Location: Remote, Global
About the Role
We're looking for a FinOps Data Engineer to help drive cost optimization and cloud financial governance across our AWS infrastructure and GCP footprint. You'll work cross-functionally with Engineering, Finance, and Product to find and ship savings, build the tooling necessary to maximize efficiency of every dollar spent in the cloud, and cultivate a culture of cost awareness across engineering teams. This role owns the data side of that mission: the models, pipelines, and analysis that make cost, usage, and margin numbers trustworthy across the company.
This is a hands-on engineering role, not an analyst seat. You'll do the schlep work other teams don't have time for, digging into a bucket's access pattern, writing the dbt model or Python pipeline that ships the fix, building the cost-vs-revenue mart that answers a margin question once instead of every time, and chasing a number until it reconciles. Strong communication matters as much as technical depth, you'll regularly explain a savings case or a data model's implications to an engineering team, a finance stakeholder, and a leadership audience in the same week.
What You'll Be Responsible For
In this role, you'll:
- Find and ship savings across AWS and GCP: Analyze billing, usage, and cost trends to identify opportunities, rightsizing, storage lifecycle policies, data transfer, and spot/auto-scaling. Take ideas from identification through to a realized dollar figure, not just a recommendation.
- Build and maintain the FinOps data pipeline: Design and run the BigQuery/dbt models that turn raw billing and usage data (CUR, Storage Lens) into reconciled, trustworthy mart tables, the foundation everything else in FinOps is built on.
- Build cost-vs-revenue and customer segmentation models: Model unit economics and margin by product line and customer segment, and extend coverage as new products and segments come online.
- Apply data science to cost and usage data: Build forecasting models, detect anomalies statistically rather than by threshold alone, and quantify savings opportunities (e.g. storage lifecycle, request-cost patterns) with rigor a spreadsheet can't match.
- Build and maintain cost attribution: Tagging policies, cost allocation, and chargeback/showback so engineering teams can see and own their own spend. This is foundational work other savings work depends on.
- Automate cost governance: Build automated cost reporting, anomaly detection, and alerting. Write the scripts and infrastructure-as-code (Python, Terraform, Lambda) that enforce cost policy rather than relying on manual review.
- Partner with engineering on architecture decisions: Bring cost into the room early, cost-efficiencies are cheaper to get right at design than to retrofit.
- Work with Finance on forecasting and reporting: Support budget tracking and financial KPIs; build dashboards and self-serve analytics engineering and finance can both read.
You Might Be a Good Fit If You
- Have strong AWS expertise and working GCP knowledge, including pricing models, billing structures, and cost optimization mechanics.
- Have data engineering depth: dbt, BigQuery (or an equivalent modern warehouse), SQL at scale, and data modeling for analytics (dimensional modeling, incremental models, testing).
- Have data science fundamentals: statistical forecasting, anomaly detection, and experimentation, and know when a rigorous model beats a rule of thumb.
- Have automation and scripting skills: Python, Terraform/ pulumi, Lambda, or Bash.
- Are comfortable with cost/billing tooling: AWS Cost Explorer, Cost & Usage Reports, GCP Billing Reports, and BI/dashboarding (we use Hex).
- Have experience with, or are comfortable picking up, Linear and Notion as the system of record for tracking and documenting FinOps work.
- Experience with a dedicated cloud cost management platform (e.g. CloudZero, Vantage, or similar) is a plus, not required.
- Can analyze cost data and present it clearly to both technical and non-technical audiences.
- Have prior experience spanning data analytics, data engineering, or data science in a FinOps, cloud cost management, or cloud governance context.
- Are energized by doing the unglamorous, high-leverage work (the schlep) that unblocks other teams, and by working cross-functionally without positional authority.
WHAT WE OFFER
- Fully Remote
We hire globally. We believe you can do your best work from anywhere. There are no Supabase offices, but we provide a WeWork membership or co-working allowance you can use anywhere in the world.
- ESOP
Every team member receives ESOP (equity ownership) in the company. We want everyone to share in the upside of what we’re building together.
- Tech Allowance
Use this budget to set up your ideal work environment—laptop, monitor, headphones, or whatever helps you do your best work.
