Вакансии

Location: Federal Way, Washington Do work that matters! Every single day. We are hiring  Assistant Guides for our lovely Guidepost Montessori school at Celebration Park! At Guidepost Montessori, our Assistant Guides play a meaningful role in helping children ages 0–6 develop independence, confidence, and a lifelong love of learning. If you’re passionate about working with young children and want to grow in a supportive, purpose-driven environment, we’d love to meet you. Your Impact As an Assistant Guide , you’ll support a mixed-age Montessori classroom (Infant, Toddler, and Children’s House ages 3–6), helping create a calm, engaging environment where children are empowered to explore, learn, and grow at their own pace. In a Montessori classroom, the environment and the guide work together to support each child’s development. You’ll assist in preparing beautiful, intentional learning spaces and support children through individualized and small-group activities—always with respect, patience, and care. Most importantly, you’ll help children build confidence in themselves and discover what they’re capable of achieving. What You’ll Do Observe children closely to understand and support their individual needs Help prepare and maintain a beautiful, orderly classroom environment Support individual and small-group learning activities Build warm, respectful relationships with children and families Collaborate with Lead Guides and fellow team members to create a strong classroom community What We’re Looking For Experience working with children ages 3–6 in a school or childcare setting A positive, adaptable, and collaborative mindset A genuine love for working with young children Interest in learning and growing within the Montessori approach (prior Montessori experience is welcome but not required) What We Offer Fully sponsored Montessori diploma training through the Prepared Montessorian Institute Ongoing professional development and mentorship 75% tuition discount for up to two children at any Guidepost school Competitive pay Health, dental, and vision insurance Paid time off and paid holidays Abundant opportunities for growth and career advancement A supportive network of peers who value collaboration and shared best practices About Guidepost Montessori Guidepost Montessori is a growing network of over 100 schools across the U.S. and Asia, serving children from 3 months to 6 years old . We believe children are capable and we design an education that nurtures independence, curiosity, and joy. Our mission is to transform education by helping every child reach their highest potential academically, socially, and emotionally. If you love working with children and are looking for a role where you can grow, make an impact, and be part of something meaningful, we’d love to hear from you. Apply today and join our school community! Guidepost Global Education and Guidepost Montessori are committed to a policy of Equal Employment Opportunity (EEO). We will not discriminate on the basis of age, sex, race, color, creed, religion, ethnicity, sexual orientation, gender identity, gender expression, national origin, alienage or citizenship, disability, marital status, military or veteran status, or any other legally recognized protected basis under applicable federal, state or local laws, regulations or ordinances. Our leadership is dedicated to ensuring the fulfillment of this policy. When requested, we are committed to reasonably accommodate employees and applicants with disabilities or special needs that may require an accommodation.

Location: Kent, Washington Do work that matters! Every single day. We are hiring  Assistant Guides for our lovely Guidepost Montessori school at Kent! At Guidepost Montessori, our Assistant Guides play a meaningful role in helping children ages 0–6 develop independence, confidence, and a lifelong love of learning. If you’re passionate about working with young children and want to grow in a supportive, purpose-driven environment, we’d love to meet you. Your Impact As an Assistant Guide , you’ll support a mixed-age Montessori classroom (Infant, Toddler, and Children’s House ages 3–6), helping create a calm, engaging environment where children are empowered to explore, learn, and grow at their own pace. In a Montessori classroom, the environment and the guide work together to support each child’s development. You’ll assist in preparing beautiful, intentional learning spaces and support children through individualized and small-group activities—always with respect, patience, and care. Most importantly, you’ll help children build confidence in themselves and discover what they’re capable of achieving. What You’ll Do Observe children closely to understand and support their individual needs Help prepare and maintain a beautiful, orderly classroom environment Support individual and small-group learning activities Build warm, respectful relationships with children and families Collaborate with Lead Guides and fellow team members to create a strong classroom community What We’re Looking For Experience working with children ages 3–6 in a school or childcare setting A positive, adaptable, and collaborative mindset A genuine love for working with young children Interest in learning and growing within the Montessori approach (prior Montessori experience is welcome but not required) What We Offer Fully sponsored Montessori diploma training through the Prepared Montessorian Institute Ongoing professional development and mentorship 75% tuition discount for up to two children at any Guidepost school Competitive pay Health, dental, and vision insurance Paid time off and paid holidays Abundant opportunities for growth and career advancement A supportive network of peers who value collaboration and shared best practices About Guidepost Montessori Guidepost Montessori is a growing network of over 100 schools across the U.S. and Asia, serving children from 3 months to 6 years old . We believe children are capable and we design an education that nurtures independence, curiosity, and joy. Our mission is to transform education by helping every child reach their highest potential academically, socially, and emotionally. If you love working with children and are looking for a role where you can grow, make an impact, and be part of something meaningful, we’d love to hear from you. Apply today and join our school community! Guidepost Global Education and Guidepost Montessori are committed to a policy of Equal Employment Opportunity (EEO). We will not discriminate on the basis of age, sex, race, color, creed, religion, ethnicity, sexual orientation, gender identity, gender expression, national origin, alienage or citizenship, disability, marital status, military or veteran status, or any other legally recognized protected basis under applicable federal, state or local laws, regulations or ordinances. Our leadership is dedicated to ensuring the fulfillment of this policy. When requested, we are committed to reasonably accommodate employees and applicants with disabilities or special needs that may require an accommodation.

Location: Kent, Washington Do work that matters! Every single day. We are hiring an Afternoon Assistant Teacher for our beautiful Kent Campus. This position would be working M-F 2-6pm or 3-6pm. At Guidepost Montessori, our Assistant Guides play a meaningful role in helping children ages 0–6 develop independence, confidence, and a lifelong love of learning. If you’re passionate about working with young children and want to grow in a supportive, purpose-driven environment, we’d love to meet you. Your Impact As an Afternoon Assistant Guide , you’ll support a mixed-age Montessori classroom (Infant, Toddler, and Children’s House ages 3–6), helping create a calm, engaging environment where children are empowered to explore, learn, and grow at their own pace. In a Montessori classroom, the environment and the guide work together to support each child’s development. You’ll assist in preparing beautiful, intentional learning spaces and support children through individualized and small-group activities—always with respect, patience, and care. Most importantly, you’ll help children build confidence in themselves and discover what they’re capable of achieving. What You’ll Do Observe children closely to understand and support their individual needs Help prepare and maintain a beautiful, orderly classroom environment Support individual and small-group learning activities Build warm, respectful relationships with children and families Collaborate with Lead Guides and fellow team members to create a strong classroom community What We’re Looking For Experience working with children ages 3–6 in a school or childcare setting A positive, adaptable, and collaborative mindset A genuine love for working with young children Interest in learning and growing within the Montessori approach (prior Montessori experience is welcome but not required) What We Offer Fully sponsored Montessori diploma training through the Prepared Montessorian Institute Ongoing professional development and mentorship 75% tuition discount for up to two children at any Guidepost school Competitive pay Health, dental, and vision insurance Paid time off and paid holidays Abundant opportunities for growth and career advancement A supportive network of peers who value collaboration and shared best practices About Guidepost Montessori Guidepost Montessori is a growing network of over 80 schools across the U.S. and Asia, serving children from 3 months to 6 years old . We believe children are capable and we design an education that nurtures independence, curiosity, and joy. Our mission is to transform education by helping every child reach their highest potential academically, socially, and emotionally. If you love working with children and are looking for a role where you can grow, make an impact, and be part of something meaningful, we’d love to hear from you. Apply today and join our school community! Guidepost Global Education and Guidepost Montessori are committed to a policy of Equal Employment Opportunity (EEO). We will not discriminate on the basis of age, sex, race, color, creed, religion, ethnicity, sexual orientation, gender identity, gender expression, national origin, alienage or citizenship, disability, marital status, military or veteran status, or any other legally recognized protected basis under applicable federal, state or local laws, regulations or ordinances. Our leadership is dedicated to ensuring the fulfillment of this policy. When requested, we are committed to reasonably accommodate employees and applicants with disabilities or special needs that may require an accommodation.

