Top Remote Machine Learning Jobs in Washington, DC

Build and productionize machine learning systems that convert complex documents into executable, verifiable logic. Responsibilities include developing parsing and transformation pipelines, experimenting with language models, designing datasets and evaluation metrics, creating adversarial tests, analyzing model failures, and implementing rule-based checks using logical reasoning and program analysis. The role requires close collaboration with founders and subject-matter experts to validate system outputs.

Leads large-scale AI transformation programs for Cresta’s strategic enterprise customers. Serves as executive sponsor and trusted advisor to C-suite stakeholders, manages matrixed implementation and engineering teams, oversees complex deployments, standardizes delivery governance, and drives measurable customer value, adoption, expansion, and advocacy. Partners with Sales, Customer Success, Product, and Engineering while connecting technical outcomes to business ROI.

Provides strategic and hands-on biostatistical leadership across pharmaceutical discovery, translational, biomarker, and early research programs. Designs studies, analyzes complex biological datasets, develops advanced statistical methods and reproducible R workflows, and translates evidence into portfolio decisions. The role collaborates with quantitative and scientific functions, supports publications and external partnerships, mentors statisticians, and advances data-driven analytical practices.

Own product strategy, vision, roadmap, discovery, prioritization, prototyping, delivery, launch, and post-launch adoption. Conduct user and market research, use AI tools for synthesis and experimentation, collaborate with Design and Engineering, manage backlogs, define metrics, and communicate product direction across stakeholders.

Own the full enterprise sales cycle for Oversee’s leisure travel business, from prospecting through closing. Build relationships with OTAs, agencies, tour operators, and vacation providers; develop pipeline; identify target segments and use cases; refine positioning and sales strategy; lead complex stakeholder processes; and partner with Product and Engineering to convert market insights into a repeatable leisure sales motion.

Own AgentSee’s sales and go-to-market strategy as the first dedicated sales leader. Personally close early enterprise opportunities, refine the ideal customer profile, positioning, pricing, packaging, and sales process, and translate AI capabilities into measurable business value. Build repeatable sales operations and playbooks, provide market feedback to Product and Engineering, and eventually hire and lead the AgentSee sales organization.

Leads the design, development, deployment, and operation of scalable machine learning solutions and high-performance microservices. Builds real-time inference systems using Triton and TensorRT, guides distributed cloud architectures, participates hands-on in coding and reviews, and mentors engineers. Drives technical strategy, operational excellence, system resilience, and business-wide innovation across generative AI, graph machine learning, and big data initiatives.

Lead a global product team building automation-first, AI-assisted investigation and agent-facing tooling for AML, KYC, EDD, and sanctions screening. Define roadmap, metrics, vendor strategy, and regulatory controls to improve investigation quality, cycle time, and operational efficiency while ensuring security, privacy, and auditability.

Develop and deploy NLP and machine learning models for classification, sentiment analysis, entity extraction, information retrieval, and user intent detection across large-scale legal, financial, news, and business datasets. Design scalable data science pipelines, evaluate modern AI algorithms and tools, translate business needs into technical solutions, and collaborate with product and engineering teams. Mentor junior scientists and contribute to model deployment, monitoring, maintenance, and development best practices.

Lead exploration and application of large language models and generative AI for enterprise contact-center automation. Translate research into high-performing, deployable ML systems; contribute across experimentation, implementation, and deployment; collaborate with engineering and product teams; optimize performance; and help shape team strategy, innovation, and professional growth.

Lead exploration and application of large language models and generative AI, translate research into deployable ML systems, and guide strategic direction. Develop solutions across experimentation, implementation, and deployment while collaborating with engineering and product teams to optimize performance and customer outcomes in contact center automation.

Lead production-grade ML systems: build data pipelines, training and evaluation workflows, and scalable GPU inference. Fine-tune and adapt large models (LoRA, QLoRA, SFT, DPO, distillation), optimize deployments for latency/cost/reliability, and collaborate with product and engineering to ship robust, observable model-driven features.

Leads the end-to-end execution layer for machine learning, including data pipelines, large-model training and fine-tuning, evaluation, inference, deployment, monitoring, and reliability. Designs scalable GPU-based systems, optimizes performance and cost, partners with research and application engineering, and translates model capabilities into measurable production improvements. Also provides technical leadership, judgment, and support for high-impact ML delivery.

