Вакансии

Location: New York City, New York What you’ll be part of Garner is on a mission to transform the U.S. healthcare system — and we’re the only proven player doing exactly that. We partner with employers to redesign how healthcare works: applying 550+ proprietary clinical metrics across 80+ specialties to a dataset of 320M+ patients to identify the best-performing doctors, then using compelling incentives to steer members to the care that helps them get healthier, faster. The result is a rare “win win” — better care and lower costs for both members and employers. In just five years, our work has helped over 2.5 million people access higher-quality care and saved $1B in healthcare costs. We recently raised our Series E and have doubled five years running. If you've ever wanted your work to solve a problem that touches every person in this country, this is the opportunity to do exactly that. You'd be joining a team fundamentally reimagining healthcare in the U.S. — and using AI to scale that impact further and faster than anyone else can. About the role: We are seeking an exceptional Staff Machine Learning Operations Engineer to join our Platform Engineering team . This role will report to the VP of Platform Engineering . As Garner's foundational dedicated MLOps Engineer, you will assume responsibility for the reliability, performance, and cost-efficiency of our production machine learning systems. You will lead the development of a robust platform designed to facilitate the secure and consistent deployment of models by our machine learning and data science teams. Given that these models directly influence health outcomes and cost-effectiveness for millions of patients, maintaining the highest standards of production quality is imperative. Where you will work: This role will be based in our New York City office (in the Financial District). You must be willing to work in the office 3 days per week on Tuesday, Wednesday and Thursday. What you will do: Own the reliability, performance, functionality, and cost-efficiency of Garner's production ML systems, including establishing SLOs, observability, and on-call responsibilities. Architect Garner's ML platform including required data infrastructure (including feature store, model registry and CI/CD for models), and standardized service patterns. Implement ML-specific CI/CD pipelines: Transition our deployment process from manual notebook hand-offs to automated, PR-driven CI/CD workflows that include automated data quality checks and statistical model validation prior to deployment. Drive down cost and latency through improved architecture, hardware choices, and model optimization as appropriate. Lay the foundation for a future Garner MLOps team, including workflows, standards, and KPIs that enables rapid teammate onboarding and helps stakeholders and teammates quickly identify the health of the team’s products, allowing engineers to focus on areas where issues reside Establish Drift Monitoring: Design and implement automated data drift and concept drift monitoring systems that alert the team when models degrade, laying the groundwork for future Continuous Training (CT) architectures The ideal candidate has: 7+ years of software engineering experience, with significant time spent operating ML or data-intensive systems in production at scale. Deep experience with the modern ML production stack: model serving (e.g., Sagemaker, Triton, or equivalent), feature stores, model registries, and CI/CD for ML. Strong infrastructure and platform engineering fundamentals: Kubernetes, containerization, cloud (AWS preferred), Terraform/IaC, observability, and incident response. Experience designing ML platforms or significant components of one (not strictly consuming SaaS) and the judgment to know when to build vs. buy. Strong collaboration with ML, data, platform engineers, data scientists, and product engineering teams, with the ability to set technical direction as the most senior MLOps voice in the org. Healthcare, regulated-data, or other high-stakes production ML experience is a plus but not required. A desire to be a part of a high-performing, mission-driven team that operates with intense urgency, a strong sense of individual accountability, and a commitment to authentic feedback What you’ll get here: You’ll work on problems that matter, at a company working to change healthcare at scale. You’ll work at the intersection of AI and systemic healthcare reform, where the problems we solve are as interesting and compelling as the mission. At Garner, you’ll take on real, ambitious problems with real ownership and autonomy, alongside exceptional, principles-based people who genuinely want you to win. It’s demanding by design. You’ll be challenged to stretch beyond what you thought possible and receive consistent coaching to help you grow and do the best work of your career. This isn't the right fit for everyone, and that's intentional. The people here are driven by what's at stake for real people, and that's what gives our intensity its purpose. Technologies we use: Python, Kubernetes, AWS, Sagemaker, Terraform, S3, Snowflake, Airflow, Datadog This is a unique opportunity to join a fast-growing company in a transformative role, helping shape the future of healthcare. Compensation Transparency: The target salary range for this position is $298,000 - $351,000. Individual compensation for this role will depend on various factors, including qualifications, skills, and applicable laws. In addition to base compensation, this role is eligible to participate in our equity incentive and competitive benefits plans, including but not limited to: flexible PTO, Medical/Dental/Vision plan options, 401(k), Teladoc Health and more. Fraud and Security Notice: Please be aware of recent job scam attempts. Our recruiters use getgarner.com and garnerhealth.com email domains exclusively. If you have been contacted by someone claiming to be a Garner recruiter or a hiring manager from a different domain about a potential job, please report it to law enforcement here and to candidateprotection@garnerhealth.com . Equal Employment Opportunity: Garner Health is proud to be an Equal Employment Opportunity employer and values diversity in the workplace. We do not discriminate based upon race, religion, color, national origin, sex (including pregnancy, childbirth, reproductive health decisions, or related medical conditions), sexual orientation, gender identity, gender expression, age, status as a protected veteran, status as an individual with a disability, genetic information, political views or activity, or other applicable legally protected characteristics. Garner Health is committed to providing accommodations for qualified individuals with disabilities in our recruiting process. If you need assistance or an accommodation due to a disability, you may contact us at talent@garnerhealth.com .

Location: Armenia; Russia; Serbia; Turkey; Georgia; Montenegro Libertex Group Overview Established in 1997, the Libertex Group has helped shape the online trading industry by merging innovative technology, market movements and digital trends. The multi-awarded online trading platform, Libertex, enables traders to access the market and invest in stocks or trade CFDs with underlying assets being commodities, Forex, ETFs, cryptocurrencies, and others. A firm believer in the power of sports to inspire, empower and push for success, Libertex is the Official Online Trading Partner of Audi Revolut F1® Team. We build innovative fintech so people can #TradeForMore with Libertex. Job Overview We are looking for a Senior Machine Learning Engineer who will play a pivotal role in implementing, enhancing, and maintaining our machine learning solutions. Responsibilities: Work closely with business departments (Sales, Marketing, Products, Payments, Dealing) to understand their needs and translate these into practical, scalable, and robust machine learning solutions. Work on such projects like LTV prediction, churn prediction, antifraud solutions, uplift modelling, GenAI-applications (AI agents, chatbots, MCP) Oversee current solutions, manage improvements if needed, expand the role of machine learning across organisation. Implement new and improve current MLOps practices, laying the foundation for efficient and effective model development, deployment, and monitoring. Requirements Great understanding of business and where ML solutions can fit and bring value Technological stack - Python, PyTorch,, SQL (MSSQL, Postgres), ClickHouse, AWS (Sagemaker, Bedrock, EC2, EKS), Docker, FastApi, Kafka, Airflow Establishing and maintaining MLOps practices, including version control for models, automated testing, and CI/CD for ML systems Utilise AWS ML (or similar) services for developing and deploying models General understanding of IT infrastructure, various data source types, development processes etc. Great to have previous experience in applying data science tools to mobile apps, gaming or fintech Intermediate level of English Experience with LLMs (AWS Bedrock, langchain, langgraph, RAG, Agents, MCP). Benefits Quarterly bonuses based on Company performance Generous relocation package for the employee and their immediate family Medical Insurance Plan with coverage for the employee and their immediate family from day one 24 working days of annual leave Yearly reimbursement of travel expenses for the employee and family's flight home Corporate events and team building activities Udemy Business unlimited membership & language training courses Professional and personal development opportunities in a fast-growing environment Libertex Group is an equal opportunity employer, fostering an inclusive and diverse environment. We do not discriminate based on any characteristic protected by the law. Candidate privacy is respected, and all data is securely stored and used solely for recruitment purposes, in line with GDPR and our internal policies. Unsuccessful applicants may have their data retained for future opportunities unless deletion is requested.