Location: Canada What the Role Is We’re hiring a Staff Software Engineer who’ll be the technical lead for a new Search team at Babylist. This new team will be focused on moving Babylist’s search capability in-house, making our search results more relevant and personalized, and partnering with other teams to transform search in the product from functional to delightful. Registry building is the heart of the Babylist product. Every parent builds a list of dozens of products, from swaddle to stroller, with real stakes (a friend or family member is going to buy these things, and a baby is going to use them). As a universal registry, this registry also typically spans many retailers. This makes registry building on Babylist one of the most interesting search and personalization problems in consumer e-commerce. This is zero-to-one work, with big ambitions. You’d build a search capability from the ground up, with a clear mandate from leadership to do it. In your first year, we expect to have rebuilt search to be highly relevant and personalized, and delivered big bets like universal search: one experience across items we sell and items from other retailers, and across products, guides and registries. We also want to work with Addie, our AI registry assistant, to have LLMs play a role in the search experience. If you want to tune an established search system, this isn’t the right role. If you’ve worked inside a strong search team and want to build a new system from scratch at a company earlier in that journey, read on. What You'll Own A Staff Engineer at Babylist sets direction. You’ll be the technical lead for search: you decide where it goes over the next year or two, sequence the bets that get there, and make the technical and product calls along the way. You’re still in the code and will drive initiatives forward across multiple teams. You’ll have software engineering and machine learning engineering support on the team. A few examples of initiatives you’d drive on the Search team: Build search in-house. Choose the architecture for indexing, retrieval and serving, and plan a seamless migration. The system you design replaces what we use today and is the foundation for everything below. Define what a good result is. Decide how we measure result quality, then improve it through query understanding, ranking and the behavioral signals we collect. Unify search across everything we show. One experience across items we sell and items from other retailers, and across products, guides and registries, including how to rank items we don’t sell next to ones we do. Decide how search and recommendations fit together. Bring what we know about a user and their registry into results, and decide how much retrieval and ranking search shares with our recommendation models. Make search conversational. Work with the Addie team, our AI registry assistant, on queries like “best stroller for urban family,” where the shopper describes a need instead of naming a product. Like every Staff Engineer here, you will: Take a fuzzy business problem from the first sketch through to production, and stay on the hook for whether it actually helped customers. Build the platform or tooling other teams adopt by choice, and own it as it scales. Set the standard for how search builds with AI. Decide what good looks like, build the patterns and evals that get agents there and catch the output that's confidently wrong before it ships. Partner with product, design and data as a peer, shaping what's worth building from the start. Coach Senior engineers through the hard calls, the ambiguous ones as much as the technical ones Who You Are You meet the Staff bar at Babylist. You’ve shipped production systems for enough years to have earned strong opinions, and you hold them loosely. You can pick up an ambiguous problem and start moving before anyone hands you the full picture. You’ve already changed how a team builds with AI, and the new way stuck. On top of that, you’ve worked on search problems before, ideally in an e-commerce setting. This role needs depth in the systems behind search and a clear focus on the people using it. You bring: Hands-on search experience: building and running search in production, including indexing, retrieval, ranking and query understanding. You know how to keep a search service fast, relevant and reliable as traffic and catalog size grow. Experience evaluating search quality: offline and online, including relevance judgments and A/B tests. A focus on the user: you use query data, behavior and judgment about result quality to decide what to build, and you can connect a search change to what shoppers do, and to conversion or revenue. Comfort building from zero: you can define the problem, design the system and own it end to end. You measure yourself by impact: a customer outcome, or a system a dozen teams come to depend on. Compensation We post real numbers. For a Canada-based Staff Engineer, the starting base salary range is $299,300 to $372,600 CAD, plus a target annual bonus of 20 percent of base. That's total target cash of roughly $359,160 to $447,120 CAD. On top of that you get meaningful equity and an RRSP match. Where you start in that range depends on your experience, and your pay grows from there with performance and scope. How We Build AI is the default here. Engineers run agentic sessions for most of the work, and a lot of the interesting engineering now lives in the scaffolding that makes the agents good: the eval harnesses, the curated context, custom review skills and fast CI. Agents also triage incidents and handle a big share of support. A human always owns the outcome. The architecture is intentionally simple: one Rails monolith, MySQL and few moving parts. That's deliberate. Simple infrastructure lets us move fast and lets AI reason about the whole system, so the hardest problems are the ones in front of customers. The Stack Core app: Rails, Packwerk, React, TypeScript, Sidekiq Mobile: iOS (Swift), Android (Kotlin) Data, search & events: MySQL, Snowflake, dbt, Airflow, Weaviate, WarpStream, Hex, Sigma Machine learning: deep learning, matrix factorization, retrieval & ranking, AWS SageMaker, MLflow AI & dev tooling: Claude Code, Devin, Linear, CodeRabbit, Warden, LangChain, Bedrock AgentCore, Maxim Infra & ops: AWS, Kubernetes, Terraform, GitHub Actions, Datadog, incident.io, LaunchDarkly Key integrations: Shopify (payments), Iterable (CRM) Why Babylist An engineer, expecting her first baby, couldn't find the registry she wanted. So she built it. That's how Babylist started, and it's still how we work: engineers solving problems for families. Becoming a parent is one of the biggest moments in a person's life. Millions reach it for the first time every year, making thousands of decisions and figuring it out as they go. That's who we build for, and we're a long way from done. Ten million people give gifts through Babylist every year. We did more than $750M in revenue in 2025, up 45 percent over the year before, and we've been profitable for eight years while staying independent. So you can take on a hard, multi-year problem without watching over your shoulder for the next round or the next correction. And the team is small, around 65 engineers, so what you ship stays visible and your scope stays wide. How We Work Remote-first across the US and Canada, and we have been for years. That's not changing. We trust you to own your time and your outcomes, and we get everyone in a room together twice a year. Teams are small, pods of three to five engineers, so nothing you ship disappears into a committee. You'll work shoulder to shoulder with product, design and data, and with the partners across the business who rely on what you ship. You'll also stay close to customers yourself: sitting in on user interviews, watching session recordings, riding along with support. Here that's part of the engineering job, on a regular basis. How We Hire Three rounds, usually two to three weeks start to finish. Recruiter conversation (30 minutes). Trade context: what you want next, what we're building and straight answers on comp, team and remote. Technical screen (1 hour). One round, no AI, language-agnostic. Reach for Google when you normally would. We just want to see how you reason from first principles. Final round (4 hours). Four one-hour interviews: system design, AI-assisted coding with the tools you'd actually use here, product sense and culture and values. You'll hear back either way within 24 to 48 hours. Benefits Company-paid medical and fully covered dental and vision A 401(k) match Generous paid parental leave for birthing and non-birthing parents, plus a gradual return-to-work program Winter Wonder Week, a paid company-wide week off at the end of the year A remote-work stipend Mental-health and wellness support A Few Things to Know We record and transcribe interviews to evaluate candidates, in line with applicable privacy laws. We expect you to use AI in your work and we welcome it in the process, but what you submit and say should reflect your own thinking. If you have a family member or close relationship with a Babylist employee, let your recruiter know. Official outreach only ever comes from an @babylist.com address

