Top Machine Learning Jobs in Portland, ME

Design, develop, and maintain high-performance, secure Java backend services and APIs. Ensure reliability, scalability, and cost-efficiency across microservices, databases, messaging systems, and capacity planning. Troubleshoot, profile, benchmark, and optimize systems (including ML applications) and collaborate with Kazakhstan stakeholders.

Design, develop, and validate functionally safe software for robotic perception systems. Build safety-critical architecture across Linux and real-time operating systems, create testing and validation frameworks for edge cases, collaborate with machine learning, hardware, and core software teams, and mentor junior engineers on safety-critical coding practices.

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.

Develop and deploy mineral exploration instruments, sensor systems, data acquisition methods, and proprietary software. Integrate geospatial, geophysical, geochemical, geological, and geographic data; build statistically rigorous and physics-based models; identify ore-forming anomalies; and generate 2D and 3D exploration predictions. Collaborate with data scientists, engineers, geoscientists, vendors, and external partners to design field programs, deploy sensors, reduce uncertainty, and guide exploration decisions.

The Machine Learning Engineer will build data-intensive products, deploy ML models, analyze datasets, and collaborate with product teams to enhance Nextdoor's platform.

Provides strategic legal counsel on clinical trials, commercial biopharma services, healthcare regulations, privacy, data protection, cybersecurity, and EU employment law. Advises on clinical documentation, data transfers, privacy assessments, AI and digital health initiatives, workforce matters, contracts, vendor agreements, and regulatory compliance. Partners with senior leadership, business teams, HR, compliance, privacy, clinical, technology, and outside counsel to manage risk and support compliant operations.

Lead and scale MEDHOST’s software engineering organization, overseeing product delivery, modernization, security, reliability, scalability, and operational performance. Develop technology strategy and architecture roadmaps while driving AI-first transformation across products, engineering workflows, and enterprise operations. Establish performance metrics, govern technology investments, manage strategic initiatives, and advise executives on emerging technologies, risks, and opportunities. The role also requires implementing generative AI, machine learning, and AI governance within healthcare software environments.

Own and grow a Southeast or Mid-Atlantic commercial territory targeting companies with 500–2,500 employees. Responsibilities include prospecting, pipeline development, new business acquisition, upselling and cross-selling, multi-product deal closure, forecasting, territory management, executive presentations, negotiation, industry events, and collaboration with internal and channel teams. The role requires communicating cybersecurity and identity-access solutions to business leaders while consistently achieving sales quotas.

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.

Designs, builds, scales, and operates distributed CI/CD services supporting millions of builds, tests, and releases. Develops code for Azure-based infrastructure spanning virtual machines, containers, and custom lab environments. Responsibilities include requirements analysis, architecture and design documentation, estimation, debugging, optimization, operational playbooks, on-call incident response, monitoring, reliability improvements, and mentoring engineers.

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.

Owns the end-to-end product strategy and execution for AI-powered systems. Defines requirements, evaluation frameworks, quality standards, and system behavior while balancing quality, latency, cost, reliability, safety, and user experience. Partners closely with ML, backend, and client engineering teams to identify failure modes, improve model performance, prioritize roadmaps, and deliver predictable, useful, trusted AI experiences.

Analyze and optimize digital partnership performance, growth, profitability, and customer acquisition economics. Design experiments, develop forecasts and performance models, build measurement frameworks, investigate anomalies, and provide actionable recommendations. The role requires translating ambiguous business questions into analytical solutions, communicating insights to technical and non-technical stakeholders, and establishing scalable analytical processes that support strategic decision-making.

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 product direction and quality of an AI-powered email application. Define AI and system requirements, translate model capabilities into product decisions, establish evaluation frameworks, and balance quality, latency, cost, safety, reliability, and user experience. Partner closely with ML, backend, and client engineers to identify failure modes, improve system behavior, prioritize execution, and build predictable, trustworthy AI experiences.

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 application engineering for an AI-powered email product across backend, mobile, and desktop platforms. Design reliable AI workflows involving orchestration, state, tool use, retries, fallbacks, and failure handling. Build APIs connecting AI capabilities to product experiences, make architectural trade-offs, establish observability and operational excellence, and lead a small team of senior engineers through hands-on technical mentorship and code development.

Own the end-to-end strategy, requirements, quality, and execution of an AI-powered email product. Partner closely with ML, backend, and client engineers to define system behavior, evaluate model performance, manage trade-offs across quality, latency, cost, reliability, safety, and UX, and establish feedback loops. Drive prioritization, specifications, experimentation, failure handling, and product quality in a technical zero-to-one environment.

Lead the architecture and development of reliable AI-powered email applications across backend, mobile, and desktop platforms. Design APIs and workflow systems supporting orchestration, state, tool use, retries, fallbacks, and failure handling. Lead a small senior engineering team, make architectural trade-offs, mentor engineers, and establish strong production practices including observability, monitoring, testing, scalability, security, and operational excellence. Partner with ML leadership to deliver robust, high-quality product experiences.

Lead the engineering strategy for an AI-powered email application across backend, mobile, and desktop platforms. Design reliable AI workflows with orchestration, state management, tool use, retries, fallbacks, and failure handling. Build APIs connecting AI capabilities to product experiences, lead a senior engineering team, make architectural trade-offs, and establish strong observability, monitoring, testing, reliability, and scalability foundations while partnering with ML leadership.

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.