Location: Hyderabad, India Location: India (100% Remote / Flexible) Contract Type: Full-Time / Enterprise Project Engagement About Xenon7 Where elite tech talent meets world-class opportunities! At Xenon7, we work with leading enterprise clients and innovative startups on high-impact projects across Data, AI, Cloud, and Software Engineering. Our expertise in AI solution architecture and specialized technical talent allows us to partner with enterprise leaders on transformative initiatives, driving innovation and business growth. Job Summary We are seeking a high-caliber Forward Deployment Engineer (FDE) to sit at the intersection of enterprise AI platforms and executive Finance business functions for our Fortune 100 enterprise client. This role bridges advanced AI platform engineering with direct business impact—translating ambiguous financial challenges into working AI applications, deploying them into regulated client environments, and iterating in tight loops to drive measurable ROI. Rather than building static models or waiting for formal spec sheets, you will operate as a product-minded technical lead. You will prototype user-facing tools in days, deploy agentic and RAG workflows on top of enterprise data stacks (Databricks Genie), and ensure high adoption across FP&A, Controllership, Treasury, and CFO leadership. Key Responsibilities End-to-End AI Application Prototyping & Delivery Own the full lifecycle of AI solutions for Finance business units—from initial discovery with CFO stakeholders to production deployment, user adoption, and ROI measurement. Rapidly prototype vertical applications using Streamlit, Databricks Apps, FastAPI/Flask, or lightweight React interfaces within 1–2 weeks to gather real user feedback. Handle "last mile" execution: edge cases, data quirks, business rule exceptions, and user training to turn prototypes into sticky enterprise products. Agentic Systems & LLM Engineering Build production-grade vertical AI tools leveraging Databricks Genie (Genie Spaces, semantic models). Construct robust RAG pipelines incorporating advanced chunking, vector databases (Pinecone, Chroma, FAISS, Azure AI Search, Cortex Search), retrieval evaluation, grounding, and citation. Implement LLM orchestration frameworks (LangChain, LangGraph, LlamaIndex) and apply rigorous prompt engineering with hallucination and accuracy evaluation discipline. Enterprise Data & Regulated Integration Deploy applications inside heavily regulated enterprise environments, adhering strictly to RBAC, data residency, audit, and compliance constraints (SOX, GxP). Connect solutions to governed datasets, semantic models, and core enterprise platforms (SAP, Workday, Coupa, ERP) via secure REST APIs. Instrument all deployed apps with usage metrics, telemetry, and business outcome tracking from day one. Strategic Stakeholder Advisory & Pattern Extraction Work directly with senior Finance leadership (CFO office, FP&A, Controllership) to translate ambiguous operational friction into crisp technical roadmaps. Extract reusable code components, prompt templates, evaluation harnesses, and retrieval patterns into shared enterprise assets for broader deployment. Requirements Experience & Mindset Experience: 5+ years of hands-on software development and full-stack AI application deployment. Product-Shaped Builder: Strong bias for action; comfortable shipping rough prototypes in Week 1 over polished spec documents in Week 4. Ambiguity Tolerance: Proven ability to navigate vague business problems without predefined specifications. Stakeholder Communication: Outstanding ability to interface directly with senior business leaders without requiring intermediary Business Analysts. Must-Have Technical Stack Languages & Frameworks: Python, FastAPI/Flask, SQL, and UI frameworks (Streamlit, Databricks Apps, React). AI Platforms: Direct hands-on experience with Databricks Genie . LLM Engineering: Production experience with LangChain, LangGraph, LlamaIndex, vector search engines, and prompt evaluation harnesses. Cloud & DevOps: Deep working knowledge of Cloud platforms (Azure preferred, AWS/GCP acceptable), modern Git workflows, CI/CD, and basic MLOps awareness (observability, versioning via LangSmith, Datadog, etc.). Domain Competency (Finance Focus) Solid foundational understanding of core Finance business processes (FP&A, order-to-cash, procure-to-pay, record-to-report, close cycles) and common financial metrics (OPEX, EBITDA, gross margin, working capital). Nice-to-Haves & Certifications Prior FDE, Solutions Engineer, or Field Engineer experience at high-growth AI/Data companies (Palantir, Databricks, OpenAI, Anthropic). Industry background in Life Sciences, Pharma, Healthcare, or CPG enterprise Finance. Experience with enterprise Finance software (SAP S/4HANA, Veeva, Workday Adaptive, Oracle Financials). Certifications: Databricks Certified, or Azure AI Engineer Associate (AI-102). What This Role Is NOT Not a pure Data Scientist or ML Researcher: You will not be training or fine-tuning foundation models from scratch; you are applying existing models to business problems. Not a non-coding Architect: This is a 100% hands-on builder role. Not a pure Backend or MLOps Engineer: You must be comfortable owning the user-facing interface and direct business interaction.

Location: San Francisco Bay Area, California; New York City Metropolitan Area Workplace: remote About the role: Provectus is a Premier AWS partner and an Anthropic Strategic Partner at the forefront of applied AI, helping enterprises turn Claude, agentic systems, and their own data into measurable business outcomes through bespoke applications, managed services, and advisory engagements. With offices in North America, LATAM, and EMEA, we partner with clients worldwide and are obsessed with reimagining how they operate and compete. Our work centers on two verticals — Financial Services & Insurance and Healthcare & Life Sciences — where we deploy five pre-built AI Blueprints: Submission Flow, Portfolio Lens, Asset Flow, Revenue Flow, and Evidence Lens. Each Blueprint rebuilds a critical business process front to back, shipped from working code and tuned to a client's specific book, regulators, and operating posture. We embed engineers and leaders inside client operations as Forward Deployed Engineers (FDE) and Forward Deployed Executives (FDX) — people who learn the work, ship the system, and own the outcome. Our team holds 100+ AWS certifications, is Claude Code certified, and co-delivers Anthropic's Agentic SDLC program, Cowork Activation, and AI Bluprints. You will do the customer’s job before you automate it. Most AI engagements fail the same way: someone gathers requirements, someone writes a PRD, and a team ships a workflow nobody uses. We think the requirements-gathering step is the bug. So we remove it. A Forward Deployed AI Engineer at Provectus spends the first weeks of an engagement in the operator’s seat — as the underwriter, the analyst, the RCM specialist, the claims clinician, whoever actually does the work we’ve been asked to change. You do the job. You learn the constraints from the inside, the ones nobody writes down. Then you sit at a table with that operator and a Forward Deployed Executive and rebuild the function from first principles — and you are the one who builds it. Three things define how you work: Embedded, not engaged . You are part of the customer’s team and inside their process — not a vendor running a project alongside it. Real tasks, not scope . You are not fenced into a siloed deliverable. You go where the operating problem is. Autonomous . Embedded is not staff-augmented. You own the method; nobody hands you a ticket. You won’t start from zero. Provectus builds industry blueprints — working systems that have already shipped for a customer in your industry. Your engagement starts from that baseline, and what you learn in the field goes back into it. That loop is the difference between an outcome and an invoice. You’ll be measured on whether the Business Unit’s number moved — not on hours, not on scope delivered. This is a role for engineers who have led before — as a founder, a CTO, a staff engineer — and who want to stay in the code while owning the outcome. On most days you’ll be the most senior technical person in the room, and you’ll still be the one shipping. Requirements: 8+ years building software , a substantial share of it writing production code you were accountable for. You are hands-on today and intend to stay that way. You will take the operator’s seat. You are genuinely willing to spend weeks doing someone else’s job — claims processing, underwriting, revenue-cycle work — before you write a line of code. Engineers who need to stay in the IDE should not apply. You learn domains fast. Demonstrated ability to become conversant in an unfamiliar business function quickly enough to argue with the people who do it for a living. Shipped GenAI/LLM systems to production — not demos, not notebooks. You’ve handled the parts that get hard after the prototype works. You evaluate. You have built or owned an eval suite for a non-deterministic system, and you can explain what you measured and why. Strong engineering fundamentals — dropped into an unfamiliar codebase or language, you’re productive. Python and/or TypeScript proficiency; depth matters more than stack. Cloud-native delivery on AWS (GCP/Azure a plus): containers, Kubernetes/ECS, IaC, CI/CD, and the operational reality of a system someone else inherits. Credible with senior stakeholders — you can hold a redesign conversation with a BU head and a scoping conversation with a CTO without losing either room. Comfort with ambiguity and ownership. Engagements start underspecified by design. Closing that gap is the job. Solid AI/ML foundations — you understand what the models do well enough to reason about failure modes, not just call the API. Strong hands-on prodcution experience with Claude Code/Cowork. Fluent English, written and spoken. Nice to have: Prior experience as a founder, CTO, or engineering leader who has chosen to return to individual contribution. Real depth in one of our blueprint industries: financial services, insurance, healthcare, asset management . Consulting, professional services, or other embedded customer-facing delivery. Data platform depth: data lakes, warehouses, streaming and real-time analytics, data mesh and data contracts, governance and data quality. MLOps and classical ML: PyTorch, SageMaker, MLflow. Fine-tuning, distillation, or inference/serving optimization. Graph databases (Neo4j, AWS Neptune). IaC depth: AWS CDK, CloudFormation, Terraform. Open-source contributions or public writing on applied AI. What you’ll do: Take the seat Do the operator’s job for two to four weeks at the start of an engagement. Learn the function from inside, not from a requirements doc. Reach working fluency in a new domain — insurance underwriting, healthcare revenue cycle, asset flow — in weeks, not quarters. Sit with the operator and the Forward Deployed Executive and redesign the function from first principles. Discovery, user research, and PRD-writing collapse into one team that re-imagines its own job. You are all three roles. Build Ship production GenAI systems into the customer’s environment — LLM applications, agentic workflows, retrieval and structured-extraction pipelines, and the services around them. Running software, not recommendations. Build the evaluation harness before you build the feature. When the engagement is bound to a business KPI, “it looked good in testing” is not an answer. Define what working means, instrument it, and let the evals drive the design. Write production code across the stack — backend services, data pipelines, and the AI layer. Python and TypeScript are our centre of gravity; we choose tools to fit the customer, not the résumé. Take systems to production on AWS (GCP/Azure where the customer requires it): containerized, observable, and maintainable after we leave. Start from the blueprint, and feed the blueprint. What you learn in the field becomes the baseline the next engagement starts from. Own the outcome Work in a pair with a Forward Deployed Executive who carries the Business Unit’s KPIs. Your work is measured against the same number. Drive adoption. A system the BU routes around has not shipped. Change management is part of the engineering job here, not a phase after it. Be credible with the customer’s engineers, their operators, and their executives — and be willing to disagree with all three. Shape what we commit to before we commit to it. You’ll have the standing to do it, because you’re the one who will build it. What We Offer: Frontier delivery work across Cowork Activation, Agentic SDLC, and Blueprint Activations in Financial Services and Healthcare The chance to shape how leading enterprises adopt AI, from strategy through first deployment A forward-deployed model working in small, senior teams alongside Principal Architects and Forward Deployed Engineers A growing AI delivery practice where you help build the tooling and frameworks, not just use them Remote-friendly culture High-impact role with direct visibility to leadership. Strong earning potential with performance-based bonuses. Opportunity to work with cutting-edge AI and cloud solutions. B2B contract model or full-time model. Unlimited Vacation policy. Generous health, vision, and dental insurance. 401(K) matching plan. OTE range: up to $250k. The salary range is determined through interviews and a review of the education, experience, knowledge, skills, abilities of the applicant, and alignment with market data. How we hire: Intro conversation — the role, your background, what you want to be doing. Two live engineering sessions. Real problems, your own editor. You may use an LLM assistant (ChatGPT, Claude) — how you work now includes these tools. Autocomplete/agentic coding tools are off for these sessions. The redesign session. We hand you an unfamiliar business function and the constraints of the person who performs it. You have to understand the job well enough to rebuild it — then say what you’d build and how you’d know it worked. No LLMs for this one. Team and practice conversation.