Location: Canada Workplace: Remote About Sailor Health Sailor Health is revolutionizing mental health care for older adults, addressing one of the fastest-growing and most underserved healthcare segments in America. With over 60 million seniors expected to make up nearly 25% of the population by 2030, we're entering a period of unprecedented demand for care. Yet, today, millions of older adults remain isolated, underserved, and struggling with mental health challenges such as anxiety, depression, and loneliness without adequate support. At Sailor Health, we envision a world where every senior has seamless access to compassionate, effective, and personalized mental health care. Join us on our mission to redefine the golden years, enabling older adults across the nation to live happier, healthier, and more fulfilling lives. About the Role Location: Remote, Canada-based Title: Provider Support Coordinator We're hiring a full-time remote Provider Support Coordinator to serve as a dedicated support resource for our provider network at Sailor Health. You'll be a go-to contact for therapists and clinicians navigating the day-to-day realities of delivering care — fielding real-time calls and emails, troubleshooting tech issues, managing caseload support, and proactively building relationships to ensure our providers feel empowered and well-supported. The key to provider satisfaction is making sure they receive exceptional and prompt support, so this role has high impact for the practice. Typical working hours are Monday-Friday 9am–6pm ET, with the occasional flexibility to work past 6pm ET when provider support requires it. This is an ideal position for someone who thrives on relationship-building, quick problem-solving, and delivering exceptional service to clinical professionals — all in support of improving mental health outcomes for older adults. We're a startup, and our processes are still being built and refined as we grow — this role isn't for everyone, but if you love creating structure out of ambiguity, you'll thrive here. What You'll Do - Manage provider email support. Be the first point of contact for inbound emails covering provider support, caseload questions, tech troubleshooting, provider-patient matching, and payroll/benefits questions — resolving what's yours to solve and escalating anything outside your scope (like actually processing payroll) to your lead. - Answer provider calls in real time. Be the first line of support for providers dealing with logistical, technical, or patient-related issues as they come up throughout the day. - Track every open item through to resolution. Keep a running, up-to-date record of outstanding provider requests, holds, and follow-ups, and see each one through to completion — even if it takes a few days to close out. - Proactively support your provider network. Reach out regularly to providers to check in, understand their needs, and identify ways Sailor Health can better support their work and caseloads. - Deliver outstanding customer service to providers. Represent Sailor Health with warmth, professionalism, and a solutions-first mindset in every provider interaction — including with the occasional difficult one. Who You Are - Extremely organized, with airtight follow-through. You catch issues early and never leave something unresolved without a plan to close it out. Colleagues can trust that once something lands with you — in Slack, email, or a call — it's handled, without needing to be reminded. - Proactive, and comfortable with ambiguity. You take initiative without being asked, and when instructions or processes are unclear, you find a path forward and build structure rather than waiting for direction. - Sound judgment on urgency and ownership. You know when something is a drop-everything issue versus one that can wait a few hours, and you know what's yours to solve versus what you should escalate. - Warm, empathetic communicator. You bring genuine warmth to every conversation — clear, kind, and easy to work with, even with a difficult provider on the other end. Why Join Sailor Health - Mission with impact. Help bring life-changing care to a population that's too often overlooked. - Remote-first team. Enjoy the flexibility of remote work while staying closely connected with a thoughtful, collaborative team rooted in purpose. - Growth and ownership. Be part of a small, agile team where you'll take initiative, shape key processes, and grow as we grow. - Make someone's day — every day. Your work helps providers feel supported so they can deliver their best care to patients who need it. If this sounds like you, we'd love to connect. Join us in redefining what it means to age with dignity, connection, and mental wellness.

Location: Canada Workplace: Remote ABOUT SAILOR HEALTH Sailor Health is revolutionizing mental health care for older adults, addressing one of the fastest-growing and most underserved healthcare segments in America. With over 60 million seniors expected to make up nearly 25% of the population by 2030, we’re entering a period of unprecedented demand for care. Yet, today, millions of older adults remain isolated, underserved, and struggling with mental health challenges such as anxiety, depression, loneliness without adequate support. At Sailor Health, we envision a world where every senior has seamless access to compassionate, effective, and personalized mental health care. Join us on our mission to redefine the golden years, enabling older adults across the nation to live happier, healthier, and more fulfilling lives. ABOUT THE ROLE Location: Remote - Canada based only Title: Patient Support Coordinator We’re hiring a full-time remote Patient Support Coordinator to provide exceptional post-intake support to patients at Sailor Health. Once a patient begins care, you’ll be their go-to point of contact for everything they need, including scheduling changes, billing questions, troubleshooting Zoom – all while providing friendly, reliable guidance every step of the way. In this role, you’ll ensure patients always feel cared for, informed, and supported. You’ll handle a range of administrative and customer service requests via phone, email, and text with empathy, precision, and professionalism. This is an ideal position for someone who thrives on solving problems, staying organized under pressure, and making people feel valued and understood — all while helping deliver high-quality mental healthcare for older adults. WHO YOU ARE - Exceptional communicator. You’re clear, kind, and patient — able to translate complex information (like billing or scheduling) into reassuring, easy-to-understand guidance. - Detail-oriented problem solver. You spot issues before they escalate, follow up without being prompted, and take pride in getting things right the first time. - Calm under pressure. You stay composed and resourceful when juggling multiple requests, maintaining professionalism and empathy throughout. - Tech-comfortable and adaptable. You learn new platforms quickly — whether it’s an EHR like Healthie, a messaging tool like OhMD, or scheduling and billing software. - Mission-driven with relevant experience. You care deeply about making mental health care accessible for older adults and may come from a background in customer support, patient support, care coordination, or healthcare administration, where empathy and organization are key. WHY JOIN SAILOR HEALTH - Mission with impact. Help bring life-changing care to a population that’s too often overlooked. - Remote-first team. Enjoy the flexibility of remote work while staying closely connected with a thoughtful, collaborative team rooted in purpose. - Growth and ownership. Be part of a small, agile team where you’ll take initiative, shape key processes, and grow as we grow. - Make someone’s day – every day. Your work helps older adults and their families feel seen, supported, and cared for. If this sounds like you, we’d love to connect. Join us in redefining what it means to age with dignity, connection, and mental wellness.

Location: United States of America What We Do: Cybercrime is growing, and more businesses are getting hit by threats that used to target only the biggest organizations. That pushes defenders like us to operate at the highest level, and it deepens our need for good people who want to make a meaningful impact. Founded in 2015 by former NSA cyber operators, Huntress is a remote-first team working to make enterprise-grade cybersecurity accessible to businesses of all sizes. We work closely with security teams and service providers protecting complex environments, often without the time or headcount to handle it all. That’s why we build our technology in-house and back it with a 24/7 human-led Security Operations Center (SOC). As a result, our platform is never disconnected from the experts who manage it, ensuring our customers' protection. Huntress now secures more than 6M endpoints and 17M identities worldwide. Those numbers keep growing because more businesses rely on us to help carry the load and operate with more confidence. Every day, you can see that commitment in how we stand with our customers and how we show up for each other. Reports to: Senior Manager, Community - Field Location: Remote US Compensation Range: $94,400 to $118,000 base plus bonus and equity What You'll Do: You are the person who brings Huntress to life in the room. You travel to roadshows, tradeshows, and other events, meet people who didn't plan to meet you, and turn genuine conversations into momentum for the business. Give More Than We Take shows up in the way you create value first, listen closely, and build relationships that feel human rather than transactional. You are Outcome-Obsessed about creating meaningful engagements at events from the moment you leave your house to the moment you return, knowing that booth hours are just the tip of the iceberg. You make sure the right prospects receive thoughtful follow-up, account executives get the context they need, and qualified conversations become fully booked demo opportunities. You take ownership of the details, spot what is missing, and use sound judgment when the process needs to bend to serve the customer and the team. Responsibilities: Travel to assigned roadshows, tradeshows, and other events up to 60% of the time while representing Huntress with energy, professionalism, and a clear understanding of the audience. Drive meaningful traffic to the Huntress booth with pre-event outreach, and by starting conversations, engaging attendees, and creating reasons for the right people to stop and learn more. Execute on-site activations and feet-on-the-street account-based marketing activities as directed by your manager. Build genuine relationships with prospects and partners, in transit, in hallways, over drinks, over dinner, at the booth, at event receptions and parties. Wherever the MSPs are, that’s where you’ll be found. Drive interest through thoughtful conversation, and book demos at in-person events. Manage event follow-up and relationship progression so account executives receive timely context, clear next steps, and strong opportunities to pursue. Use AI tools to research target accounts, prepare for conversations, organize event notes, and draft timely follow-up while applying sound judgment to the final work. What You Bring to the Team: Experience engaging prospects at tradeshows, roadshows, conferences, or other in-person events is valuable. You are curious, self-starting, eager to learn, driven, and comfortable taking initiative without waiting for perfect instructions. You can engage strangers naturally, make small talk that leads somewhere useful, and build relationships without making the interaction feel transactional. You are travel-ready and comfortable working in environments that require frequent movement, quick judgment, and consistent energy. You are detail-oriented and able to follow a process while recognizing when the situation calls for a thoughtful deviation. You use AI to work faster and smarter in day-to-day preparation, research, and follow-up, with good judgment about accuracy, privacy, and when human context matters most. What We Offer: 100% remote work environment - since our founding in 2015 Generous paid time off policy, including vacation, sick time, and paid holidays 12 weeks of paid parental leave Highly competitive and comprehensive medical, dental, and vision benefits plans 401(k) with a 5% contribution regardless of employee contribution Life and Disability insurance plans Stock options for all full-time employees One-time $500 reimbursement for building/upgrading home office Annual allowance for education and professional development assistance $75 USD/month digital reimbursement Access to the BetterUp platform for coaching, personal, and professional growth Huntress is committed to creating a culture of inclusivity where every single member of our team is valued, has a voice, and is empowered to come to work every day just as they are. We do not discriminate based on race, ethnicity, color, ancestry, national origin, religion, sex, sexual orientation, gender identity, disability, veteran status, genetic information, marital status, or any other legally protected status. We do discriminate against hackers who try to exploit small businesses. Accommodations: If you require reasonable accommodation to complete this application, interview, or pre-employment testing or participate in the employee selection process, please direct your inquiries to accommodations@huntresslabs.com . Please note that non-accommodation requests to this inbox will not receive a response. Huntress uses artificial intelligence tools to assist in reviewing and evaluating job applications, including resume screening, skills assessment, and candidate matching and comparisons. These AI tools support our human recruiters in the initial review process but do not make final hiring decisions without human involvement. By submitting your application, you acknowledge this use of AI in our recruitment process. Please review our Candidate Privacy Notice for more details on our practices and your data privacy rights. Huntress is committed to creating a culture of inclusivity where every single member of our team is valued, has a voice, and is empowered to come to work every day just as they are. We do not discriminate based on race, ethnicity, color, ancestry, national origin, religion, sex, sexual orientation, gender identity, disability, veteran status, genetic information, marital status, or any other legally protected status. We do discriminate against hackers who try to exploit businesses of all sizes. Accommodations: If you require reasonable accommodation to complete this application, interview, or pre-employment testing or participate in the employee selection process, please direct your inquiries to accommodations@huntresslabs.com . Please note that non-accommodation requests to this inbox will not receive a response. Huntress uses artificial intelligence tools to assist in reviewing and evaluating job applications, including resume screening, skills assessment, and candidate matching and comparisons. These AI tools support our human recruiters in the initial review process, but do not make final hiring decisions without human involvement. By submitting your application, you acknowledge this use of AI in our recruitment process. Please review our Candidate Privacy Notice for more details on our practices and your data privacy rights. #BI-Remote