- Health Benefits
Supabase covers 100% of health insurance for employees and 80% for dependents, wherever you are. Your wellbeing and your family’s health are important to us.
- Annual Off-Sites
Once a year, the entire company gathers in a new city for a week of connection, collaboration, and fun. It’s a highlight of our year.
- Flexible Work
We operate asynchronously and trust you to manage your own time. You know what needs to be done and when.
- Professional Development
Every team member receives an annual education allowance to spend on learning—courses, books, conferences, or anything that supports your growth.
ABOUT THE TEAM
Supabase was born-remote and open-source-first. We believe our globally distributed team is our secret weapon in building tools developers love.
- ~400 team members
- 60+ countries
- 20+ languages spoken
- Over $1B raised (including our $500M Series F)
- 540,000+ community members
We move fast, build in public, and use what we ship. If it’s in your project, we probably use it in ours too. We believe deeply in the open-source ecosystem and strive to support—not replace—existing tools and communities.
Задачи:
— вести сбор требований с бизнес‑стейкхолдерами
— проектировать модели данных по паттернам medallion‑архитектуры (bronze/silver/gold)
— строить и оптимизировать ETL/ELT‑пайплайны с dbt
— настраивать проверки качества данных (Great Expectations)
— оптимизировать Spark‑джобы под SLA
— диагностировать проблемы в многосистемных пайплайнах
— поддерживать документацию и runbook’и
Требования:
— от 5 лет опыта в Data Engineering
— продвинутое владение Python, Spark и SQL
— опыт работы с AWS и Databricks
— знание Kafka и инструментов современного Data‑стека (dbt, Airflow или аналогов)
— понимание dimensional‑моделирования (star/snowflake‑схемы)
— понимание принципов проектирования хранилищ данных и архитектур (lambda, kappa, lakehouse)
— умение структурировать задачи для работы с ИИ‑ассистентами
— знание топологий систем
— уровень английского — Intermediate и выше.
• От 5 лет опыта в Data Science / ML Engineering, из них от 2 лет в управлении DS/ML-командой. • Обязательный production-опыт с recommendation systems (retrieval, ranking, personalization).
• Коммерческий опыт построения и развития DWH и data pipelines. • Уверенный коммерческий опыт минимум с 3 из 4 инструментов: ClickHouse, Debezium, dbt, Airflow.
About the Team Launched in 2019, Constructor is an AI-first ecommerce search and discovery platform that helps shoppers find the right products at the right time and enables leading global e-commerce brands to drive meaningful revenue and conversion gains.
As a member of the Personalization team, you will improve the e-commerce experience for hundreds of millions of users across the world by designing, delivering & maintaining high-load real-time services in close collaboration with great engineers both within the team and across other teams such as the Ranking team.
The general goal of the Personalization team is providing best-in-class personalization for all e-commerce experiences provided by Constructor.
The job can consist of, but is not limited to:
Improve personalized experience across different products, domain verticals and platforms
Improve business KPIs by validating hypotheses around personalization
Research different personalization approaches to provide the best outcome to the business
Model user interactions to provide the best experience to shoppers
Collaborate with technical and non-technical business partners to develop and update personalization capabilities
Communicate with stakeholders within and outside the team
Requirements
Hard skills
3+ years of professional experience in applied machine learning
Proficiency with Python, SQL and the big data stack for end-to-end ML product development
Experience building, shipping, and maintaining personalization / recommender / ranking systems in production
Experience with any of the major public cloud platforms will be a plus
Experience with design and analysis of A/B tests
Great skills delivering & communicating business value to stakeholders
Soft skills
Excellent English communication skills
Experience collaborating in cross-functional teams
Ability to deep dive into problems and provide actionable outcomes
Ability to work autonomously and independently
Proactivity
Benefits
Work with smart and empathetic people who will help you grow and make a meaningful impact.
Regular team offsite events to connect and collaborate.
Fully remote team - choose where you live.
Unlimited vacation time - we strongly encourage all of our employees take at least 3 weeks per year.
Work from home stipend!
We want you to have the resources you need to set up your home office.
Apple laptops provided for new employees.