Location: United States, San Mateo, CA Why Sony Interactive Entertainment? Sony Interactive Entertainment isn’t just the Best Place to Play — it’s also the Best Place to Work. Sony Interactive Entertainment (SIE) is the company behind the PlayStation brand. As a subsidiary of Sony Group Corporation, we’re part of a proud legacy of innovation and excellence. SIE is a dynamic technology company, delivering cutting-edge hardware and network services to more than 100 million people and an entertainment leader, home to some of the most beloved and recognizable intellectual properties (IP) in the world. Our role at SIE is to create and nurture the experiences under the PlayStation brand, a name synonymous with entertainment excellence and creativity. Director, Product Management - Data Science San Mateo, CA At Sony Interactive Entertainment, PlayStation creates unforgettable gaming experiences for millions of players around the world. As a global leader in interactive entertainment, we operate at massive scale across a complex digital ecosystem spanning hardware, software, services, commerce, marketing, and player engagement. About the Role We are seeking a Director of Data Science Product Management to lead the strategy, prioritization, and execution of data science to drive better decision-making and player experiences across PlayStation. This role is responsible for portfolio of products across the data science lifecycle - identifying high-value analytical opportunities, shaping predictive and decision products, and ensuring they deliver measurable business outcomes through adoption, monitoring and continuous improvement. The role will lead a team of Data Science Product Managers responsible for applying data science to solve business problems, including capabilities across customer lifetime value, player lifecycle intelligence, forecasting, simulations, audience intelligence and decision automation. The Director will help ensure that SIE’s data science investments are focused on the highest-impact opportunities, built with strong product discipline, and adopted in the workflows where they can create measurable business value. Key Responsibilities Translate business priorities into structured problem statements, investment cases, product strategies, and measurable outcomes, ensuring data science work is managed as a scalable product portfolio rather than one-off analytical requests. Drive product strategy and lifecycle ownership for player value and decision intelligence capabilities, such as CLV, engagement and forecasting, ensuring high quality products are advanced from discovery through productionization, adoption, monitoring, iteration, and long-term value realization. Establish clear criteria for when analytical work should remain exploratory, become a recurring decision process, or graduate into a production-grade product. Define success metrics and quality standards for data science products, including business impact, adoption, reliability, interpretability, model quality, operational sustainability, and decision quality. Partner with Data Science and Analytics leaders to ensure rigor across experimentation, causal inference, forecasting, incrementality measurement, model evaluation, and value analysis, while promoting these approaches as core decision-making tools. Influence senior stakeholders across functions to align strategy, clarify priorities, manage tradeoffs, and drive adoption of data science products. Guide Product Managers across the product lifecycle while directly leading or sponsoring the organization's highest-impact and most ambiguous product initiatives. Create a strong Data Science product culture centered on hypothesis-driven discovery, evidence-based prioritization, measurable business impact and close partnership with cross-functional partners. Qualifications Bachelor’s degree in Business, Data Science, Computer Science, Statistics, Economics, Engineering, or a related field. 15+ years of relevant experience, including 8+ years in digital product management, data product management, AI/ML product management, analytics product management, or related roles. 6+ years of people management experience, including experience leading Product Managers or senior product-oriented analytics leaders. Proven experience leading complex, cross-functional portfolios involving Data Science, ML Engineering, Analytics, Data Engineering, and business stakeholders. Strong understanding of data science and machine learning workflows, including experimentation, predictive modeling, forecasting, segmentation, recommender systems, lifecycle modeling, productionization, and monitoring. Experience with products or capabilities related to CLV/LTV, personalization, churn, propensity modeling, forecasting, audience targeting, marketing optimization, commerce, or digital engagement. Familiarity with modern data and ML ecosystems such as SQL, Python, Snowflake, Spark, feature stores, MLOps tools, model monitoring, APIs, and analytics platforms. Demonstrated ability to translate ambiguous business needs into product strategies, roadmaps, priorities, and measurable outcomes. Strong executive communication skills, including the ability to translate technical concepts into business impact. Experience leading, mentoring, and developing high-performing product teams. Preferred Skills Experience in gaming, digital commerce, subscription, marketplace, streaming, or consumer technology businesses. Deep experience with customer intelligence, player lifecycle analytics, personalization, experimentation, audience intelligence, or growth optimization. Experience defining success metrics and quality standards for data science products, including adoption, reliability, latency, interpretability, fairness, model drift, and business impact. Experience building product management capabilities, teams, or operating models from the ground up. Demonstrated ability to use AI-enabled tools, including generative or agentic AI, to improve product workflows, accelerate insight generation, and enhance decision-making. At SIE, we consider several factors when setting each role’s base pay range, including the competitive benchmarking data for the market and geographic location. Please note that the base pay range may vary in line with our hybrid working policy and individual base pay will be determined based on job-related factors which may include knowledge, skills, experience, and location. In addition, this role is eligible for SIE’s top-tier benefits package that includes medical, dental, vision, matching 401(k), paid time off, wellness program and coveted employee discounts for Sony products. This role also may be eligible for a bonus package. Click here to learn more. The estimated base pay range for this role is listed below. $273,000 — $409,600 USD Please note, Sony Interactive Entertainment conducts background checks at the offer stage for all new employees (which may include criminal background checks for some roles) and will need to process personal information to support these checks. Please refer to our Candidate Privacy Notice for more information about what personal information we collect, how we use it, who we share it with, and your data protection rights. Equal Opportunity Statement: Sony is an Equal Opportunity Employer. All persons will receive consideration for employment without regard to gender (including gender identity, gender expression and gender reassignment), race (including colour, nationality, ethnic or national origin), religion or belief, marital or civil partnership status, disability, age, sexual orientation, pregnancy, maternity or parental status, trade union membership or membership in any other legally protected category. We strive to create an inclusive environment, empower employees and embrace diversity. We encourage everyone to respond. Sony Interactive Entertainment is a Fair Chance employer and qualified applicants with arrest and conviction records will be considered for employment.

Задачи: — Разрабатывать и поддерживать масштабируемые сервисы ML-моделей, оптимизируя их производительность и эффективность. — Проектировать инфраструктуру, обеспечивающую критически важные входные данные для алгоритма диспетчеризации, влияющего на опыт миллионов клиентов по всему миру. — Тесно сотрудничать с дата-сайентистами и инженерами для понимания их потребностей и создания масштабируемых решений. — Разрабатывать и улучшать инструменты ML-инжиниринга для разработки моделей, мониторинга, обслуживания и экспериментов. — Использовать облачную инфраструктуру Delivery Hero, построенную на современных технологиях и популярных облачных платформах, для создания высокодоступных систем. — Предлагать инновационные решения и новые технологии для поддержки новых кейсов. Требования: — Опыт ML-инжиниринга или MLOps не менее 4 лет, знакомство с инструментами для обслуживания моделей, обучения, экспериментов и инженерии признаков. — Опыт построения инфраструктуры для ML-пайплайнов с использованием современных инструментов и языков программирования, таких как Python. — Значительный опыт (от 3 лет) в проектировании и реализации различных моделей машинного обучения (регрессия, классификация, ансамбли деревьев) для решения бизнес-задач, с хорошей теоретической базой в статистике и теории вероятностей. — Опыт разработки приложений с нуля и создания быстрых прототипов для решения системных задач с использованием open-source инструментов. — Опыт технической оценки технологий и предложений решений на основе анализа плюсов и минусов. — Опыт глубокого анализа существующих систем для их улучшения. — Свободное владение английским языком, четкая устная и письменная коммуникация. — Желательно: опыт работы с ML-инструментами Metaflow, Airflow, MLflow, Argo Workflows, Cloud Notebooks. — Желательно: знание публичных облачных платформ (AWS, Google Cloud или аналогичных). — Желательно: опыт работы с CI/CD пайплайнами и инфраструктурой как код (Terraform или аналог). — Желательно: знание контейнеризации и оркестрации (Docker, Kubernetes, Helm). Условия: — Релокация в Берлин, Германия. — Гибридный формат работы: 2 дня в неделю в офисе на кампусе в Берлине. — 27 дней отпуска с дополнительными днями на 2-й и 3-й год работы. — Поддержка развития и карьерного роста: образовательный бюджет 1000 €, языковые курсы, поддержка родителей, доступ к Udemy Business. — Медицинские обследования, медитация, спортзал. — Программы поддержки сотрудников: план покупки акций, банк саббатикала, скидки на транспорт, страхование жизни и несчастных случаев, корпоративная пенсия. — Питание: цифровые и обычные ваучеры на еду. Компания Delivery Hero — одна из крупнейших технологических платформ Европы, работающая в 65 странах, штаб-квартира в Берлине, котируется на Франкфуртской бирже с 2017 года.

