716 Remote Machine Learning Engineer Jobs (September 2026)

Zillow's Agentic AI team seeks a Principal Machine Learning Engineer to innovate in AI-driven real estate solutions. This role involves developing multimodal agentic experiences, leading initiatives, and ensuring AI agent safety and trustworthiness. Ideal candidates should have extensive experience in ML model deployment, dialogue systems, and AI frameworks. The position is remote, offering flexibility to work from anywhere within the U.S., with competitive salary ranges varying by location.

Posted 3 weeks ago

About the Role There are over 5 billion users using basic applications today such email, notes, tasks, calendar and they're not AI-native. Our mission is to build proactive applications for anyone in the world, who are not used to complex prompting. We aim to bring intelligence to conversations, errands, organising and workflows, with minimal to no prompting. Our product focuses on achieving high reliability for long-running workflows, persistent context, and real-world task completion. We believe products will greatly reduce hallucinations Our objective is to organise anyone's life, allowing us all to spend time on valuable and meaningful things As Technical Lead, Machine Learning, you own the execution layer of our intelligence, turning research and model capabilities into reliable, scalable production systems. You will work across the model lifecycle: data, training, evaluation, inference, and deployment. This is a hands-on leadership role for someone who wants to operate at the intersection of research, systems, and product. What You'll Own Own the end-to-end ML systems powering our company, from data and training to evaluation, inference, and deployment. Build and evolve training and fine-tuning pipelines for large models. Design evaluation systems that measure capability, robustness, safety, and real-world product performance. Architect high-performance inference systems, optimizing latency, GPU utilization, memory, cost, and reliability. Build data pipelines and systems for high-quality real-world and synthetic training data. Establish reliable production infrastructure for deploying, monitoring, and continuously improving models. Partner closely with research and application engineering to turn model capabilities into product improvements. Make pragmatic technical trade-offs and rapidly iterate based on real-world performance. What We're Looking For Experience building and shipping ML systems used in production, not just research prototypes. Strong understanding of modern large-model training, fine-tuning, evaluation, and inference. Strong software engineering and systems fundamentals. Experience operating ML workloads at meaningful scale, particularly GPU-based systems. Strong technical judgment and the ability to navigate ambiguous problems independently. A bias toward experimentation, measurement, and shipping. High standards for correctness, reliability, and production quality. Outcomes Research and models reliably translate into production-ready solutions with clear performance and quality targets. ML pipelines, training loops, and inference systems are stable, efficient, and maintainable. Production issues are detected, debugged, and resolved quickly, minimizing user impact. Team members are supported, aligned, and able to deliver high-impact ML work with minimal friction. Iterations on models and systems are measurable, safe, and improve user experience over time. Tech Stack Python PyTorch / JAX GPU-based training and inference system Ideal Experience You have built or shipped real ML systems used by people, not just demos. You are comfortable working with large models and understanding their failure modes. You write strong, production-grade code and care about system correctness. How We Work We are a small, high-talent-density, hands-on team. Engineers have broad ownership and are expected to exercise strong judgment and execute independently. We make decisions quickly, work closely together, and balance speed with engineering fundamentals. We care less about process and more about building something exceptional. Interview process If there appears to be a fit, we'll reach to schedule 3, but no more than 4 interviews. Applications are evaluated by our technical team members. Interviews will be conducted via virtual meetings and/or onsite. We value transparency and efficiency, so expect a prompt decision. If you've demonstrated the exceptional skills and mindset we're looking for, we'll extend an offer to join us. This isn't just a job offer; it's an invitation to be part of a team that's bringing AI to have practical benefits to billions globally.

Posted 3 weeks ago

Join Angi as a Staff Machine Learning Engineer to advance the future of home services. You will lead the development of machine learning models, focusing on search ranking and user experience. Collaborate with cross-functional teams to deploy and evaluate models, driving innovation and mentoring junior members. With a rich history and global presence, Angi offers a dynamic environment where skilled professionals can thrive and contribute to impactful projects.

Posted 5 days ago

Phantom seeks a visionary Staff Machine Learning Engineer to lead the technical strategy for Growth and Engagement ML initiatives. In this fully remote role, you will manage complex ML models and collaborate with Product, Data Science, and Marketing teams to drive user acquisition and retention. You will define the technical roadmap, architect ML pipelines, and mentor engineers, ensuring a culture of excellence. With extensive experience in ML engineering and a focus on business impact, you will play a pivotal role in shaping the user experience and embracing the growing crypto market.