Training and development budget for every employee, refreshed each year.
Maternity & Paternity leave for qualified employees.
Base salary: $80k–$120K USD, depending on knowledge, skills, experience, and interview results
Stock options - offered in addition to the base salary.
Diversity, Equity, and Inclusion at Constructor
Constructor.io is committed to cultivating a work environment that is diverse, equitable, and inclusive.
As an equal opportunity employer, they welcome individuals of all backgrounds and provide equal opportunities to all applicants regardless of their education, diversity of opinion, race, color, religion, gender, gender expression, sexual orientation, national origin, genetics, disability, age, veteran status or affiliation in any other protected group.
They encourage you to apply even if you don’t meet all of their listed qualifications.
How they handle your data
By submitting this application, you acknowledge that Constructor will collect and process the personal data you provide for the purpose of evaluating your candidacy for this role.
If you are not selected, they will retain your data for a limited period as described in their Recruitment Privacy Notice.
After that period your data will be deleted or anonymized.
Location: United States; Canada
Workplace: OnSite
WHO ARE WE?
Cohere is the leading security-first enterprise AI company. We build cutting-edge foundation AI models and end-to-end products that are designed to solve real-world business problems.
We’re training and deploying frontier models for enterprises who are building AI systems. We believe that our work is instrumental to the widespread adoption of AI and we are looking for folks that want to be part of that.
We obsess over what we build. Each one of us is responsible for contributing to increasing the capabilities of our models and the value they drive for our customers. Cohere is a team of researchers, engineers, designers, and more, who are all passionate about their craft.
We are a global technology company headquartered in Toronto with key offices in London, New York City, San Francisco, Montreal, Paris, Berlin and Seoul. Join us!
Role Overview
As a Member of Technical Staff in Post-Training Data Infrastructure, you will build and own the pipelines that produce the post-training data for our large language models (LLMs), from SFT and RL data to synthetic and human-annotated data. You will work closely with researchers and annotation teams so they can iterate on data as quickly as they iterate on models.
Key Responsibilities
- Build and maintain data pipelines for SFT, preference, and RL post-training data.
- Own dataset quality, versioning, and reproducibility.
- Turn research data recipes into production-grade, reusable components.
- Scale model inference for data generation and model-based grading.
- Design data formats and schemas that keep post-training datasets consistent and efficient to train on.
- Collaborate with researchers and engineers to make sure data pipelines keep pace with the needs of cutting-edge language models.
Qualifications
- Strong software engineering skills.
- Experience designing and building large-scale data pipelines for machine learning, ideally for LLM post-training.
- Track record of running distributed data processing and model inference at scale.
- Good understanding of post-training methods (e.g., SFT, RLHF, DPO) and how data shapes model behaviour.
- Excellent communication skills.
This is neither an exhaustive nor necessary set of attributes. Even if none of these apply to you, but you believe you will contribute to Cohere, please reach out. We value diverse backgrounds and perspectives at Cohere
FULL-TIME EMPLOYEES AT COHERE ENJOY THESE PERKS:
- A weekly lunch stipend of $75/£75 or equivalent in your local currency for lunch.
- Full health and dental benefits, including a separate budget for mental health.
- RRSP matching, 401K, Pension Scheme.
- 100% Parental Leave top-up for up to 6 months, for either parent.
- Annual enrichment benefits:
Arts & culture, fitness/wellness, quality time, and a workspace improvement credit.
Education & learning stipend for conferences, courses, and coaching.
- 6 weeks of paid vacation (30 working days!)
- Budget for traveling to other offices if you are remote, plus an annual company offsite.
HOW AND WHERE WE WORK:
- Cohere is remote-friendly, but we also have offices in Toronto, London, New York City, San Francisco, Montreal, Paris, Berlin and Seoul with more opening soon.
- For those in the office: a daily lunch program, plenty of snacks, and regular community and social events.
- For those not near an office: a co-working benefit so you can work alongside others in your city.
- Everyone receives a $500 home office stipend to set up your workspace properly.
If any of the above doesn’t line up exactly with your experience, we still encourage you to apply.