Location: Brazil; Poland; Argentina; Colombia Workplace: Remote Solvd Inc. is a rapidly growing AI-native consulting and technology services firm delivering enterprise transformation across cloud, data, software engineering, and artificial intelligence. We work with industry-leading organizations to design, build, and operationalize technology solutions that drive measurable business outcomes. Following the acquisition of Tooploox, a premier AI and product development company, Solvd now offers true end-to-end delivery—from strategic advisory and solution design to custom AI development and enterprise-scale implementation. Our capability centers combine deep technical expertise, proven delivery methodologies, and sector-specific knowledge to address complex business challenges quickly and effectively. We are looking for a Forward Deployed Engineer to work directly with clients on high ambiguity GenAI, agentic AI and AI-enabled workflow transformation initiatives. This role is designed for someone who can operate at the intersection of customer discovery, hands-on engineering, solution architecture, early delivery and production adoption. You will work closely with client stakeholders and users to uncover the real operational problem, build thin end-to-end solutions with real systems and data, validate feasibility, shape the delivery path and stay involved through MVP or first production release to assure alignment with expected outcome. ROLE MISSION Your mission is to turn ambiguous customer problems into validated, production-grade AI solutions. You will help Solvd move from “we can build this” to “we have proven this works in the customer’s real operating environment.” You will bridge presales, discovery, prototyping and delivery, while converting field learnings into solution blueprints and service accelerators. YOUR RESPONSIBILITIES - Lead discovery and solution shaping for GenAI / Agentic AI initiatives, working directly with client stakeholders, users and technical teams to understand workflows, systems, data and business constraints. - Drive early-stage opportunities partnering with account executives, product analysts and technology SMEs to prototype, define scope, implementation roadmap and effort estimates. - Translate ambiguous business problems into validated AI solution designs covering model strategy, data access, orchestration, tool use, integration patterns and production constraints. - Build proof-of-concepts fast, prioritizing speed and impact over perfect design, technical spikes to validate architectural assumptions using real or representative systems and data. - Evaluate and select appropriate AI frameworks, platforms and tools, including LLMs, vector databases, orchestration frameworks, cloud AI services and agentic tooling. - Present, defend and explain complex AI, architecture and delivery concepts in simple, practical terms for both business and technical stakeholders. - Stay involved through MVP or first production release to preserve context, support delivery teams and validate that the solution works in the customer’s operating environment. - Define evaluation criteria for AI quality and business impact, including accuracy, groundedness, tool-call correctness, latency, cost, adoption and workflow effectiveness. - Ensure proposed solutions adhere to security, compliance, Responsible AI, observability and production-readiness principles. - Convert field learnings into reusable Solvd assets, including solution blueprints, evaluation frameworks and service accelerators. OUR REQUIREMENTS - A seasoned hands-on technical leader who has evolved from full-stack or server-side software engineering into the GenAI and agentic AI domain. - Experienced in designing, prototyping and integrating systems that combine LLMs, agentic frameworks, enterprise data and business workflows. - Proficient at combining established engineering patterns with emerging AI capabilities to deliver reliable, production-ready solutions. - Skilled in guiding teams and clients through ambiguity - able to prototype rapidly, validate assumptions and converge toward delivery-ready designs. - Comfortable engaging with executives, product teams, operational users and engineers alike, translating complex AI concepts into clear, actionable solution narratives. - Pragmatic, curious, collaborative and obsessed with customer problem — equally focused on technical soundness, business value, user adoption and delivery readiness. - 8+ years of experience in IT, including strong experience in software design and engineering. - 2+ years of hands-on experience architecting or building GenAI or agentic AI systems using modern LLM ecosystems, such as OpenAI, Anthropic, Gemini, Azure AI or AWS Bedrock. - Experience supporting presales solutioning, discovery, prototyping or early delivery for AI engagements. - Strong understanding of LLM orchestration, retrieval-augmented generation, vector databases, prompt engineering and tool-calling patterns. - Understanding of cost modeling for AI workloads, including token usage, inference scaling and hosting models. - Proficiency with at least one major cloud platform, such as AWS, Azure or GCP, and its AI/ML service offerings. - Demonstrated experience leading technical discussions with both engineering and non-technical stakeholders in presales, discovery or early delivery phases. - Strong command of software engineering fundamentals, API design, integration patterns and production-readiness practices. - Knowledge of modern software delivery practices, including CI/CD, containerization, observability and DevSecOps. - Excellent communication, presentation and documentation skills, with the ability to articulate complex solutions clearly and persuasively. OPTIONAL - Prior experience as a Forward Deployed Engineer, Field Engineer, staff engineer, solution architect or technical product lead. - Prior experience with traditional AI/ML systems, including model training, MLOps, data pipelines or feature stores. - Familiarity with frameworks for agentic or multi-component AI pipelines. - Exposure to Responsible AI, data privacy and governance frameworks. - Hands-on experience with Databricks or Snowflake. - Experience creating reusable solution blueprints, evaluation frameworks or technical accelerators. - Industry-recognized cloud and AI certifications from Anthropic, AWS, Azure or Google. When you join Solvd, you'll… - Shape real-world AI-driven projects across key industries, working with clients from startup innovation to enterprise transformation. - Be part of a global team with equal opportunities for collaboration across continents and cultures. - Thrive in an inclusive environment that prioritizes continuous learning, innovation, and ethical AI standards. Ready to make an impact? If you're excited to build things that matter, champion responsible AI, and grow with some of the industry’s sharpest minds. Apply today and let’s innovate together. Solvd is an equal opportunity employer.

Location: Ukraine; Poland; Spain; Portugal; Germany; Romania At SPD Technology, we bring together a team of like-minded people who are driven by the desire to bring value through their work, united in their commitment to high performance and delivering custom, cutting-edge tech solutions that drive clients’ growth. We empower our people with a culture of excellence and enable them with the opportunity to uphold their accountability to contribute on each level. We value humanity and collaboration, encourage professional and personal growth, and foster a supportive and flexible work environment where everyone’s contribution is welcomed. And now we are looking for a Engineering Manager, Applied AI & Machine Learning Engineering to join us as part of our team. About Role PitchBook — a platform for investment professionals. Our software provides access to data and the analytical tools to get answers fast and discover promising opportunities. Uncovers actionable insights and trends hidden within the financial data of more than three million companies. Users all over the world include large corporations, start-ups, venture capital and private equity firms, investment banks, and many others. As an Engineering Manager, AI/ML, you will support a cross-functional team of MLE and MLOps engineers working across several AI/ML initiatives. Your primary focus will be resource planning, execution, and people management. You will work closely with project leads, technical leads, functional managers, and product partners to ensure projects are appropriately staffed, plans are realistic, risks are visible, and teams can deliver effectively. You will manage capacity and resource allocation across multiple projects, help coordinate dependencies and priorities, and provide stakeholders with a clear view of delivery progress. Project and technical leads will continue to own product direction and technical decisions. You will also support the career growth and performance of team members. This includes gathering feedback, coaching employees, creating development plans, and contributing to performance, promotion, and compensation reviews. You should have enough AI/ML experience to understand the work, communicate effectively with engineers, and identify technical delivery risks. However, this is not expected to be a hands-on engineering or architecture role. Our Tech Stack & Focus Areas Generative AI & LLMs: Develop and maintain LLM-based solutions for generative features. Classic ML Models: Develop and maintain predictive and classification models for financial data Vector Databases: Implementing efficient storage and retrieval systems for high-dimensional data embeddings. Embeddings: Developing hybrid models to extract insights from diverse data sources. High-Volume Data Management: Engineering systems capable of handling massive, real-time data streams. Work Environment Flexible schedule with participation in team ceremonies and cross-functional collaboration. Time zone: Europe/Kyiv. As a qualified expert, You will: Manage resource allocation and capacity planning across multiple AI/ML projects and operational commitments Partner with project and technical leads to create realistic delivery plans, milestones, and staffing models Maintain a clear view of project progress, dependencies, risks, and resource constraints Help resolve delivery blockers, priority conflicts, and cross-team dependencies Facilitate quarterly and ongoing planning across MLEs and MLOps teams Provide clear delivery and staffing updates to functional managers and other stakeholders Manage and coach engineers through regular one-to-one meetings and career discussions Gather and synthesize feedback from project leads, technical leads, and cross-functional partners Create development plans and support performance, promotion, and compensation review processes Identify team capability gaps and contribute to hiring, onboarding, retention, and workforce planning Foster an inclusive, collaborative, and high-performing team environment Apply Agile, Lean, and Fast-Flow practices to improve team effectiveness and delivery predictability We’re looking for you if you have At least three years of experience in software engineering, AI/ML, or a related field At least three years of experience in people management, engineering management, or technical program leadership Working knowledge of AI/ML modeling, systems and the development lifecycle Experience managing multiple technical projects with shared resources and cross-team dependencies Strong skills in capacity planning, resource allocation, project execution, risk management, and stakeholder communication Experience supporting the performance and career development of technical employees Ability to understand technical discussions and work effectively with MLE and MLOps teams Experience working with distributed or geographically dispersed teams English — Upper Intermediate or higher; Ukrainian — fluent Bonus points Experience managing or supporting MLE and MLOps teams Experience working in a matrix organization with separate people, project, product, and technical leadership roles Experience with planning or allocating engineers across multiple projects Familiarity with production ML, generative AI, semantic search, summarization, prediction, or similar capabilities Familiarity with technologies such as Kubernetes, Docker, Kafka, Airflow, Snowflake, and cloud platforms Experience conducting performance reviews and building employee development plans Experience working with staffing partners, contractors, or blended internal and external teams Experience in fintech, SaaS, market data, or another data-intensive industry What’s in it for You Reveal great tech solutions Join the team of experts who create custom, cutting-edge tech solutions for world-renowned businesses, fueling clients growth. Unleash your potential, tackle new challenges, and be part of a team that values your skills and contributions. Focus on long-term impact and building tailored, long-lasting partnerships with our clients. Experience an agile and flexible working environment Work fully remote around the globe, or in a hybrid work model from our office hubs which are equipped with everything needed for comfortable work. Enjoy a flexible working schedule and create work and rest balance for yourself. Empower yourself with a stable workload and a stable income, focusing on lasting cooperation and high-performance culture. Embrace the opportunity for personal and professional growth Take advantage of performance review and career path. Elevate your skills with mentorship program, personal development plan, and individual learnings through the corporate library, public speaking support, and more. Feel cared about Be among like-minded people and feel supported with collaboration, open communication and mutual respect. Join company-wide tech and cultural events, and contribute to meaningful CSR initiatives that resonate with your values. Interview Steps: Interview with the recruiter EM & HRBP Interview Technical Interview Final сlient Interview.

