conduct market and competitive research; lead design partner programs; coordinate cross-portfolio product work; and support analyst, customer, marketing, and pre-sales engagements.

Owns a portfolio of NovaOS capabilities from concept through delivery, adoption, and continuous improvement. Manages backlogs, roadmaps, prioritization, requirements, user stories, release planning, and product metrics. Partners with stakeholders, Engineering, AI, UX, Data Science, and Architecture teams to deliver enterprise AI-enabled products aligned with governance, Responsible AI, and business objectives.

Leads product strategy, roadmap, pricing, security product direction, AI governance, integrations, regulatory capabilities, customer discovery, launches, and product team development. Partners with Engineering, Research, Sales, Marketing, Finance, and executives to improve retention, expansion, win rate, margin, and enterprise adoption. Owns detection quality, deployment models, secure-by-design requirements, compliance certifications, competitive positioning, and board-level product communications.

Own and improve ClickUp’s voice AI systems, including real-time speech-to-text, audio processing, context-aware transcription, LLM post-processing, mention resolution, formatting, and voice-to-action workflows. Evaluate and integrate ASR models for accuracy, latency, and cost, while collaborating across product and platform teams to deliver voice and multimodal assistant features across desktop, mobile, web, and browser extension products.

Leads enterprise CrowdStrike deployments, architecture, compromise assessments, threat hunting, SOC modernization, detection engineering, cyber analytics, Purple Team exercises, and incident response. Advises CISOs and executives, manages cross-functional teams, designs global 24/7 SOCs, and coordinates breach containment and recovery. Requires deep CrowdStrike expertise, cybersecurity leadership, scripting, network and detection engineering, and executive communication skills.

Leads AI governance and assurance engagements, designing policies, controls, operating models, risk assessments, and compliance programs. Evaluates AI security, model governance, third-party solutions, and control effectiveness against frameworks including NIST AI RMF, ISO 42001, and the EU AI Act. Collaborates with security and engineering teams to test threats such as prompt injection, model manipulation, data poisoning, and leakage. Advises executives and clients, develops methodologies and proposals, researches emerging AI risks and regulations, and supports business development.

Designs and maintains scalable data models, semantic layers, ETL/ELT workflows, data-quality frameworks, and governed analytics products for commercial operations. Owns data solutions from requirements through deployment, monitoring, documentation, and support. Partners with technical and business stakeholders to deliver trusted reporting, dashboards, scorecards, productivity measurements, and AI-enabled decision support.