Location: San Francisco Workplace: Hybrid WHY HARVEY At Harvey, we’re transforming how legal and professional services operate. By combining frontier agentic AI, an enterprise-grade platform, and deep domain expertise, we’re reshaping how critical knowledge work gets done for decades to come. This is a rare chance to help build a generational company at a true inflection point. We have strong product-market fit and world-class investor support. We’re scaling fast and defining a new category in real time. The work is ambitious, the bar is high, and the opportunity for growth — personal, professional, and financial — is unmatched. Our team moves fast, takes ownership, and is deeply committed to the mission — operating with intensity, staying close to our customers, and pushing each other for excellence. We live by three values: Decisiveness, Simplicity, and Job's Not Finished. We act quickly on clear judgment over perfect information, we believe simplicity is what scales, and we're never satisfied with where we are. If you want to do the best work of your career alongside people who share that drive, we'd love to build with you. At Harvey, the future of professional services is being written today — and we’re just getting started. ROLE OVERVIEW In this role, you'll build the systems that power how we adapt and improve leading open-weight models for legal work. We recently post-trained our first model built for legal work, and we’re now building the team to scale up our post-training efforts. You’ll own the infrastructure behind that effort: distributed training, RL rollout systems, the environments where models learn to use tools and complete complex legal workflows, and the internal systems the team uses to manage data, run evals, and launch and track training jobs. You’ll help decide what we build, what we adopt, and how the pieces fit together. You’ll join a small, high-bar team of hands-on engineers and researchers, where each person owns a large part of the stack. We’re looking for research engineers who have built demanding ML or distributed systems, can debug across the stack, and want to work closely with researchers to make training faster, more reliable, and easier to iterate on. Learn more about our work at harvey.ai/research http://harvey.ai/research. WHAT YOU'LL DO - Scale our post-training stack, supporting SFT and RL on leading open-weight models. - Build distributed rollout infrastructure that coordinates model inference, tool execution, and collection of training experience for long-horizon tasks. - Build the infrastructure for reproducible agent environments where models can interact with documents, tools, and external systems during training and evaluation. - Own the performance and reliability of training runs, including GPU utilization, checkpointing, failure recovery, and observability. - Identify and remove bottlenecks across training and inference to shorten the path from research idea to useful result. - Build the internal tools and services the team runs on: training data management and versioning, running evals and browsing results, and launching and tracking training jobs and experiments. - Integrate models into production, including serving, latency, cost, and online evaluation, so research reaches customers quickly and safely. - Partner closely with researchers to turn experimental workflows into dependable systems while keeping room for rapid experimentation. WHAT YOU HAVE - Experience building ML infrastructure, distributed systems, or research tooling, with ownership of performance, reliability, and operation. - Strong Python and generalist software engineering skills across backend services, data systems, and internal tools, with familiarity with PyTorch, JAX, or similar ML frameworks. - Experience with distributed training or inference, including diagnosing performance bottlenecks and failures across the stack. - Understanding of post-training workflows; experience with RL rollouts, agent environments, or sandboxed tool execution is a plus. - Practical judgment about what to build versus adopt, and the ability to partner with researchers and ship quickly as needs evolve. We are hiring for this role across multiple levels. COMPENSATION $230,000 - $450,000 USD DEPENDING ON YOUR LOCATION, AN APPLICANT PRIVACY NOTICE MAY APPLY TO YOU. YOU CAN FIND ALL OF OUR APPLICANT PRIVACY NOTICES HERE https://harveyai.notion.site/Harvey-Candidate-Privacy-Notices-319ac3fcdd7a803bb807d5094f249922?pvs=74. #LI-DZ1 Harvey is an equal opportunity employer and does not discriminate on the basis of race, gender, sexual orientation, gender identity/expression, national origin, disability, age, genetic information, veteran status, marital status, pregnancy or related condition, or any other basis protected by law. We are committed to providing reasonable accommodations to applicants with disabilities, and requests can be made by emailing accommodations@harvey.ai

Location: Remote Workplace: Remote WHY HARVEY At Harvey, we’re transforming how legal and professional services operate. By combining frontier agentic AI, an enterprise-grade platform, and deep domain expertise, we’re reshaping how critical knowledge work gets done for decades to come. This is a rare chance to help build a generational company at a true inflection point. We have strong product-market fit and world-class investor support. We’re scaling fast and defining a new category in real time. The work is ambitious, the bar is high, and the opportunity for growth — personal, professional, and financial — is unmatched. Our team moves fast, takes ownership, and is deeply committed to the mission — operating with intensity, staying close to our customers, and pushing each other for excellence. We live by three values: Decisiveness, Simplicity, and Job's Not Finished. We act quickly on clear judgment over perfect information, we believe simplicity is what scales, and we're never satisfied with where we are. If you want to do the best work of your career alongside people who share that drive, we'd love to build with you. At Harvey, the future of professional services is being written today — and we’re just getting started. ROLE OVERVIEW In this role, you'll build the systems that power how we adapt and improve leading open-weight models for legal work. We recently post-trained our first model built for legal work, and we’re now building the team to scale up our post-training efforts. You’ll own the infrastructure behind that effort: distributed training, RL rollout systems, the environments where models learn to use tools and complete complex legal workflows, and the internal systems the team uses to manage data, run evals, and launch and track training jobs. You’ll help decide what we build, what we adopt, and how the pieces fit together. You’ll join a small, high-bar team of hands-on engineers and researchers, where each person owns a large part of the stack. We’re looking for research engineers who have built demanding ML or distributed systems, can debug across the stack, and want to work closely with researchers to make training faster, more reliable, and easier to iterate on. Learn more about our work at harvey.ai/research http://harvey.ai/research. WHAT YOU'LL DO - Scale our post-training stack, supporting SFT and RL on leading open-weight models. - Build distributed rollout infrastructure that coordinates model inference, tool execution, and collection of training experience for long-horizon tasks. - Build the infrastructure for reproducible agent environments where models can interact with documents, tools, and external systems during training and evaluation. - Own the performance and reliability of training runs, including GPU utilization, checkpointing, failure recovery, and observability. - Identify and remove bottlenecks across training and inference to shorten the path from research idea to useful result. - Build the internal tools and services the team runs on: training data management and versioning, running evals and browsing results, and launching and tracking training jobs and experiments. - Integrate models into production, including serving, latency, cost, and online evaluation, so research reaches customers quickly and safely. - Partner closely with researchers to turn experimental workflows into dependable systems while keeping room for rapid experimentation. WHAT YOU HAVE - Experience building ML infrastructure, distributed systems, or research tooling, with ownership of performance, reliability, and operation. - Strong Python and generalist software engineering skills across backend services, data systems, and internal tools, with familiarity with PyTorch, JAX, or similar ML frameworks. - Experience with distributed training or inference, including diagnosing performance bottlenecks and failures across the stack. - Understanding of post-training workflows; experience with RL rollouts, agent environments, or sandboxed tool execution is a plus. - Practical judgment about what to build versus adopt, and the ability to partner with researchers and ship quickly as needs evolve. We are hiring for this role across multiple levels. COMPENSATION $230,000 - $450,000 USD DEPENDING ON YOUR LOCATION, AN APPLICANT PRIVACY NOTICE MAY APPLY TO YOU. YOU CAN FIND ALL OF OUR APPLICANT PRIVACY NOTICES HERE https://harveyai.notion.site/Harvey-Candidate-Privacy-Notices-319ac3fcdd7a803bb807d5094f249922?pvs=74. #LI-DZ1 Harvey is an equal opportunity employer and does not discriminate on the basis of race, gender, sexual orientation, gender identity/expression, national origin, disability, age, genetic information, veteran status, marital status, pregnancy or related condition, or any other basis protected by law. We are committed to providing reasonable accommodations to applicants with disabilities, and requests can be made by emailing accommodations@harvey.ai