Location: Limassol, Cyprus Workplace: on-site Mayflower is a technology company that alters the entertainment industry to a new level of perception and engagement. We are building the future of live entertainment. We believe that by providing people with the best and highest-quality live streaming experience in entertainment, we can tackle the world’s “problems” better, together! We are seeking an experienced Python Developer (ML) to join our dynamic team ML team. Our technology stack: Languages: Python; Monitoring: Prometheus, VictoriaMetrics, Grafana, Grafana Loki, Sentry; DBs: MariaDB, Clickhouse, Redis; Streaming: Apache Kafka; Workflow management: Apache Airflow, Argo Workflow; Infrastructure: k8s, gitlab. Job Responsibilities Design and build production-grade Python services for running and maintaining ML applications for different domains: RecSys, CV, Classic ML, NLP. Develop and maintain data pipelines for training, validation, batch inference workflows Build internal tools and reusable modules for the ML team Implement monitoring and observability for ML services using metrics, logs, and dashboards. Contribute to continuous integration and delivery pipelines (testing, packaging, and automation). Develop visualization and debugging tools for model outputs — including active learning and content analysis interfaces. Ensure that all components are reliable, testable, and easy to maintain. You’ll thrive here if you have 3+ years of experience as a Python Developer, Backend Engineer, or Data Engineer. Strong Python skills (asyncio, FastAPI, pydantic, typing, modular architecture). Proven experience designing and deploying production systems with Docker, Kubernetes and CI/CD. Familiarity with ML workflows (you don’t need to train models yourself, but you understand how they’re served, retrained, and monitored). Experience building data pipelines and integrating with various data sources. Knowledge of metrics, logging, and observability best practices (Grafana, Prometheus, Loki, etc.). Strong understanding of testing, clean code, and maintainable software design. Qualification that can be a plus: Experience working with machine learning teams or MLOps pipelines. Experience with distributed systems or high-load backend services. Conditions We know that great talent deserves great conditions, so here's what you can expect when joining us: EU-based employment contract and a 3-year Cyprus work visa with full support for your relocation and visa processes, including assistance for your family. Full relocation package: flights to Limassol for you and your family, a company-covered apartment for the first month, and full relocation support to make your move smooth and hassle-free. Transparent performance reviews twice a year, with bonus opportunities and salary adjustments. Private medical insurance for you and your family, a corporate mobile plan (unlimited in Cyprus with roaming included), and interest-free support for car purchases. Provident fund (Cypus): a long-term savings plan co-funded by you and the Company together (available after probation) that grows throughout your time with us in Cyprus. Mindfulness & well-being support, including psychological assistance with 50% coverage. 50% coverage of school and kindergarten fees for your children. Fully covered sports benefits, and also access to in-house electric scooters and bike rentals, and cycling purchase compensation. Investment in your growth: paid language courses and access to suited-for-you development programs, including conferences, training programs, and coaching to support your professional journey. A culture of recognition: a peer reward program to celebrate your contributions. A fully equipped office in Limassol’s city center, with everything you need for deep work and collaboration. Free catering in the office and an in-house coffee bar with high-quality drinks and a health bar stocked with nutritious snacks. A strong engineering culture: international teams, corporate events, team buildings, and hackathons—because great work happens in great communities. Recruitment process HR Interview (40 min); Technical Interview + Live Coding (1.5 hour); Final Interview (1 hour).

Location: Remote or Mississauga Workplace: remote At PointClickCare our mission is simple: to help providers deliver exceptional care. And that starts with our people. As a leading health tech company that’s founder-led and privately held, we empower our employees to push boundaries, innovate, and shape the future of healthcare.   With the largest long-term and post-acute care dataset and a Marketplace of 400+ integrated partners, our platform serves over 30,000 provider organizations, making a real difference in millions of lives. We also reinvest a significant percentage of our revenue back into research and development, ensuring our employees have the resources to innovate and make a lasting impact. Recognized by Forbes as a top private cloud company and honored as one of Canada’s Most Admired Corporate Cultures, we offer flexibility, growth opportunities, and meaningful work.    At PointClickCare, we empower our people to be the architects of a smarter healthcare future; one that is human-first and accelerated by AI to create meaningful and lasting change. Employees harness AI as a catalyst for creativity, productivity, and thoughtful decision-making. By integrating AI tools into our daily workflows, collaboration is enhanced, outcomes are improved, and every team member has the proficiency to maximize their impact. It all starts with our hiring practices where we uncover AI expertise that complements our mission, and we continue to invest in training and development to nurture innovation throughout the employee journey.   Join us in redefining healthcare — so it doesn’t just survive, it thrives. To learn more about PointClickCare, check out Life at PointClickCare and connect with us on Glassdoor and LinkedIn.     **Travel to Office expectations** For Remote Roles: If this role is remote, there will be in-office events that will require travel to and from the Mississauga and/or Salt Lake City office. These will include, but not limited to, onboarding, team events, semi-annual and annual team meetings.   For Hybrid Roles: If this role is Hybrid, there will be an expectation to reside within commutable distance to the office/location specified in the job listing. This will include, but not limited to, weekly/bi-weekly/monthly events in the office with your specific team. This is a requirement for this role. Team Summary This team will serve as the product owner for the machine learning platform capabilities within PointClickCare, working closely with other engineering teams across the organization to identify, build and support traditional machine learning (ML) and hybrid ML/LLMsolutions. This centralized team with deep specialization will closely integrate with key horizontal partners to ensure delivery of safe, scalable, and high-impact AI products. Job Summary The Principal AI Machine Learning Platform Engineer will set the technical vision and strategy for the machine learning platform that powers ML and generative AI development across PointClickCare, partnering with Product and Engineering leadership to align that direction with product and business goals. As the technical authority for the ML platform, the Principal Engineer will define the reference architectures, standards, and roadmap for the pipelines, tooling, and infrastructure used for model training, deployment, serving, and monitoring, and will provide technical leadership and mentorship to raise the engineering bar for ML systems company-wide. Key Responsibilities Partner with product and engineering leadership to translate business and product objectives into a multi-quarter technical strategy and roadmap for the ML platform. Define the reference architectures and standards for scalable data and ML pipelines spanning model training, evaluation, deployment, and serving that engineering teams across the organization build upon. Set the direction and best practices for MLOps across the company — including CI/CD for models, model registry, feature stores, and experiment tracking — and drive build-vs-buy decisions for core platform components. Establish the practices and architecture for reliability, observability, and performance of ML systems in production, including monitoring, alerting, and automated remediation. Establish the security architecture for the ML platform, including authentication, role-based access control, audit logging, and compliance monitoring, and ensure adoption across teams. Define secure, cost-efficient integration and infrastructure patterns for connecting the platform with existing systems, APIs, and data sources at scale. Provide technical leadership and mentorship across engineering teams, guiding senior engineers and influencing the org-wide technical roadmap for ML infrastructure. Qualifications & Skills Expert level in Python and Java with strong software engineering fundamentals. Deep experience designing and building ML platforms and ML Ops workflows at scale, familiarity with tools such as MLFlow, Kubeflow, Ray, and model-serving frameworks or equivalents. Extensive experience with cloud platforms (AWS, Azure, and/or GCP), containerization, and orchestration (Docker, Kubernetes). Demonstrated track record of setting technical direction and driving org-wide technical initiatives across multiple teams. Preferred Bachelor’s degree or higher in Computer Science, Machine Learning, or a related field. Sufficient familiarity with Azure Machine Learning components, Databricks processing and serverless environments, and ML Frameworks to support strategic decision making Demonstrable history of leading and sustaining build out of critical cross-team systems Experience implementing security at scale including role-based access control, multi-factor authentication, network security best practices, and compliance monitoring. Experience optimizing large model training and inference (including LLM serving) for performance and cost. #LI-AJ1 #LI-remote PointClickCare Benefits & Perks:   Benefits starting from Day 1! Retirement Plan Matching Flexible Paid Time Off Wellness Support Programs and Resources Parental & Caregiver Leaves Fertility & Adoption Support Continuous Development Support Program Employee Assistance Program Allyship and Inclusion Communities Employee Recognition … and more!   It is the policy of PointClickCare to ensure equal employment opportunity without discrimination or harassment on the basis of race, religion, national origin, status, age, sex, sexual orientation, gender identity or expression, marital or domestic/civil partnership status, disability, veteran status, genetic information, or any other basis protected by law. PointClickCare welcomes and encourages applications from people with disabilities. Accommodations are available upon request for candidates taking part in all aspects of the selection process. Please contact recruitment@pointclickcare.com should you require any accommodations. As part of our commitment to a streamlined and equitable hiring experience, PointClickCare uses AI tools to assist with candidate screening and assessment.   When you apply for a position, your information is processed and stored with Lever, in accordance with Lever’s Privacy Policy. We use this information to evaluate your candidacy for the posted position. We also store this information, and may use it in relation to future positions to which you apply, or which we believe may be relevant to you given your background. When we have no ongoing legitimate business need to process your information, we will either delete or anonymize it.  If you have any questions about how PointClickCare uses or processes your information, or if you would like to ask to access, correct, or delete your information, please contact PointClickCare’s human resources team: recruitment@pointclickcare.com    PointClickCare is committed to Information Security. By applying to this position, if hired, you commit to following our information security policies and procedures and making every effort to secure confidential and/or sensitive information. At PointClickCare, base salary is one of the many components that make up our total rewards package. The CAD base salary range for this position is $179,000-$224 000 (not overtime eligible) + bonus + benefits. Compensation is assessed individually and aligned to experience, skills, and market context. The posted range reflects typical expectations for this role.