Posted 1 week ago

O Impacto Você já imaginou ter a oportunidade única de construir uma plataforma de Machine Learning do zero em uma das maiores fintechs do país? Aqui na Neon, nosso desafio não é apenas implementar modelos de dados prontos, mas sim desenhar a infraestrutura e a arquitetura que darão autonomia ponta a ponta aos nossos cientistas de dados. Você trabalhará em sinergia direta com os times de ciência de dados em projetos de altíssimo impacto (como prevenção a fraudes e novos modelos de cobrança), em um ambiente colaborativo. Se você ama engenharia de software e quer ver suas soluções escalarem de verdade no mercado financeiro, este desafio é para você! O Impacto Você já imaginou ter a oportunidade única de construir uma plataforma de Machine Learning do zero em uma das maiores fintechs do país? Aqui na Neon, nosso desafio não é apenas implementar modelos de dados prontos, mas sim desenhar a infraestrutura e a arquitetura que darão autonomia ponta a ponta aos nossos cientistas de dados. Você trabalhará em sinergia direta com os times de ciência de dados em projetos de altíssimo impacto (como prevenção a fraudes e novos modelos de cobrança), em um ambiente colaborativo. Se você ama engenharia de software e quer ver suas soluções escalarem de verdade no mercado financeiro, este desafio é para você! Suas Responsabilidades Analisar e especificar requisitos funcionais e arquiteturais da plataforma de Machine Learning, garantindo sua escalabilidade e robustez. Desenvolver e implantar modelos de ML em produção em larga escala, focando na excelência de design de sistemas. Dominar conceitos e frameworks de Infrastructure as Code (IaC) para automação e sustentação do ambiente de ML. Colaborar ativamente com os times de Ciência de Dados para integrar novos modelos à plataforma de maneira fluida e automatizada. Garantir a disciplina técnica e conformidade regulatória necessárias para o desenvolvimento de modelos em um ambiente altamente regulado (setor financeiro). Requisitos (O que você precisa ter) Sólida experiência em Engenharia de Software (foco no desenvolvimento e design arquitetural de sistemas complexos). Domínio avançado da linguagem de programação Python . Experiência real com implantação de modelos de Machine Learning em produção e escala ( ML Lifecycle / MLOps). Conhecimento prático e domínio de frameworks de Infraestrutura como Código (IaC), como Terraform . Diferenciais (O que te destaca) Experiência prévia em liderança técnica de engenheiros de ML. Conhecimento e vivência na utilização de Feature Store . Histórico profissional no setor financeiro (fintechs ou bancos), lidando com conformidade e governança.

Posted 1 week ago

Payabli is seeking a Staff Machine Learning Engineer to lead the technical direction for machine learning initiatives. This role involves enhancing existing models and developing new ones that optimize payments, reduce disputes, and improve overall financial operations. You'll closely collaborate with product, engineering, and risk teams, setting foundational practices for machine learning that will scale with the organization. Ideal candidates will have extensive experience in ML engineering and a strong background in deploying production models that drive business decisions.

Posted 2 weeks ago

BetterHelp is seeking a Staff Machine Learning Engineer to join its Data team, which plays a vital role in making mental health care more accessible. Since its founding in 2013, BetterHelp has become the world's largest online therapy service, relying on over 30,000 licensed therapists. In this role, you will design and implement scalable ML models while collaborating with cross-functional teams. Strong experience in machine learning techniques and software engineering is essential. You will also have the opportunity to mentor and guide junior team members, contributing to impactful ML initiatives.

Posted 2 weeks ago

ZipRecruiter is seeking a Staff Machine Learning Engineer to lead technical initiatives in machine learning and AI, reporting to the Director of Recommendation Systems. The role involves shaping the ML roadmap, optimizing algorithmic architecture, and translating research into production systems. This high-visibility position offers opportunities for mentorship and establishing best practices across the organization, focusing on recommendation systems and matching algorithms.