Location: London Workplace: Hybrid WHY HARVEY At Harvey, we’re transforming how legal and professional services operate. By combining frontier agentic AI, an enterprise-grade platform, and deep domain expertise, we’re reshaping how critical knowledge work gets done for decades to come. This is a rare chance to help build a generational company at a true inflection point. We have strong product-market fit and world-class investor support. We’re scaling fast and defining a new category in real time. The work is ambitious, the bar is high, and the opportunity for growth — personal, professional, and financial — is unmatched. Our team moves fast, takes ownership, and is deeply committed to the mission — operating with intensity, staying close to our customers, and pushing each other for excellence. We live by three values: Decisiveness, Simplicity, and Job's Not Finished. We act quickly on clear judgment over perfect information, we believe simplicity is what scales, and we're never satisfied with where we are. If you want to do the best work of your career alongside people who share that drive, we'd love to build with you. At Harvey, the future of professional services is being written today — and we’re just getting started. ROLE OVERVIEW In this role, you'll build the systems that power how we adapt and improve leading open-weight models for legal work. We recently post-trained our first model built for legal work, and we’re now building the team to scale up our post-training efforts. You’ll own the infrastructure behind that effort: distributed training, RL rollout systems, the environments where models learn to use tools and complete complex legal workflows, and the internal systems the team uses to manage data, run evals, and launch and track training jobs. You’ll help decide what we build, what we adopt, and how the pieces fit together. You’ll join a small, high-bar team of hands-on engineers and researchers, where each person owns a large part of the stack. We’re looking for research engineers who have built demanding ML or distributed systems, can debug across the stack, and want to work closely with researchers to make training faster, more reliable, and easier to iterate on. Learn more about our work at harvey.ai/research http://harvey.ai/research. WHAT YOU'LL DO - Scale our post-training stack, supporting SFT and RL on leading open-weight models. - Build distributed rollout infrastructure that coordinates model inference, tool execution, and collection of training experience for long-horizon tasks. - Build the infrastructure for reproducible agent environments where models can interact with documents, tools, and external systems during training and evaluation. - Own the performance and reliability of training runs, including GPU utilization, checkpointing, failure recovery, and observability. - Identify and remove bottlenecks across training and inference to shorten the path from research idea to useful result. - Build the internal tools and services the team runs on: training data management and versioning, running evals and browsing results, and launching and tracking training jobs and experiments. - Integrate models into production, including serving, latency, cost, and online evaluation, so research reaches customers quickly and safely. - Partner closely with researchers to turn experimental workflows into dependable systems while keeping room for rapid experimentation. WHAT YOU HAVE - Experience building ML infrastructure, distributed systems, or research tooling, with ownership of performance, reliability, and operation. - Strong Python and generalist software engineering skills across backend services, data systems, and internal tools, with familiarity with PyTorch, JAX, or similar ML frameworks. - Experience with distributed training or inference, including diagnosing performance bottlenecks and failures across the stack. - Understanding of post-training workflows; experience with RL rollouts, agent environments, or sandboxed tool execution is a plus. - Practical judgment about what to build versus adopt, and the ability to partner with researchers and ship quickly as needs evolve. We are hiring for this role across multiple levels. DEPENDING ON YOUR LOCATION, AN APPLICANT PRIVACY NOTICE MAY APPLY TO YOU. YOU CAN FIND ALL OF OUR APPLICANT PRIVACY NOTICES HERE https://harveyai.notion.site/Harvey-Candidate-Privacy-Notices-319ac3fcdd7a803bb807d5094f249922?pvs=74. #LI-DZ1 Harvey is an equal opportunity employer and does not discriminate on the basis of race, gender, sexual orientation, gender identity/expression, national origin, disability, age, genetic information, veteran status, marital status, pregnancy or related condition, or any other basis protected by law. We are committed to providing reasonable accommodations to applicants with disabilities, and requests can be made by emailing accommodations@harvey.ai

Location: New York Workplace: Hybrid WHY HARVEY At Harvey, we’re transforming how legal and professional services operate. By combining frontier agentic AI, an enterprise-grade platform, and deep domain expertise, we’re reshaping how critical knowledge work gets done for decades to come. This is a rare chance to help build a generational company at a true inflection point. We have strong product-market fit and world-class investor support. We’re scaling fast and defining a new category in real time. The work is ambitious, the bar is high, and the opportunity for growth — personal, professional, and financial — is unmatched. Our team moves fast, takes ownership, and is deeply committed to the mission — operating with intensity, staying close to our customers, and pushing each other for excellence. We live by three values: Decisiveness, Simplicity, and Job's Not Finished. We act quickly on clear judgment over perfect information, we believe simplicity is what scales, and we're never satisfied with where we are. If you want to do the best work of your career alongside people who share that drive, we'd love to build with you. At Harvey, the future of professional services is being written today — and we’re just getting started. ROLE OVERVIEW In this role, you'll build the systems that power how we adapt and improve leading open-weight models for legal work. We recently post-trained our first model built for legal work, and we’re now building the team to scale up our post-training efforts. You’ll own the infrastructure behind that effort: distributed training, RL rollout systems, the environments where models learn to use tools and complete complex legal workflows, and the internal systems the team uses to manage data, run evals, and launch and track training jobs. You’ll help decide what we build, what we adopt, and how the pieces fit together. You’ll join a small, high-bar team of hands-on engineers and researchers, where each person owns a large part of the stack. We’re looking for research engineers who have built demanding ML or distributed systems, can debug across the stack, and want to work closely with researchers to make training faster, more reliable, and easier to iterate on. Learn more about our work at harvey.ai/research http://harvey.ai/research. WHAT YOU'LL DO - Scale our post-training stack, supporting SFT and RL on leading open-weight models. - Build distributed rollout infrastructure that coordinates model inference, tool execution, and collection of training experience for long-horizon tasks. - Build the infrastructure for reproducible agent environments where models can interact with documents, tools, and external systems during training and evaluation. - Own the performance and reliability of training runs, including GPU utilization, checkpointing, failure recovery, and observability. - Identify and remove bottlenecks across training and inference to shorten the path from research idea to useful result. - Build the internal tools and services the team runs on: training data management and versioning, running evals and browsing results, and launching and tracking training jobs and experiments. - Integrate models into production, including serving, latency, cost, and online evaluation, so research reaches customers quickly and safely. - Partner closely with researchers to turn experimental workflows into dependable systems while keeping room for rapid experimentation. WHAT YOU HAVE - Experience building ML infrastructure, distributed systems, or research tooling, with ownership of performance, reliability, and operation. - Strong Python and generalist software engineering skills across backend services, data systems, and internal tools, with familiarity with PyTorch, JAX, or similar ML frameworks. - Experience with distributed training or inference, including diagnosing performance bottlenecks and failures across the stack. - Understanding of post-training workflows; experience with RL rollouts, agent environments, or sandboxed tool execution is a plus. - Practical judgment about what to build versus adopt, and the ability to partner with researchers and ship quickly as needs evolve. We are hiring for this role across multiple levels. COMPENSATION $230,000 - $450,000 USD DEPENDING ON YOUR LOCATION, AN APPLICANT PRIVACY NOTICE MAY APPLY TO YOU. YOU CAN FIND ALL OF OUR APPLICANT PRIVACY NOTICES HERE https://harveyai.notion.site/Harvey-Candidate-Privacy-Notices-319ac3fcdd7a803bb807d5094f249922?pvs=74. #LI-DZ1 Harvey is an equal opportunity employer and does not discriminate on the basis of race, gender, sexual orientation, gender identity/expression, national origin, disability, age, genetic information, veteran status, marital status, pregnancy or related condition, or any other basis protected by law. We are committed to providing reasonable accommodations to applicants with disabilities, and requests can be made by emailing accommodations@harvey.ai