Location: Remote, USA Workplace: remote At PointClickCare our mission is simple: to help providers deliver exceptional care. And that starts with our people. As a leading health tech company that’s founder-led and privately held, we empower our employees to push boundaries, innovate, and shape the future of healthcare.   With the largest long-term and post-acute care dataset and a Marketplace of 400+ integrated partners, our platform serves over 30,000 provider organizations, making a real difference in millions of lives. We also reinvest a significant percentage of our revenue back into research and development, ensuring our employees have the resources to innovate and make a lasting impact. Recognized by Forbes as a top private cloud company and honored as one of Canada’s Most Admired Corporate Cultures, we offer flexibility, growth opportunities, and meaningful work.    At PointClickCare, we empower our people to be the architects of a smarter healthcare future; one that is human-first and accelerated by AI to create meaningful and lasting change. Employees harness AI as a catalyst for creativity, productivity, and thoughtful decision-making. By integrating AI tools into our daily workflows, collaboration is enhanced, outcomes are improved, and every team member has the proficiency to maximize their impact. It all starts with our hiring practices where we uncover AI expertise that complements our mission, and we continue to invest in training and development to nurture innovation throughout the employee journey.   Join us in redefining healthcare — so it doesn’t just survive, it thrives. To learn more about PointClickCare, check out Life at PointClickCare and connect with us on Glassdoor and LinkedIn.     **Travel to Office expectations** For Remote Roles: If this role is remote, there will be in-office events that will require travel to and from the Mississauga and/or Salt Lake City office. These will include, but not limited to, onboarding, team events, semi-annual and annual team meetings.   For Hybrid Roles: If this role is Hybrid, there will be an expectation to reside within commutable distance to the office/location specified in the job listing. This will include, but not limited to, weekly/bi-weekly/monthly events in the office with your specific team. This is a requirement for this role. Team Summary This team will serve as the product owner for the machine learning platform capabilities within PointClickCare, working closely with other engineering teams across the organization to identify, build and support traditional machine learning (ML) and hybrid ML/LLMsolutions. This centralized team with deep specialization will closely integrate with key horizontal partners to ensure delivery of safe, scalable, and high-impact AI products. Job Summary The Principal AI Machine Learning Platform Engineer will set the technical vision and strategy for the machine learning platform that powers ML and generative AI development across PointClickCare, partnering with Product and Engineering leadership to align that direction with product and business goals. As the technical authority for the ML platform, the Principal Engineer will define the reference architectures, standards, and roadmap for the pipelines, tooling, and infrastructure used for model training, deployment, serving, and monitoring, and will provide technical leadership and mentorship to raise the engineering bar for ML systems company-wide. Key Responsibilities Partner with product and engineering leadership to translate business and product objectives into a multi-quarter technical strategy and roadmap for the ML platform. Define the reference architectures and standards for scalable data and ML pipelines spanning model training, evaluation, deployment, and serving that engineering teams across the organization build upon. Set the direction and best practices for MLOps across the company — including CI/CD for models, model registry, feature stores, and experiment tracking — and drive build-vs-buy decisions for core platform components. Establish the practices and architecture for reliability, observability, and performance of ML systems in production, including monitoring, alerting, and automated remediation. Establish the security architecture for the ML platform, including authentication, role-based access control, audit logging, and compliance monitoring, and ensure adoption across teams. Define secure, cost-efficient integration and infrastructure patterns for connecting the platform with existing systems, APIs, and data sources at scale. Provide technical leadership and mentorship across engineering teams, guiding senior engineers and influencing the org-wide technical roadmap for ML infrastructure. Qualifications & Skills Expert level in Python and Java with strong software engineering fundamentals. Deep experience designing and building ML platforms and ML Ops workflows at scale, familiarity with tools such as MLFlow, Kubeflow, Ray, and model-serving frameworks or equivalents. Extensive experience with cloud platforms (AWS, Azure, and/or GCP), containerization, and orchestration (Docker, Kubernetes). Demonstrated track record of setting technical direction and driving org-wide technical initiatives across multiple teams. Preferred Bachelor’s degree or higher in Computer Science, Machine Learning, or a related field. Sufficient familiarity with Azure Machine Learning components, Databricks processing and serverless environments, and ML Frameworks to support strategic decision making Demonstrable history of leading and sustaining build out of critical cross-team systems Experience implementing security at scale including role-based access control, multi-factor authentication, network security best practices, and compliance monitoring. Experience optimizing large model training and inference (including LLM serving) for performance and cost. #LI-AJ1 #LI-remote PointClickCare Benefits & Perks:   Benefits starting from Day 1! Retirement Plan Matching Flexible Paid Time Off Wellness Support Programs and Resources Parental & Caregiver Leaves Fertility & Adoption Support Continuous Development Support Program Employee Assistance Program Allyship and Inclusion Communities Employee Recognition … and more!   It is the policy of PointClickCare to ensure equal employment opportunity without discrimination or harassment on the basis of race, religion, national origin, status, age, sex, sexual orientation, gender identity or expression, marital or domestic/civil partnership status, disability, veteran status, genetic information, or any other basis protected by law. PointClickCare welcomes and encourages applications from people with disabilities. Accommodations are available upon request for candidates taking part in all aspects of the selection process. Please contact recruitment@pointclickcare.com should you require any accommodations. As part of our commitment to a streamlined and equitable hiring experience, PointClickCare uses AI tools to assist with candidate screening and assessment.   When you apply for a position, your information is processed and stored with Lever, in accordance with Lever’s Privacy Policy. We use this information to evaluate your candidacy for the posted position. We also store this information, and may use it in relation to future positions to which you apply, or which we believe may be relevant to you given your background. When we have no ongoing legitimate business need to process your information, we will either delete or anonymize it.  If you have any questions about how PointClickCare uses or processes your information, or if you would like to ask to access, correct, or delete your information, please contact PointClickCare’s human resources team: recruitment@pointclickcare.com    PointClickCare is committed to Information Security. By applying to this position, if hired, you commit to following our information security policies and procedures and making every effort to secure confidential and/or sensitive information. At PointClickCare, base salary is one of the many components that make up our total rewards package. The US base salary range for this position is $158,000-$198, 000 (not overtime eligible) + bonus + benefits. Compensation is assessed individually and aligned to experience, skills, and market context. The posted range reflects typical expectations for this role.

About our Machine Learning Business Banking team: Our mission in Business Banking is to simplify banking for small businesses; making business banking fairer, simpler and more transparent. Monzo Business Banking is fast-growing, is the UK’s most recommended business account for overall service quality. We recently hit 1 million customers and are used by 1 in 6 UK SMEs. We want to help businesses spend less time on financial admin and more time focused on running and growing their business. We’re building intelligent, data-driven and AI/ML-enabled product experiences that help small businesses feel more in control, make better decisions, and get more value from Monzo. ML is at the core of how we build and scale our products, enabling Monzo to make better decisions, faster, and helping us serve businesses more effectively. As a Lead Machine Learning Scientist in Business Banking, you’ll help build and ship models that power the next generation of Business Banking experiences. You’ll work closely with Product, Engineering, Design, Research, Data Science and Analytics Engineering to bring ML-enabled product capability into the heart of how we build for businesses. Whether it’s helping businesses understand their cash flow, reducing manual admin, surfacing the right insight at the right moment, or helping teams build intelligent product experiences responsibly, our work makes business banking smarter, simpler and more useful for SMEs. What you'll be working on As a Lead Machine Learning Scientist, you’ll be a technical leader and hands-on individual contributor, spearheading our ML and GenAI capabilities and shipping key models that powers magical Business Banking experiences. You’ll: — Be one of the first ML Scientists in Business Banking, bringing leadership through ambiguity by adding structure and direction to our ML capabilities while staying agile and building momentum. — Develop and deploy advanced ML models on our cloud-native data platform to serve hundreds of thousands of business customers. — Partner with Product, data science and other stakeholders to identify the highest-impact opportunities, size impact, and define success metrics. — Decide when to use ML, GenAI, or simpler approaches and be clear on trade-offs. — Design robust evaluation and monitoring so we can ship responsibly and measure impact in production. — Lead the design and implementation of batch and near-real-time models (e.g. LLM-powered experiences, predictive models, time-series forecasting). — Collaborate closely with MLOps and Backend Engineering to operationalise models end-to-end and raise the bar on ML lifecycle rigor. — Set technical standards, mentor others, and support experimentation and iteration. You should apply if: — What we’re doing here at Monzo excites you! — You have a multiple year track record of excellence leading the development and deployment of advanced Machine Learning models to tackle real business problems, preferably in a fast-moving tech company. — You have experience developing and shipping state-of-the-art ML models to production and delivering business impact. — You're impact-driven and excited to own the end-to-end journey that starts with a business problem and ends with your solution having a measurable impact in production. — You have a self-starter mindset; you proactively identify issues and opportunities and tackle them without being told to do so. — You speak Python fluently and have extensive, hands-on experience with scikit-learn. — You are comfortable using SQL, and keen to learn Go, which is used in many of our backend microservices. — You’re comfortable working in a team that deals with ambiguity and have experience helping your team and stakeholders resolve that ambiguity. — You want to be involved in building a product that small businesses use to run and manage their financial lives every day. — You have a product mindset: you care about customer outcomes and you want to make data-informed decisions. — You’re excited about fast-moving developments in Machine Learning and AI and can communicate those ideas to colleagues who are not familiar with the domain. — You’re adaptable, curious and enjoy learning new technologies and ideas. — You’re excited by the opportunity to help Business Banking evolve into AI and ML-enabled product capability. — You can work closely with Product, Engineering, Design, Research and Data colleagues to shape product strategy, clarify trade-offs and build things that customers actually use. — You’re thoughtful about responsible ML/AI; especially in financial products where trust, transparency and customer control matter. Nice to haves: — Experience working on personalisation, ranking, recommendation, forecasting, classification or decisioning problems for customer-facing applications. — Experience working on ML systems for fintech, banking, accounting, payments, lending, invoicing, cash flow, tax, risk or business software. — Experience with GenAI, LLMs, agentic workflows, retrieval, evaluation frameworks, or human-in-the-loop product experiences. — Commercial experience writing critical production code and working with microservices. — Experience designing experiments, A/B tests or other approaches to measure product and customer impact. — Experience working in regulated environments or with products where explainability, auditability and customer trust are especially important. The interview process: Our interview process involves 3 main stages. We promise not to ask you any brain teasers or trick questions! — 30 minute recruiter call — 45 minute call with hiring manager — Full Loop: ML Modelling Interview, Product & ML interview, Behavioural Interview, Leadership interview Our average process takes around 3-4 weeks but we will always work around your availability. You will have the chance to speak to our recruitment team at various points during your process but if you do have any specific questions ahead of this please contact us on tech-hiring@monzo.com. Please also use that email to let us know if there's anything we can do to make your application process easier for you, because of disability, neurodiversity or any other personal reason. What’s in it for you: — We can help you relocate to the UK — We can sponsor visas — This role can be based in our London office, but we're open to distributed working within the UK (with ad hoc meetings in London). — We offer flexible working hours and trust you to work enough hours to do your job well, at times that suit you and your team. — Learning budget of £1,000 a year for training courses and conferences — And much more, see our full list of benefits here If you prefer to work part-time, we'll make this happen whenever we can - whether this is to help you meet other commitments or strike a great work-life balance.