Posted 2 weeks ago

About the Role There are over 5 billion users using basic applications today such email, notes, tasks, calendar and they're not AI-native. Our mission is to build proactive applications for anyone in the world, who are not used to complex prompting. We aim to bring intelligence to conversations, errands, organising and workflows, with minimal to no prompting. Our product focuses on achieving high reliability for long-running workflows, persistent context, and real-world task completion. We believe products will greatly reduce hallucinations Our objective is to organise anyone's life, allowing us all to spend time on valuable and meaningful things As Technical Lead, Machine Learning, you own the execution layer of our intelligence, turning research and model capabilities into reliable, scalable production systems. You will work across the model lifecycle: data, training, evaluation, inference, and deployment. This is a hands-on leadership role for someone who wants to operate at the intersection of research, systems, and product. What You'll Own Own the end-to-end ML systems powering our company, from data and training to evaluation, inference, and deployment. Build and evolve training and fine-tuning pipelines for large models. Design evaluation systems that measure capability, robustness, safety, and real-world product performance. Architect high-performance inference systems, optimizing latency, GPU utilization, memory, cost, and reliability. Build data pipelines and systems for high-quality real-world and synthetic training data. Establish reliable production infrastructure for deploying, monitoring, and continuously improving models. Partner closely with research and application engineering to turn model capabilities into product improvements. Make pragmatic technical trade-offs and rapidly iterate based on real-world performance. What We're Looking For Experience building and shipping ML systems used in production, not just research prototypes. Strong understanding of modern large-model training, fine-tuning, evaluation, and inference. Strong software engineering and systems fundamentals. Experience operating ML workloads at meaningful scale, particularly GPU-based systems. Strong technical judgment and the ability to navigate ambiguous problems independently. A bias toward experimentation, measurement, and shipping. High standards for correctness, reliability, and production quality. Outcomes Research and models reliably translate into production-ready solutions with clear performance and quality targets. ML pipelines, training loops, and inference systems are stable, efficient, and maintainable. Production issues are detected, debugged, and resolved quickly, minimizing user impact. Team members are supported, aligned, and able to deliver high-impact ML work with minimal friction. Iterations on models and systems are measurable, safe, and improve user experience over time. Tech Stack Python PyTorch / JAX GPU-based training and inference system Ideal Experience You have built or shipped real ML systems used by people, not just demos. You are comfortable working with large models and understanding their failure modes. You write strong, production-grade code and care about system correctness. How We Work We are a small, high-talent-density, hands-on team. Engineers have broad ownership and are expected to exercise strong judgment and execute independently. We make decisions quickly, work closely together, and balance speed with engineering fundamentals. We care less about process and more about building something exceptional. Interview process If there appears to be a fit, we'll reach to schedule 3, but no more than 4 interviews. Applications are evaluated by our technical team members. Interviews will be conducted via virtual meetings and/or onsite. We value transparency and efficiency, so expect a prompt decision. If you've demonstrated the exceptional skills and mindset we're looking for, we'll extend an offer to join us. This isn't just a job offer; it's an invitation to be part of a team that's bringing AI to have practical benefits to billions globally.