We strive to create an inclusive work environment for all; we welcome applicants from all backgrounds and are committed to providing equal opportunities. Should you require any accommodations during the recruitment process, please submit an Accommodations Request Form https://docs.google.com/forms/d/12a6IrLdF3kI2nonKSr4tiFuz18rLQbaeYV-JM9L4o9Q/edit, and we will work together to meet your needs.
We may use AI-enabled tools to screen and assess applicants against the criteria for this position. This helps our recruiters identify potentially qualified candidates, but it doesn't limit the applications our recruiters may review or consider.
Beware of Scams: Cohere will never ask for payment or third-party services (e.g., CV writing) as part of our hiring process. All legitimate roles are listed on the Cohere careers page and LinkedIn only, with all communications from Cohere employees coming from an @cohere.com or @cw.cohere email alias. If jobs are viewed on other sites then please verify these through our official careers https://cohere.com/careers page.
Location: Europe; New York; Toronto
Workplace: Remote
WHO ARE WE?
Cohere is the leading security-first enterprise AI company. We build cutting-edge foundation AI models and end-to-end products that are designed to solve real-world business problems.
We’re training and deploying frontier models for enterprises who are building AI systems. We believe that our work is instrumental to the widespread adoption of AI and we are looking for folks that want to be part of that.
We obsess over what we build. Each one of us is responsible for contributing to increasing the capabilities of our models and the value they drive for our customers. Cohere is a team of researchers, engineers, designers, and more, who are all passionate about their craft.
We are a global technology company headquartered in Toronto with key offices in London, New York City, San Francisco, Montreal, Paris, Berlin and Seoul. Join us!
Key Overview:
As a Member of Technical Staff in Post-Training, you will train our large language models (LLMs) with reinforcement learning and develop methods that help them generalize beyond the distribution they were trained on. You will design the training recipes, environments, and evaluations that tell us whether a model has truly learned a skill or has only fitted its training tasks, and turn what works into the recipes behind our production models. You will work closely with the post-training data infrastructure, evaluation, and pre-training teams.
Key Responsibilities
- Design, run, and analyse large-scale RL post-training experiments (e.g., RLHF, RL from verifiable rewards).
- Research and develop methods that improve out-of-distribution generalization, such as reward design, environment and task diversity, curricula, and regularization.
- Build held-out evaluations that measure generalization to unseen tasks, domains, and formats, and that catch reward hacking and benchmark overfitting.
- Diagnose training dynamics and failure modes, including instability, reward over-optimization, and capability regressions.
- Turn successful research into robust, reproducible training recipes for production models.
Qualifications
- Strong research and software engineering skills, with hands-on experience training LLMs.
- Experience applying reinforcement learning to LLMs (e.g., PPO, GRPO, RLHF, RLVR), ideally at scale.
- Track record of work on generalization, robustness, or distribution shift, through publications or shipped models.
- Rigorous experimental practice: clean ablations, careful controls, and sound conclusions from noisy results.
- Experience with distributed training (e.g., JAX, PyTorch, FSDP, Megatron) is a plus.
- Excellent communication skills.
- Deep understanding of data quality principles and best practices: demonstrates willingness to dive deep into data while possessing strong intuition for assessing the quality of datasets and anticipating how different data characteristics will impact model training outcomes.
Working Location
This role can be based remotely or from one of our office locations listed on the job description - there is no minimum in-office qualification requirement. We care most about hiring exceptional people regardless of locations, though please check the location listed on the posting for guidance around the core time zone or working hours alignment expected for the role.
FULL-TIME EMPLOYEES AT COHERE ENJOY THESE PERKS:
- A weekly lunch stipend of $75/£75 or equivalent in your local currency for lunch.
- Full health and dental benefits, including a separate budget for mental health.
- RRSP matching, 401K, Pension Scheme.
- 100% Parental Leave top-up for up to 6 months, for either parent.
- Annual enrichment benefits:
Arts & culture, fitness/wellness, quality time, and a workspace improvement credit.
Education & learning stipend for conferences, courses, and coaching.
- 6 weeks of paid vacation (30 working days!)
- Budget for traveling to other offices if you are remote, plus an annual company offsite.