Location: United Kingdom; Dublin; Tokyo; United States; New York; Los Angeles; Poland; London; Boston; San Francisco; Washington, D.C. Workplace: Remote ABOUT ELEVENLABS ElevenLabs is an AI research and product company transforming how we interact with technology. We launched in January 2023 with the first human-like AI voice model. Today, we serve millions of users and thousands of businesses - from fast-growing startups to large enterprises like Deutsche Telekom and Meta. Our investors are some of the world's most prominent, including Andreessen Horowitz, ICONIQ Growth and Sequoia. We've raised $781M in funding and our last valuation was $22B - multiples of 11, always. We have expanded from voice into three main platforms: - ElevenAgents enables businesses to deliver seamless and intelligent customer experiences, with the integrations, testing, monitoring, and reliability necessary to deploy voice and chat agents at scale. - ElevenCreative empowers creators and marketers to generate and edit speech, music, image, and video across 70+ languages. - ElevenAPI gives developers access to our leading AI audio foundational models. Everything we do is the result of the creativity and commitment of our team - builders doing the best work of their lives. We are researchers, engineers, and operators. IOI medalists and ex-founders. If you want to work hard and create lasting positive impact, we want to hear from you. HOW WE WORK - High-velocity: Rapid experimentation, lean autonomous teams, and minimal bureaucracy. - Impact not job titles: We don’t have job titles. Instead, it’s about the impact you have. No task is above or beneath you. - AI first: We use AI to move faster with higher-quality results. We do this across the whole company—from engineering to growth to operations. - Excellence everywhere: Everything we do should match the quality of our AI models. - Global team: We prioritize your talent, not your location. WHAT WE OFFER - Innovative culture: You’ll be part of a generational opportunity to define the trajectory of AI, surrounded by a team pushing the boundaries of what’s possible. - Growth paths: Joining ElevenLabs means joining a dynamic team with countless opportunities to drive impact - beyond your immediate role and responsibilities. - Learning & development: ElevenLabs proactively supports professional development through an annual discretionary stipend. - Social travel: We also provide an annual discretionary stipend to meet up with colleagues each year, however you choose. - Annual company offsite: Each year, we bring the entire team together in a new location - past offsites have included Croatia and Italy. - Co-working: If you’re not located near one of our main hubs, we offer a monthly co-working stipend. ABOUT THE ROLE We're looking for an experienced AI Safety Engineer to drive the deployment and operationalization of automated moderation and guardrail systems that protect our platform and users across a multimodal space, with a particular focus on detecting and preventing abuse of ElevenLabs' free tiers. You'll work alongside a team of ML and full stack engineers to build production-grade safety infrastructure from the ground up. This is a product ownership role where you'll be responsible for end-to-end technical execution of our safety systems, from architecture to deployment and monitoring. You'll bridge the gap between ML research and production-grade systems, ensuring our safety infrastructure is robust, observable, and scalable. WHAT YOU'LL DO: - Design and build scalable backend infrastructure for abuse detection, deploying AI/ML models into production systems - Architect robust APIs, data pipelines, and service architectures supporting real-time and batch moderation workflows - Implement comprehensive monitoring, alerting, and observability systems; establish SLIs, SLOs, and performance benchmarks - Partner with ML engineers to translate research models into production-ready systems and integrate them across our product suite - Drive technical decisions and contribute vision to the safety roadmap on how the next generation of platform guardrails should be built for scale and precision. REQUIREMENTS - Direct experience tackling free-tier abuse, fraud detection, or fake-account / bot farming at scale - 6+ years of backend software engineering experience building production systems at scale - Strong production backend experience: distributed systems, APIs, data pipelines, and Python expertise (asynchronous Python, backend frameworks) - Infrastructure & DevOps proficiency: cloud platforms (AWS/GCP), containerization (Docker/K8s), CI/CD pipelines - Observability mindset with experience in monitoring tools (Prometheus, Grafana) and building observable systems - Track record of taking products or systems from 0→1 with measurable impact, including deploying or working alongside ML/AI systems in production Bonus: - Trust & Safety, Content Moderation, or Integrity engineering experience - MLOps experience: deployment, monitoring, and versioning of ML models - Experience with SQL, data analysis tools, real-time streaming systems (Kafka, Redis), or event-driven architectures - Familiarity with React or modern frontend frameworks LOCATION This role is remote and can be executed globally. If you prefer, you can work from our offices in London, New York, San Francisco, and Warsaw. #LI-remote We are an equal opportunity employer and do not discriminate on the basis of race, religion, national origin, gender, sexual orientation, age, veteran status, disability or other legally protected statuses.

Location: Dubai, United Arab Emirates Job Description We are seeking experienced AI Engineers / Agentic AI Developers to design, develop, and deploy enterprise-grade AI solutions with a focus on Agentic AI , Large Language Models (LLMs) , and Retrieval-Augmented Generation (RAG) . The successful candidate will be responsible for building intelligent AI agents and workflows capable of reasoning, planning, task execution, and orchestration while ensuring all solutions are deployed in an on-premises environment using local AI models and tools. Key Responsibilities Gather, analyze, and translate business requirements into scalable AI and Agentic AI solutions. Design, develop, test, deploy, and maintain AI-powered applications and autonomous AI agents. Build Agentic AI workflows capable of reasoning, planning, task execution, and orchestration. Develop and integrate Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), and multi-agent architectures. Integrate AI solutions with enterprise applications, APIs, databases, cloud services, and on-premises systems. Optimize AI models, prompts, workflows, and application performance. Ensure AI solutions comply with enterprise security, governance, privacy, and Responsible AI standards. Prepare technical documentation, deployment guides, operational manuals, and knowledge transfer materials. Provide technical support, troubleshooting, performance tuning, and continuous improvements. Design and implement AI solutions using on-premises infrastructure, local models, and enterprise-approved AI tools . Requirements Bachelor's degree in Computer Science, Artificial Intelligence, Software Engineering, or a related field. 5+ years of software development experience, including 2+ years working with AI/Generative AI technologies. Strong experience with Python and AI application development. Hands-on experience with Large Language Models (LLMs) and prompt engineering. Experience designing and implementing Retrieval-Augmented Generation (RAG) solutions. Experience building Agentic AI or multi-agent systems. Experience integrating AI applications with REST APIs , databases, and enterprise applications. Familiarity with AI orchestration frameworks such as LangChain, LangGraph, LlamaIndex, CrewAI, AutoGen , or similar. Experience working with vector databases such as Milvus, ChromaDB, FAISS, Pinecone , or equivalent. Knowledge of model deployment using Ollama, vLLM, Hugging Face Transformers, NVIDIA NIM , or similar on-premises inference platforms. Experience deploying and managing AI solutions in on-premises environments . Understanding of AI security, governance, privacy, and Responsible AI principles. Experience with Docker, Kubernetes, Git, and CI/CD pipelines is preferred. Strong analytical, problem-solving, communication, and documentation skills. Preferred Skills Experience with open-source LLMs such as Llama, Mistral, Qwen, Gemma, or DeepSeek . Experience with GPU infrastructure and AI model optimization. Knowledge of MLOps practices and AI monitoring. Experience integrating AI solutions with enterprise platforms such as SAP, ServiceNow, Microsoft 365, or other enterprise systems. Experience working in highly secure or regulated enterprise environments. This JD is aligned with your requirement that all AI use cases must run on-premises using local models and on-premises tools , making it suitable for enterprise or government projects.