Posted 3 weeks ago

Our Story Since 2023, our mission has been clear: to be the north star of patient equity. Every day, we strive to bridge the gaps in healthcare access and outcomes to ensure that every patient, regardless of background or circumstance, receives the care they deserve. As a member of our team, you'll be at the forefront of innovation, working alongside passionate individuals who share your dedication to creating best-in-class, patient-centric products in healthcare. Together, we're revolutionizing the way people understand their health and working with the world's top researchers to accelerate innovation. About Novellia Novellia is the first and only company that lets anyone in the U.S. gain access to nearly a decade of their health data in under 30 seconds — 100% free. All your health records, across every doctor, in one place, always up to date. We are the only patient-powered real-world data platform delivering comprehensive, patient-authorized longitudinal health insights to accelerate biopharma innovation. Unlike traditional RWD providers who deliver fragmented institutional data, we empower patients to access 20+ years of their health records, then transform these complete health journeys into fit-for-purpose datasets for evidence generation, regulatory submissions, and market access. We are growing 5x year over year, have raised close to $30M in funding, and are backed by tier-1 investors including Spark Capital, Khosla Ventures, and Bling Capital. Working with the world's top researchers, we turn health insights into life-changing action for millions of people around the world. About the role Most of what matters in a health record isn't in a structured field - it's in the note, the discharge summary, the pathology report, the scanned fax. Turning that unstructured clinical text into trustworthy, structured features is what makes a longitudinal health history usable for research, and it's one of the highest-leverage capabilities Novellia can own. You'll be our first ML hire, joining Platform Engineering and reporting to the Head of Platform Engineering, as technical owner of this multi-quarter effort. The interesting decisions are still open - what we extract first, how we know we're right, what a mature extraction pipeline looks like at our scale. There's no existing approach to inherit or defend. The work draws on two toolkits. Roughly 70% is applied ML on clinical text: entity extraction, classification, sequence labelling, annotation strategy, error analysis, calibration, and the evaluation discipline that tells you whether your numbers mean anything. Roughly 30% is LLM-based: prompt development, structured output, retrieval, and the evals and observability that keep generative approaches honest. Deciding which approach a given problem calls for is the most interesting part of the job, and that call is yours. We're looking for a leader in this seat: setting technical direction rather than waiting to be handed a problem. If this grows the way we think it will, leading the team we build around it is on the table. What you'll do Own the full lifecycle of extraction models - framing, data/annotation strategy, model selection, training/fine-tuning, evaluation, deployment, monitoring, retraining. Not a research seat, not a hand-off seat. Define what "accurate enough" means with clinical and customer-facing stakeholders, and build the evaluation harness that makes the answer defensible - the first deliverable, not a follow-up. Partner with Clinical Data Managers on curation design and own the technical half of QA/QC alongside them: which variables are extractable, how an instruction becomes a model spec, and the tooling/sampling/error analysis behind human-in-the-loop review. Build clinical NLP pipelines against messy real-world data and work with backend engineers to productionize what you build. Use LLMs with the same rigor you'd apply anywhere: versioned prompts, real evals, tracked cost/latency, known failure modes. Make extraction quality legible to non-ML colleagues, and treat de-identification, PHI handling, audit trails, and access controls as part of the modelling problem, not someone else's checklist. Help shape the roadmap around the problems you see - a mission and a close working partner, not a backlog. What we're looking for Healthcare or life sciences experience with real clinical data - clinical notes, EHR data, claims, registries, or similar. This one is not negotiable for us. 6+ years in applied ML, with models you personally took from problem statement to production and kept working - you know what degraded, how you found out, and what you did. Depth in applied ML on text: information extraction, NER, classification, sequence labelling, weak supervision, and the evaluation practice around them, including annotation guidelines and inter-annotator agreement you've had to act on. Practical, current experience with LLM-based approaches: prompt development, structured output, retrieval, fine-tuning where warranted, evals and observability for generative systems - enough to know where they help, and where they quietly don't. Strong engineering fundamentals in Python. Your work runs in production, not only in a notebook. Strong collaboration instincts across the ML boundary: you define problems with stakeholders before solving them, write clearly, and bring people along. A track record of solving problems rather than closing tickets. Self-directed, comfortable without a playbook, and comfortable being wrong in public when the evidence says so. Nice to have Fluency with clinical terminologies and standards: SNOMED CT, ICD-10, LOINC, RxNorm, CPT, FHIR Experience with HIPAA, SOC 2, de-identification methodology, or IRB and regulatory-grade data work Experience as an early or first ML hire Experience building or running human-in-the-loop annotation and QC operations at scale OCR and document-understanding experience on low-quality real-world documents Experience mentoring or leading ML engineers, or interest in growing that way What this role is not Not a research role. The bar is extraction quality in production, not publications. Not an LLM-wrapper role. If your instinct is that every problem is a prompt away from being solved, we'll frustrate each other. Not a large-team role yet. You'd be the first ML engineer in a small Platform Engineering function - breadth and influence, and fewer specialists to lean on. Not a role where someone hands you a clean labelled dataset. Building it is the job. Why this role is a good bet Ground-floor ownership of a capability with direct commercial weight, with influence over architecture, roadmap, and eventually hiring. Both halves of the modern ML toolkit in one seat, on a problem where the choice between them genuinely matters. A manager who treats process and people work as legitimate engineering work, and intends for this seat to grow. Health tech means the work has stakes - better extraction means higher quality research Benefits Perks Equity in Novellia Medical, dental, and vision coverage 401(k) Flexible time off Wellness stipend Up to 12 weeks of parental leave Don't meet every requirement? Studies show women and people of color are less likely to apply unless they meet every qualification. If you're excited about this role but your experience doesn't align perfectly, we encourage you to apply anyway - you may be the right fit for this or another role. U.S. Applicants Only

Posted 3 weeks ago

Angi is looking for a Senior Machine Learning Engineer to join their Data Science and Machine Learning team. This role involves developing advanced machine learning models to enhance their online marketplace, optimizing model deployment, and collaborating with cross-functional teams. The ideal candidate should have expertise in AI techniques, a strong understanding of machine learning frameworks, and the ability to mentor junior members. Angi values diversity and seeks individuals who can contribute unique perspectives to solve complex challenges in the home services industry.

Posted 5 days ago

Zillow is seeking a Senior Machine Learning Engineer to join its Rich Media Experiences team. This high-impact role focuses on developing AI-powered systems for transforming data into immersive customer experiences. The engineer will collaborate with applied scientists and software engineers, driving the development of reliable machine learning workflows and enhancing product capabilities. Candidates should have significant experience in ML systems, strong proficiency in Python, and a collaborative mindset. The position is remote, allowing flexibility within the U.S.

Posted 1 week ago