HOW AND WHERE WE WORK:
- Cohere is remote-friendly, but we also have offices in Toronto, London, New York City, San Francisco, Montreal, Paris, Berlin and Seoul with more opening soon.
- For those in the office: a daily lunch program, plenty of snacks, and regular community and social events.
- For those not near an office: a co-working benefit so you can work alongside others in your city.
- Everyone receives a $500 home office stipend to set up your workspace properly.
If any of the above doesn’t line up exactly with your experience, we still encourage you to apply.
We strive to create an inclusive work environment for all; we welcome applicants from all backgrounds and are committed to providing equal opportunities. Should you require any accommodations during the recruitment process, please submit an Accommodations Request Form https://docs.google.com/forms/d/12a6IrLdF3kI2nonKSr4tiFuz18rLQbaeYV-JM9L4o9Q/edit, and we will work together to meet your needs.
We may use AI-enabled tools to screen and assess applicants against the criteria for this position. This helps our recruiters identify potentially qualified candidates, but it doesn't limit the applications our recruiters may review or consider.
Beware of Scams: Cohere will never ask for payment or third-party services (e.g., CV writing) as part of our hiring process. All legitimate roles are listed on the Cohere careers page and LinkedIn only, with all communications from Cohere employees coming from an @cohere.com or @cw.cohere email alias. If jobs are viewed on other sites then please verify these through our official careers https://cohere.com/careers page.
Требования: умеешь писать чистый и понятный код на современном Python; хорошо ориентируешься в классическом ML и соответствующих библиотеках (scikit-learn, CatBoost);
Нужен опыт управления командой аналитиков, BI или Data от 3 лет и СИЛЬНАЯ ЭКСПЕРТИЗА В КОММЕРЧЕСКОЙ АНАЛИТИКЕ, ОСОБЕННО В ПРОДАЖАХ НА МАРКЕТПЛЕЙСАХ: АССОРТИМЕНТ, МАРЖИНАЛЬНОСТЬ, ЭФФЕКТИВНОСТЬ КАНАЛОВ И ДИНАМИКА ПРОДАЖ.
Обязанности:
— Ведение делопроизводства редакции (табель, служебная документация и т.п.).
— Помощь редакторам в подготовке и сдаче книг.
— Работа с базой 1С.
— Ведение проектов в 1С.
— Внесение данных и изменение в программе учета 1С.
— Формирование отчетов из программ учета по запросам руководителей.
— Распечатка/пересылка рукописи для рецензента/редактора и др.
— Ксерокопирование документов.
— Информирование об отказе рукописи.
— Подготовка и предоставление комплекта сопроводительных документов для производства рукописи.
— Внесение изменения в сопроводительные документы.
— Внутренняя сверка макетов, проверка колонтитулов и колонцифр (помощь редакторам).
— Составление презентаций для продвижения книг.
— Осуществление взаимодействия с другими редакциями и службами.
— Сдача допечаток.
— Отслеживание информации о запрещенных веществах и лгбт-контента, при необходимости внесение информации в 1С, выходные данные и обложку книги.
Требования:
— Высшее/Среднее специальное образование (в области издательского дела).
— Знание основ полиграфического производства, полиграфических материалов и издательских процессов.
— Уверенный пользователь ПК: Word, Excel, PowerPoint, желательно 1С.
— Организованность, стрессоустойчивость, многозадачность, коммуникабельность.
Условия:
— Полная пятидневная рабочая неделя (10.00 - 18.30) в офисе в Москва-Сити, Башня "Империя" (метро и МЦК "Деловой центр", метро Москва-Сити).
— После испытательного срока один удаленный день.
— Оклад, премирование по системе KPI после испытательного срока.
— Соблюдение ТК РФ.
— Социальный пакет: добровольное медицинское страхование и страхование выезжающих за рубеж, программы заботы о здоровье, занятия спортом, значительная скидка на книги и другую продукцию издательства.
— Обучение и профессиональное развитие, внутренние и внешние курсы и тренинги.
— Публичная оценка выдающихся результатов и призы для победителей на корпоративной Премии «Признание».
— Планирование карьеры в Издательской группе.
— Справедливое вознаграждение и премирование по системе KPI.