Location: Palo Alto Workplace: Hybrid ABOUT MISTRAL Mistral provides full-stack AI solutions: from frontier models to developer tools, applications, and compute. We partner with enterprises tackling the hardest problems across high-stakes industries like finance, manufacturing, defense, healthcare, and the public sector, co-creating customized AI systems that they can run on their terms. We are a dynamic, collaborative team passionate about AI and its potential to transform society. Our diverse workforce thrives in competitive environments and is committed to driving innovation. Our teams are distributed between Europe, North America, Asia and the Middle East. We are creative, low-ego and team-spirited. THE ROLE This role focuses on building and operating the end-to-end execution, training, and data infrastructure that powers Mistral’s agentic models and coding assistants. You will be a core contributor to our agent research stack: designing scalable systems for synthetic data generation, building ultra-fast training and RL execution environments, and maintaining high-throughput execution engines. You will tackle the engineering challenges at every step of the agent lifecycle: from orchestrating 1M+ concurrent and short-lived sandboxes for untrusted code execution to optimizing agent training codebases, distributed trajectory collection pipelines, and dataset processing workflows across massive hybrid and multi-cloud clusters. WHAT YOU WILL DO - Large-Scale Sandboxing Infrastructure: Design, deploy, and operate our high-throughput sandboxing platform, executing LLM-generated untrusted code across over 1 million isolated environments concurrently for model evaluation and interactive RL environments. - Agent Data Generation Pipelines: Architect and scale high-throughput pipelines for synthetic code generation, agent trajectories, rollouts, and self-play data collection to power post-training and RL loops. - Training Codebase & Systems Optimization: Optimize agent training codebases and distributed execution runtimes (PyTorch, Ray, SLURM/Kubernetes) to minimize multi-step rollout overhead, improve GPU utilization, and eliminate scaling bottlenecks. - Low-Latency Orchestration & Warm Pooling: Reduce sandbox cold-start times to sub-second levels using container warm pools, snapshot/restore technology (e.g., CRIU, microVMs), and optimized image delivery layers across hybrid clusters. - Multi-Cluster Queueing & Resource Allocation: Implement Kubernetes-native custom controllers, CRDs, and queuing systems to dynamically route short-lived evaluation, synthetic data, and agent execution tasks across diverse hardware fleets. - Isolation, Security & Security Boundary: Ensure strict multi-tenant network and process isolation for untrusted agent code using container/sandboxing runtimes (e.g., gVisor, Firecracker) and default-deny network postures. - Operational Excellence: Maintain high availability, telemetry, and automated self-healing across millions of transient jobs while participating in on-call rotations for critical agent training and execution pipelines. WHAT WE'RE LOOKING FOR - 4+ years of experience in Systems Engineering, Distributed Systems, Cloud Infrastructure, or MLOps supporting LLM/RL workloads. - Data & Pipeline Engineering: Proven experience building high-throughput data processing and generation pipelines for large-scale datasets (e.g., Ray, Spark, custom distributed queues). - Deep experience with Kubernetes & Container Tech: Strong expertise writing custom K8s operators/controllers, managing Linux cgroups/namespaces, and optimizing Docker image layers and distribution systems. - High-Performance Software Engineering: Advanced proficiency in Python, Go, C++ or Rust, with a track record of profiling and optimizing high-performance ML or backend systems codebases. - Sandboxing & Isolation Technologies: Hands-on experience with lightweight virtualization, container runtimes, or WASM (e.g., Docker, gVisor, Firecracker). - Queueing & Scheduling: Deep familiarity with task queue systems, resource schedulers, and low-latency queuing architectures for high-volume, short-lived workloads. - Comfort with Ambiguity: Passion for working directly alongside AI researchers to rapidly turn frontier agent ideas into scalable, production-grade infrastructure. WHAT WE OFFER We offer a comprehensive benefits package designed to support your well-being, growth, and work-life balance. Benefits vary by country and may include healthcare coverage, parental leave, retirement plans, relocation support, wellness programs, meal and transportation allowances, and other location-specific perks. For the most up-to-date details on benefits available in your location, please refer to our Benefits page https://app.notion.com/p/mistralai/Benefits-at-Mistral-36e6ba59a7fe836b93dd01737fcc27ef?source=copy_link. PRIVACY POLICY Your privacy matters to us. You can learn more about how we handle your personal data in our Applicant Privacy Policy https://legal.mistral.ai/terms/applicant-privacy-policy.

Location: Krakow, Poland; Krosno, Poland; Lublin, Poland; Poland; Poznan, Poland; Warsaw, Poland Workplace: remote At TechBiz Global, we provide recruitment services to top clients from our international portfolio. We are currently looking for a Senior Data Scientist with strong hands-on experience in training AI models, particularly Large Language Models (LLMs) and Small Language Models (SLMs), using GPU infrastructure and real-world datasets . The ideal candidate should be based in Poland and able to clearly demonstrate their technical expertise, explain the tools and frameworks they use, and describe the complete model-training process—from data preparation to deployment and performance optimisation. Key Responsibilities Train, fine-tune, and optimise LLMs and SLMs using GPU infrastructure. Build and manage end-to-end machine learning training pipelines. Prepare, clean, structure, and process large volumes of real-world data. Select appropriate models, frameworks, tools, and training approaches based on project requirements. Apply techniques such as supervised fine-tuning, transfer learning, prompt tuning, and parameter-efficient fine-tuning. Monitor model performance and improve accuracy, speed, scalability, and resource utilisation. Work with structured, unstructured, time-series, telemetry, log, and streaming data. Clearly document and explain the tools, methods, and technical decisions used throughout the model-training process. Collaborate with engineering, data, and business teams to move models from experimentation into production. Troubleshoot issues related to model quality, training stability, GPU performance, and data pipelines. Proven professional experience as a Data Scientist, Machine Learning Engineer, AI Engineer, or similar role. Strong hands-on experience training or fine-tuning LLMs and/or SLMs. Practical experience using GPUs for AI model training. Strong Python programming skills. Experience with machine learning and deep-learning frameworks such as: PyTorch TensorFlow Hugging Face Transformers Experience with GPU-related technologies and environments, such as CUDA, distributed training, cloud GPU platforms, or GPU clusters. Strong understanding of model-training workflows, including data preparation, tokenisation, model selection, training, evaluation, and optimisation. Ability to clearly explain previous AI projects, tools used, technical challenges, and achieved results. Experience working with large and complex datasets. Good English communication skills. Based in Poland. Relevant Industry Experience Experience in one or more of the following industries or data environments would be highly valuable: E-commerce Finance or banking Insurance Healthcare or medical data Telemetry and IoT data Application or system logs Real-time and streaming data High-volume enterprise data environments Nice to Have Experience with distributed model training. Experience with LoRA, QLoRA, PEFT, quantisation, or model compression. Experience with MLOps tools and model deployment. Knowledge of Docker, Kubernetes, MLflow, or similar technologies. Experience using AWS, Azure, or Google Cloud for AI workloads. Experience deploying AI models into production environments. Knowledge of data privacy, security, and governance requirements.

Чего ждём от тебя Без этого не получится: - 5+ лет в ML/DS — опыт выкатывания моделей в прод и понимание практических аспектов - Уверенный Python: ООП, типизация, тесты - Классический ML: бустинги, валидация, понимание, где нейросеть избыточна, а где достаточно градиентного бустинга - Опыт с текстовыми нейросетевыми моделями: BERT-подобные трансформеры, LLM API, промпт-инжиниринг, eval-сеты - Практический опыт MLOps: пайплайны данных, оркестрация (Airflow, Kubeflow или аналоги), трекинг экспериментов - Инфраструктурный бэкграунд: Docker, CI/CD, Kubernetes, логирование и мониторинг — понимание высоконагруженных сервисов - Умение мыслить P&L: перевод метрик моделей (F1, ROC-AUC) в конверсии и деньги, защита технических решений перед бизнесом финансовыми аргументами - Опыт менторства или лидерства и желание этим заниматься

Location: Remote USA; Canada Workplace: remote ABOUT YOU We are looking for an accomplished Principal Machine Learning Engineer to join our global ML organization. In this role, you will drive innovation across our machine learning ecosystem, architect advanced ML solutions, and mentor junior ML engineers around the world. You will play a key part in shaping our technical direction—leading complex ML initiatives, elevating engineering standards, and guiding teams as they build scalable, production‑ready machine learning systems. If you are ambitious, energized by solving challenging technical problems, passionate about developing talent, and excited to influence the future of ML/AI in the video game industry, this could be the perfect role for you. ABOUT US   Xsolla is a global commerce company with robust tools and services to help developers solve the inherent challenges of the video game industry. From indie to AAA, companies partner with Xsolla to help them fund, distribute, market, and monetize their games. Grounded in the belief in the future of video games, Xsolla is resolute in the mission to bring opportunities together, and continually make new resources available to creators. Headquartered and incorporated in Los Angeles, California, Xsolla operates as the merchant of record and has helped over 1,500+ game developers to reach more players and grow their businesses around the world. With more paths to profits and ways to win, developers have all the things needed to enjoy the game. For more information, visit xsolla.com. Requirements: Modeling Depth Advanced Degree in Statistics, machine learning or related areas. Experience in statistics/ML expertise with a track record of leading high-impact data science initiatives at scale of billions of transactions. Hands-on experience creating, training and fine-tuning models not just integrating hosted model APIs. You should be able to walk through  the data, the objective, what broke, and the before/after evaluation numbers, and why the model did not perform as expected. Experience owning models in production: deployment, monitoring, drift detection, retraining — with real latency budgets, not just research notebooks. Production experience with classical ML for fraud/anomaly detection, recommendation, or churn/LTV (gradient boosting, deep learning, graph-based models). Technology Familiarity Supervised learning, transfer leaning on machine learning as well as neural networks Basic LLM knowledge, especially how to use it and where not to use it. MLOps foundations: feature stores, experiment tracking, model registries (MLflow/W&B-class), continuous training pipelines. Model serving and inference optimization (vLLM-class serving, quantization). Nice-to-Have: Publications, conference talks, or recognized open-source contributions to training/eval tooling . Graph-based fraud detection (fraud rings, device/account linkage).. Gaming, payments,  fraud, advertising  domain experience. Hands-on, up-to-date experience with modern AI tools (e.g., Claude, Copilot, Cursor) for code generation, review, and accelerating day-to-day engineering work. Benefits: We are passionate about fostering a supportive environment for our team, so we prioritize the physical, mental, and emotional well-being of our employees and their families through a comprehensive Benefits Program. This includes 100% company-paid medical, dental, and vision plans, unlimited Flexible Time Off, and a personalized career roadmap for each US employee. By investing in professional development through training and educational opportunities, we ensure that our team thrives both personally and professionally. Together, we’re not just building a business; we’re cultivating a community that values creativity, collaboration, and the transformative power of play.   Equal Employment Opportunity Statement: Xsolla is an equal opportunity employer. We celebrate diversity and are committed to creating an inclusive environment for all employees. We do not discriminate based on race, color, religion, sex, national origin, age, disability, sexual orientation, gender identity, or any other characteristic protected by law. We consider qualified applicants with criminal histories in accordance with the Fair Chance Act.   Criminal History Consideration: For the Data Scientist position, we will conduct a background check that may include the following: Criminal history check Employment verification Education verification   Relevance to Job Responsibilities: The background check is relevant to this position because of the following role responsibilities: Accessing confidential company data Ensuring compliance with regulatory requirements   Rights Under the Fair Chance Act: Applicants are encouraged to inquire about their rights under the Fair Chance Act. If you have questions regarding our hiring practices, please contact careers@xsolla.com.