Lead the end-to-end ML execution: build and operate data pipelines, training and evaluation systems, and scalable inference. Fine-tune and optimize large models (LoRA/QLoRA/SFT/DPO/distillation), ensure GPU efficiency, monitor production quality, collaborate with product and application engineering, and rapidly ship reliable, safe ML solutions that translate research into production.

Design, build, and operate large-scale ML systems across data, training, evaluation, inference, and deployment. Lead GPU-optimized training and inference pipelines, ensure reliability, performance, safety, and seamless integration into products. Own production deployments, monitoring, and cross-functional best practices to drive measurable model improvements and efficient research-to-production cycles.

Partner with healthcare clients to discover, design, and deliver production-grade AI/LLM solutions. Architect cloud-native systems, build and deploy ML/LLM applications, prototype rapidly, troubleshoot distributed systems, and lead client engagements to drive measurable business outcomes while collaborating across product, platform, and engineering teams.

Lead B2B activation marketing to drive patient starts from commercial referral channels by building scalable systems, attribution frameworks, and repeatable playbooks. Implement MarTech and data-driven strategies, run A/B and incrementality tests, automate campaign execution using AI/ML and LLM tools, and templatize activation campaigns while managing and growing a cross-functional team.

Lead technical project success for enterprise AI infrastructure engagements. Collaborate with engineers, clients, and account leaders to capture requirements, decisions, dependencies, risks, and tradeoffs. Track constraints involving security, compliance, data sovereignty, procurement, and restricted environments. Maintain project visibility, align deliverables with business objectives, resolve issues, support implementation-to-operations handoffs, and identify expansion and renewal opportunities. Translate complex technical concepts for engineering, business, and executive stakeholders.

Analyze business processes, data quality, and datasets to identify AI and automation opportunities. Translate business requirements into AI/ML use cases, collaborate on model development, evaluate model performance, and deliver dashboards and reports. The role also supports data validation, AI solution monitoring, emerging technology research, governance, compliance, responsible AI, prompt engineering, and generative AI initiatives while partnering with business, data science, engineering, and product teams.

Manage post-sales relationships with a portfolio of customers, primarily financial institutions, to drive adoption and business outcomes from BioCatch’s behavioral biometric solution. Responsibilities include strategic consulting, project coordination, risk and escalation management, customer presentations, quarterly business reviews, roadmap feedback, stakeholder communication, team coaching, statements of work, and identifying expansion opportunities with account managers.

Lead design and implementation of Headway's insurance systems (eligibility, pricing, matching, claims). Build data-intensive, ML-powered platforms and automation, mentor engineers, write high-impact code, and improve accuracy and reliability across state and payer complexity to scale insurance-covered therapy.

Leads business analysis, advanced analytics, KPI development, data modeling, reporting automation, and AI/ML initiatives supporting global data-center construction and operational excellence. Builds datasets, dashboards, performance metrics, forecasts, and scenario models; translates complex findings for executives; aligns technology solutions with business needs; and promotes data governance, documentation, process improvement, and analytics maturity across cross-functional teams.

Owns the roadmap, problem definition, business cases, prioritization, and delivery for a core fintech platform area. Partners with product owners, engineering, data science, marketing, sales, clients, and support to develop scalable, data- and AI-driven capabilities. Monitors adoption, time-to-value, and retention, supports go-to-market activities, incorporates customer feedback, and addresses regulatory and compliance requirements.

Design, productionize, and scale machine learning and optimization models, pipelines, libraries, and deployment platforms. Partner with data scientists, engineers, product managers, and business stakeholders to deliver ML capabilities, personalization systems, and reusable end-to-end workflows. Establish ML engineering best practices, troubleshoot data pipelines, mentor junior professionals, and help define BetterHelp’s ML platform strategy.

Manage enterprise metadata, business glossaries, data catalogs, and end-to-end data lineage for a federal case management modernization program. Configure governance and metadata platforms, integrate data sources and pipelines, establish standards and workflows, monitor metadata quality, support compliant data sharing, develop governance metrics, document procedures, and train data stewards and business users.