Location: San Francisco Workplace: Hybrid WHY HARVEY At Harvey, we’re transforming how legal and professional services operate. By combining frontier agentic AI, an enterprise-grade platform, and deep domain expertise, we’re reshaping how critical knowledge work gets done for decades to come. This is a rare chance to help build a generational company at a true inflection point. We have strong product-market fit and world-class investor support. We’re scaling fast and defining a new category in real time. The work is ambitious, the bar is high, and the opportunity for growth — personal, professional, and financial — is unmatched. Our team moves fast, takes ownership, and is deeply committed to the mission — operating with intensity, staying close to our customers, and pushing each other for excellence. We live by three values: Decisiveness, Simplicity, and Job's Not Finished. We act quickly on clear judgment over perfect information, we believe simplicity is what scales, and we're never satisfied with where we are. If you want to do the best work of your career alongside people who share that drive, we'd love to build with you. At Harvey, the future of professional services is being written today — and we’re just getting started. ROLE OVERVIEW In this role, you'll build the data and measurement systems that power our post-training, benchmarking, and agent capability efforts. We recently post-trained our first model built for legal work, and we’re now building the team to scale up our post-training efforts. You’ll own synthetic data pipelines, evaluation design, the tasks and grading behind training environments, and the systems that turn feedback from domain experts into training signals and rewards. These are tasks where correctness often can’t be verified automatically, so defining and measuring quality is a core research challenge. You’ll join a small, high-bar team of hands-on engineers and researchers, where each person owns a large part of the work. We’re looking for research engineers who can build reliable data and evaluation systems, question what a metric actually tells us, and work closely with researchers and legal experts to turn model failures into measurable improvements. Learn more about our work at harvey.ai/research http://harvey.ai/research. WHAT YOU'LL DO - Build synthetic data pipelines that generate, filter, and curate training and evaluation samples for complex legal reasoning and agentic workflows. - Design evaluations and verifiers that reflect real legal work, calibrate them against expert judgment, and investigate where they fail. - Build and maintain capability benchmarks like our Legal Agent Benchmark (LAB), along with product-focused evals that track how models perform on real customer workflows. - Work with legal domain experts to define quality metrics, rubrics, and other grading mechanisms for training and evaluation. - Own training and evaluation data quality, including provenance, coverage, and safeguards against contamination. - Close the loop from product usage and customer feedback to new evaluations and training data, so models improve where customers need them most. - Partner with researchers to investigate model failures, distinguish real gains from misleading metrics, and use findings to guide training and release decisions. WHAT YOU HAVE - Experience building data pipelines, ML evaluation systems, or experimentation tooling, with strong Python skills and ownership of production-quality code. - A rigorous approach to measurement, including statistical reasoning, experimental design, and understanding when a metric can mislead. - The ability to translate ambiguous goals or expert judgment into structured datasets, rubrics, and measurable criteria. - Experience with synthetic data, LLM evaluations, automated raters, or preference data is a plus. Strong data and measurement engineering experience close to ML model development is valuable even without prior LLM work. We are hiring for this role across multiple levels. COMPENSATION $230,000 - $450,000 USD DEPENDING ON YOUR LOCATION, AN APPLICANT PRIVACY NOTICE MAY APPLY TO YOU. YOU CAN FIND ALL OF OUR APPLICANT PRIVACY NOTICES HERE https://harveyai.notion.site/Harvey-Candidate-Privacy-Notices-319ac3fcdd7a803bb807d5094f249922?pvs=74. #LI-DZ1 Harvey is an equal opportunity employer and does not discriminate on the basis of race, gender, sexual orientation, gender identity/expression, national origin, disability, age, genetic information, veteran status, marital status, pregnancy or related condition, or any other basis protected by law. We are committed to providing reasonable accommodations to applicants with disabilities, and requests can be made by emailing accommodations@harvey.ai

Location: London Workplace: Hybrid WHY HARVEY At Harvey, we’re transforming how legal and professional services operate. By combining frontier agentic AI, an enterprise-grade platform, and deep domain expertise, we’re reshaping how critical knowledge work gets done for decades to come. This is a rare chance to help build a generational company at a true inflection point. We have strong product-market fit and world-class investor support. We’re scaling fast and defining a new category in real time. The work is ambitious, the bar is high, and the opportunity for growth — personal, professional, and financial — is unmatched. Our team moves fast, takes ownership, and is deeply committed to the mission — operating with intensity, staying close to our customers, and pushing each other for excellence. We live by three values: Decisiveness, Simplicity, and Job's Not Finished. We act quickly on clear judgment over perfect information, we believe simplicity is what scales, and we're never satisfied with where we are. If you want to do the best work of your career alongside people who share that drive, we'd love to build with you. At Harvey, the future of professional services is being written today — and we’re just getting started. ROLE OVERVIEW In this role, you'll build the data and measurement systems that power our post-training, benchmarking, and agent capability efforts. We recently post-trained our first model built for legal work, and we’re now building the team to scale up our post-training efforts. You’ll own synthetic data pipelines, evaluation design, the tasks and grading behind training environments, and the systems that turn feedback from domain experts into training signals and rewards. These are tasks where correctness often can’t be verified automatically, so defining and measuring quality is a core research challenge. You’ll join a small, high-bar team of hands-on engineers and researchers, where each person owns a large part of the work. We’re looking for research engineers who can build reliable data and evaluation systems, question what a metric actually tells us, and work closely with researchers and legal experts to turn model failures into measurable improvements. Learn more about our work at harvey.ai/research http://harvey.ai/research. WHAT YOU'LL DO - Build synthetic data pipelines that generate, filter, and curate training and evaluation samples for complex legal reasoning and agentic workflows. - Design evaluations and verifiers that reflect real legal work, calibrate them against expert judgment, and investigate where they fail. - Build and maintain capability benchmarks like our Legal Agent Benchmark (LAB), along with product-focused evals that track how models perform on real customer workflows. - Work with legal domain experts to define quality metrics, rubrics, and other grading mechanisms for training and evaluation. - Own training and evaluation data quality, including provenance, coverage, and safeguards against contamination. - Close the loop from product usage and customer feedback to new evaluations and training data, so models improve where customers need them most. - Partner with researchers to investigate model failures, distinguish real gains from misleading metrics, and use findings to guide training and release decisions. WHAT YOU HAVE - Experience building data pipelines, ML evaluation systems, or experimentation tooling, with strong Python skills and ownership of production-quality code. - A rigorous approach to measurement, including statistical reasoning, experimental design, and understanding when a metric can mislead. - The ability to translate ambiguous goals or expert judgment into structured datasets, rubrics, and measurable criteria. - Experience with synthetic data, LLM evaluations, automated raters, or preference data is a plus. Strong data and measurement engineering experience close to ML model development is valuable even without prior LLM work. We are hiring for this role across multiple levels. DEPENDING ON YOUR LOCATION, AN APPLICANT PRIVACY NOTICE MAY APPLY TO YOU. YOU CAN FIND ALL OF OUR APPLICANT PRIVACY NOTICES HERE https://harveyai.notion.site/Harvey-Candidate-Privacy-Notices-319ac3fcdd7a803bb807d5094f249922?pvs=74. #LI-DZ1 Harvey is an equal opportunity employer and does not discriminate on the basis of race, gender, sexual orientation, gender identity/expression, national origin, disability, age, genetic information, veteran status, marital status, pregnancy or related condition, or any other basis protected by law. We are committed to providing reasonable accommodations to applicants with disabilities, and requests can be made by emailing accommodations@harvey.ai

Location: Remote Workplace: Remote WHY HARVEY At Harvey, we’re transforming how legal and professional services operate. By combining frontier agentic AI, an enterprise-grade platform, and deep domain expertise, we’re reshaping how critical knowledge work gets done for decades to come. This is a rare chance to help build a generational company at a true inflection point. We have strong product-market fit and world-class investor support. We’re scaling fast and defining a new category in real time. The work is ambitious, the bar is high, and the opportunity for growth — personal, professional, and financial — is unmatched. Our team moves fast, takes ownership, and is deeply committed to the mission — operating with intensity, staying close to our customers, and pushing each other for excellence. We live by three values: Decisiveness, Simplicity, and Job's Not Finished. We act quickly on clear judgment over perfect information, we believe simplicity is what scales, and we're never satisfied with where we are. If you want to do the best work of your career alongside people who share that drive, we'd love to build with you. At Harvey, the future of professional services is being written today — and we’re just getting started. ROLE OVERVIEW In this role, you'll build the data and measurement systems that power our post-training, benchmarking, and agent capability efforts. We recently post-trained our first model built for legal work, and we’re now building the team to scale up our post-training efforts. You’ll own synthetic data pipelines, evaluation design, the tasks and grading behind training environments, and the systems that turn feedback from domain experts into training signals and rewards. These are tasks where correctness often can’t be verified automatically, so defining and measuring quality is a core research challenge. You’ll join a small, high-bar team of hands-on engineers and researchers, where each person owns a large part of the work. We’re looking for research engineers who can build reliable data and evaluation systems, question what a metric actually tells us, and work closely with researchers and legal experts to turn model failures into measurable improvements. Learn more about our work at harvey.ai/research http://harvey.ai/research. WHAT YOU'LL DO - Build synthetic data pipelines that generate, filter, and curate training and evaluation samples for complex legal reasoning and agentic workflows. - Design evaluations and verifiers that reflect real legal work, calibrate them against expert judgment, and investigate where they fail. - Build and maintain capability benchmarks like our Legal Agent Benchmark (LAB), along with product-focused evals that track how models perform on real customer workflows. - Work with legal domain experts to define quality metrics, rubrics, and other grading mechanisms for training and evaluation. - Own training and evaluation data quality, including provenance, coverage, and safeguards against contamination. - Close the loop from product usage and customer feedback to new evaluations and training data, so models improve where customers need them most. - Partner with researchers to investigate model failures, distinguish real gains from misleading metrics, and use findings to guide training and release decisions. WHAT YOU HAVE - Experience building data pipelines, ML evaluation systems, or experimentation tooling, with strong Python skills and ownership of production-quality code. - A rigorous approach to measurement, including statistical reasoning, experimental design, and understanding when a metric can mislead. - The ability to translate ambiguous goals or expert judgment into structured datasets, rubrics, and measurable criteria. - Experience with synthetic data, LLM evaluations, automated raters, or preference data is a plus. Strong data and measurement engineering experience close to ML model development is valuable even without prior LLM work. We are hiring for this role across multiple levels. COMPENSATION $230,000 - $450,000 USD DEPENDING ON YOUR LOCATION, AN APPLICANT PRIVACY NOTICE MAY APPLY TO YOU. YOU CAN FIND ALL OF OUR APPLICANT PRIVACY NOTICES HERE https://harveyai.notion.site/Harvey-Candidate-Privacy-Notices-319ac3fcdd7a803bb807d5094f249922?pvs=74. #LI-DZ1 Harvey is an equal opportunity employer and does not discriminate on the basis of race, gender, sexual orientation, gender identity/expression, national origin, disability, age, genetic information, veteran status, marital status, pregnancy or related condition, or any other basis protected by law. We are committed to providing reasonable accommodations to applicants with disabilities, and requests can be made by emailing accommodations@harvey.ai

Location: New York Workplace: Hybrid WHY HARVEY At Harvey, we’re transforming how legal and professional services operate. By combining frontier agentic AI, an enterprise-grade platform, and deep domain expertise, we’re reshaping how critical knowledge work gets done for decades to come. This is a rare chance to help build a generational company at a true inflection point. We have strong product-market fit and world-class investor support. We’re scaling fast and defining a new category in real time. The work is ambitious, the bar is high, and the opportunity for growth — personal, professional, and financial — is unmatched. Our team moves fast, takes ownership, and is deeply committed to the mission — operating with intensity, staying close to our customers, and pushing each other for excellence. We live by three values: Decisiveness, Simplicity, and Job's Not Finished. We act quickly on clear judgment over perfect information, we believe simplicity is what scales, and we're never satisfied with where we are. If you want to do the best work of your career alongside people who share that drive, we'd love to build with you. At Harvey, the future of professional services is being written today — and we’re just getting started. ROLE OVERVIEW In this role, you'll build the data and measurement systems that power our post-training, benchmarking, and agent capability efforts. We recently post-trained our first model built for legal work, and we’re now building the team to scale up our post-training efforts. You’ll own synthetic data pipelines, evaluation design, the tasks and grading behind training environments, and the systems that turn feedback from domain experts into training signals and rewards. These are tasks where correctness often can’t be verified automatically, so defining and measuring quality is a core research challenge. You’ll join a small, high-bar team of hands-on engineers and researchers, where each person owns a large part of the work. We’re looking for research engineers who can build reliable data and evaluation systems, question what a metric actually tells us, and work closely with researchers and legal experts to turn model failures into measurable improvements. Learn more about our work at harvey.ai/research http://harvey.ai/research. WHAT YOU'LL DO - Build synthetic data pipelines that generate, filter, and curate training and evaluation samples for complex legal reasoning and agentic workflows. - Design evaluations and verifiers that reflect real legal work, calibrate them against expert judgment, and investigate where they fail. - Build and maintain capability benchmarks like our Legal Agent Benchmark (LAB), along with product-focused evals that track how models perform on real customer workflows. - Work with legal domain experts to define quality metrics, rubrics, and other grading mechanisms for training and evaluation. - Own training and evaluation data quality, including provenance, coverage, and safeguards against contamination. - Close the loop from product usage and customer feedback to new evaluations and training data, so models improve where customers need them most. - Partner with researchers to investigate model failures, distinguish real gains from misleading metrics, and use findings to guide training and release decisions. WHAT YOU HAVE - Experience building data pipelines, ML evaluation systems, or experimentation tooling, with strong Python skills and ownership of production-quality code. - A rigorous approach to measurement, including statistical reasoning, experimental design, and understanding when a metric can mislead. - The ability to translate ambiguous goals or expert judgment into structured datasets, rubrics, and measurable criteria. - Experience with synthetic data, LLM evaluations, automated raters, or preference data is a plus. Strong data and measurement engineering experience close to ML model development is valuable even without prior LLM work. We are hiring for this role across multiple levels. COMPENSATION $230,000 - $450,000 USD DEPENDING ON YOUR LOCATION, AN APPLICANT PRIVACY NOTICE MAY APPLY TO YOU. YOU CAN FIND ALL OF OUR APPLICANT PRIVACY NOTICES HERE https://harveyai.notion.site/Harvey-Candidate-Privacy-Notices-319ac3fcdd7a803bb807d5094f249922?pvs=74. #LI-DZ1 Harvey is an equal opportunity employer and does not discriminate on the basis of race, gender, sexual orientation, gender identity/expression, national origin, disability, age, genetic information, veteran status, marital status, pregnancy or related condition, or any other basis protected by law. We are committed to providing reasonable accommodations to applicants with disabilities, and requests can be made by emailing accommodations@harvey.ai

Location: San Francisco Workplace: Hybrid WHY HARVEY At Harvey, we’re transforming how legal and professional services operate. By combining frontier agentic AI, an enterprise-grade platform, and deep domain expertise, we’re reshaping how critical knowledge work gets done for decades to come. This is a rare chance to help build a generational company at a true inflection point. We have strong product-market fit and world-class investor support. We’re scaling fast and defining a new category in real time. The work is ambitious, the bar is high, and the opportunity for growth — personal, professional, and financial — is unmatched. Our team moves fast, takes ownership, and is deeply committed to the mission — operating with intensity, staying close to our customers, and pushing each other for excellence. We live by three values: Decisiveness, Simplicity, and Job's Not Finished. We act quickly on clear judgment over perfect information, we believe simplicity is what scales, and we're never satisfied with where we are. If you want to do the best work of your career alongside people who share that drive, we'd love to build with you. At Harvey, the future of professional services is being written today — and we’re just getting started. ROLE OVERVIEW In this role, you'll own how we adapt and improve leading open-weight models for legal work. We recently post-trained our first model built for legal work, and we’re now building the team to scale up our post-training efforts. You’ll own the post-training recipe across SFT and RL, including reward design and verification for challenging, semi-verifiable legal tasks. You’ll help set the research direction and carry ideas from experiments through training runs to models used by customers. You’ll join a small, high-bar team of hands-on engineers and researchers, where each person owns a large part of the work. We’re looking for research engineers and scientists who have gotten training runs working at scale, are immersed in RL for language models, and want their work to reach users quickly. Learn more about our work at harvey.ai/research http://harvey.ai/research. WHAT YOU'LL DO - Own and pursue a research agenda to improve model capability on long-horizon legal reasoning and agentic workflows. - Design and run post-training experiments across SFT and RL, building on leading open-weight models. - Develop reward functions and verifiers for semi-verifiable tasks, including rubrics, learned preference models, and expert preference data, and study where they break. - Run and debug training at scale, addressing divergence, reward hacking, and instability. - Build and use rigorous evals to track improvements and decide what ships. - Design, implement, test, and debug code across our research stack. WHAT YOU HAVE - Hands-on experience post-training LLMs with SFT and RL, including preference optimization, and direct ownership of training runs and results. - Deep ML fundamentals and experience getting distributed training runs working reliably. - Strong engineering skills in Python, with production-quality code and end-to-end system ownership. - Research experience in RL for language models or reward modeling, with the ability to move quickly from experiments to shipped improvements. Publications welcome, not required. We are hiring for this role across multiple levels. COMPENSATION $230,000 - $450,000 USD DEPENDING ON YOUR LOCATION, AN APPLICANT PRIVACY NOTICE MAY APPLY TO YOU. YOU CAN FIND ALL OF OUR APPLICANT PRIVACY NOTICES HERE https://harveyai.notion.site/Harvey-Candidate-Privacy-Notices-319ac3fcdd7a803bb807d5094f249922?pvs=74. #LI-DZ1 Harvey is an equal opportunity employer and does not discriminate on the basis of race, gender, sexual orientation, gender identity/expression, national origin, disability, age, genetic information, veteran status, marital status, pregnancy or related condition, or any other basis protected by law. We are committed to providing reasonable accommodations to applicants with disabilities, and requests can be made by emailing accommodations@harvey.ai

Location: Remote Workplace: Remote WHY HARVEY At Harvey, we’re transforming how legal and professional services operate. By combining frontier agentic AI, an enterprise-grade platform, and deep domain expertise, we’re reshaping how critical knowledge work gets done for decades to come. This is a rare chance to help build a generational company at a true inflection point. We have strong product-market fit and world-class investor support. We’re scaling fast and defining a new category in real time. The work is ambitious, the bar is high, and the opportunity for growth — personal, professional, and financial — is unmatched. Our team moves fast, takes ownership, and is deeply committed to the mission — operating with intensity, staying close to our customers, and pushing each other for excellence. We live by three values: Decisiveness, Simplicity, and Job's Not Finished. We act quickly on clear judgment over perfect information, we believe simplicity is what scales, and we're never satisfied with where we are. If you want to do the best work of your career alongside people who share that drive, we'd love to build with you. At Harvey, the future of professional services is being written today — and we’re just getting started. ROLE OVERVIEW In this role, you'll own how we adapt and improve leading open-weight models for legal work. We recently post-trained our first model built for legal work, and we’re now building the team to scale up our post-training efforts. You’ll own the post-training recipe across SFT and RL, including reward design and verification for challenging, semi-verifiable legal tasks. You’ll help set the research direction and carry ideas from experiments through training runs to models used by customers. You’ll join a small, high-bar team of hands-on engineers and researchers, where each person owns a large part of the work. We’re looking for research engineers and scientists who have gotten training runs working at scale, are immersed in RL for language models, and want their work to reach users quickly. Learn more about our work at harvey.ai/research http://harvey.ai/research. WHAT YOU'LL DO - Own and pursue a research agenda to improve model capability on long-horizon legal reasoning and agentic workflows. - Design and run post-training experiments across SFT and RL, building on leading open-weight models. - Develop reward functions and verifiers for semi-verifiable tasks, including rubrics, learned preference models, and expert preference data, and study where they break. - Run and debug training at scale, addressing divergence, reward hacking, and instability. - Build and use rigorous evals to track improvements and decide what ships. - Design, implement, test, and debug code across our research stack. WHAT YOU HAVE - Hands-on experience post-training LLMs with SFT and RL, including preference optimization, and direct ownership of training runs and results. - Deep ML fundamentals and experience getting distributed training runs working reliably. - Strong engineering skills in Python, with production-quality code and end-to-end system ownership. - Research experience in RL for language models or reward modeling, with the ability to move quickly from experiments to shipped improvements. Publications welcome, not required. We are hiring for this role across multiple levels. COMPENSATION $230,000 - $450,000 USD DEPENDING ON YOUR LOCATION, AN APPLICANT PRIVACY NOTICE MAY APPLY TO YOU. YOU CAN FIND ALL OF OUR APPLICANT PRIVACY NOTICES HERE https://harveyai.notion.site/Harvey-Candidate-Privacy-Notices-319ac3fcdd7a803bb807d5094f249922?pvs=74. #LI-DZ1 Harvey is an equal opportunity employer and does not discriminate on the basis of race, gender, sexual orientation, gender identity/expression, national origin, disability, age, genetic information, veteran status, marital status, pregnancy or related condition, or any other basis protected by law. We are committed to providing reasonable accommodations to applicants with disabilities, and requests can be made by emailing accommodations@harvey.ai

Location: London Workplace: Hybrid WHY HARVEY At Harvey, we’re transforming how legal and professional services operate. By combining frontier agentic AI, an enterprise-grade platform, and deep domain expertise, we’re reshaping how critical knowledge work gets done for decades to come. This is a rare chance to help build a generational company at a true inflection point. We have strong product-market fit and world-class investor support. We’re scaling fast and defining a new category in real time. The work is ambitious, the bar is high, and the opportunity for growth — personal, professional, and financial — is unmatched. Our team moves fast, takes ownership, and is deeply committed to the mission — operating with intensity, staying close to our customers, and pushing each other for excellence. We live by three values: Decisiveness, Simplicity, and Job's Not Finished. We act quickly on clear judgment over perfect information, we believe simplicity is what scales, and we're never satisfied with where we are. If you want to do the best work of your career alongside people who share that drive, we'd love to build with you. At Harvey, the future of professional services is being written today — and we’re just getting started. ROLE OVERVIEW In this role, you'll own how we adapt and improve leading open-weight models for legal work. We recently post-trained our first model built for legal work, and we’re now building the team to scale up our post-training efforts. You’ll own the post-training recipe across SFT and RL, including reward design and verification for challenging, semi-verifiable legal tasks. You’ll help set the research direction and carry ideas from experiments through training runs to models used by customers. You’ll join a small, high-bar team of hands-on engineers and researchers, where each person owns a large part of the work. We’re looking for research engineers and scientists who have gotten training runs working at scale, are immersed in RL for language models, and want their work to reach users quickly. Learn more about our work at harvey.ai/research http://harvey.ai/research. WHAT YOU'LL DO - Own and pursue a research agenda to improve model capability on long-horizon legal reasoning and agentic workflows. - Design and run post-training experiments across SFT and RL, building on leading open-weight models. - Develop reward functions and verifiers for semi-verifiable tasks, including rubrics, learned preference models, and expert preference data, and study where they break. - Run and debug training at scale, addressing divergence, reward hacking, and instability. - Build and use rigorous evals to track improvements and decide what ships. - Design, implement, test, and debug code across our research stack. WHAT YOU HAVE - Hands-on experience post-training LLMs with SFT and RL, including preference optimization, and direct ownership of training runs and results. - Deep ML fundamentals and experience getting distributed training runs working reliably. - Strong engineering skills in Python, with production-quality code and end-to-end system ownership. - Research experience in RL for language models or reward modeling, with the ability to move quickly from experiments to shipped improvements. Publications welcome, not required. We are hiring for this role across multiple levels. DEPENDING ON YOUR LOCATION, AN APPLICANT PRIVACY NOTICE MAY APPLY TO YOU. YOU CAN FIND ALL OF OUR APPLICANT PRIVACY NOTICES HERE https://harveyai.notion.site/Harvey-Candidate-Privacy-Notices-319ac3fcdd7a803bb807d5094f249922?pvs=74. #LI-DZ1 Harvey is an equal opportunity employer and does not discriminate on the basis of race, gender, sexual orientation, gender identity/expression, national origin, disability, age, genetic information, veteran status, marital status, pregnancy or related condition, or any other basis protected by law. We are committed to providing reasonable accommodations to applicants with disabilities, and requests can be made by emailing accommodations@harvey.ai