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Location: San Francisco, CA Senior Software Design Lead, IDEO San Francisco In a nutshell: IDEO San Francisco is in search of a Senior Software Design Lead who will architect the human and technical systems behind the digital products and AI experiences we design, build prototypes that behave like real production software, and be the technical voice client CTOs trust. You'll pick up design at IDEO along the way. Why is this role important for IDEO’s work? Software design makes the biggest impact by turning a product vision into something a client can actually build, and by testing ideas in working code rather than static screens. Their skillset is critical to our business because the work has evolved from designing interfaces to designing systems: how people, software, and AI agents behave together. We count on them to understand data models, APIs and agentic infrastructure from first principles, and speak credibly on technical choices with executives and engineering leaders. Once here you will: Work with product designers, product strategists and design researchers on 8–12+ week digital product engagements that may span from product vision and strategy to full end-to-end build. You will play a key role in turning human insights into technical architecture: data, APIs, integrations and the agentic layer. Build early prototypes with real data, real models and real integrations, so we learn quickly and lower the risk of big bets. Advise client executives and engineering teams on tech stack choices, build-vs-buy, evaluating APIs and LLMs, and feasibility. Design how agents and systems behave on a human's behalf, including guardrails, failure states and how the system stays legible to both people and machines. Raise the craft bar inside IDEO. Create reusable starter kits and toolkits so teams stop building from scratch, and coach designers who prototype in code. Help shape proposals and scope technical work in business development. What does IDEO offer you? IDEO is a place where you can solve diverse, complex challenges for our clients and their organizations, alongside thoughtful, intelligent individuals who lead with curiosity, empathy, and humility. We intentionally craft moments that allow our community to pause, create, and connect. We encourage our community to bring their whole selves to work, respect the need for work-life balance, and allow for autonomy in the design process and your career. As a Software Designer in San Francisco, we provide the opportunity for you to: Learn design from a leading design and innovation firm . IDEO's founders designed Apple's first mouse and the first laptop. Since then, we've kept people at the center of design through every wave of technological change. You'd learn to do field research with real people, synthesize insights and tell compelling stories, and work alongside some of the best designers in the world. Work across many problem spaces . In one year you might redesign a retail or finance app, prototype new ideas in media and entertainment, and take on systemic challenges in health and education. Working across clients and industries stretches your thinking. A good solution isn't only technically sound: it has to fit real people, real businesses and the constraints they work within. Be in the room for the "why" before the "how." You'd shape the question from day one instead of receiving a spec after decisions have been made. And you’d help build this practice at IDEO to shape how next generation digital products are designed and built in the era of AI. Some previous projects to check out: Straight Arrow News : a redesigned news app and website with features like See All Sides, Sources and Media Landscape views. App page views grew 1,170% and active users 700%. This role would design the content and data model underneath and prototype with real feeds. Gates Foundation – Last Mile Money : human-centered design for 100+ digital financial products serving 34M underserved users. This role would work on architecture for low-connectivity settings and evaluate partner platforms and APIs. Jack Technology – Aitu : AI-enabled industrial sewing machines and a humanoid robot, designed around how they work with factory workers. This role would work on system behavior: how the AI signals what it's doing and how humans stay in control. Relevant experience and mindset: Must-haves: 6-8+ years as a software engineer shipping production software, ideally at growth-stage or established tech companies where speed and scale both mattered. You've taken products from concept to launch. 1-2+ years as an engineering lead or solutions architect: scoping, estimating and planning projects, and making architecture decisions others build on. You think at a systems level. Deep software fundamentals, including systems design, data modeling, APIs, and tradeoffs around performance, security and scale, plus fluency in a modern stack (e.g., TypeScript, React, Python, Node; AWS, GCP, Azure). Hands-on building with LLMs (tool use, agents, retrieval, evals) and advanced prototyping. You turn concepts into working software quickly, using real data and integrations, with quality that holds up beyond vibe coding. A track record of working closely with product, design and data, plus curiosity about people and a desire to learn how the craft of software intersects with product and design. You use prototypes as a way to think, not just to deliver. Strong communication and storytelling: architecture diagrams, system maps and presentations that make technical recommendations clear and persuasive to executives and engineering leaders. Nice-to-haves: Comfort with Figma, design systems and front-end craft Client-facing or consulting experience, or taking part in UX research Experience with hardware or connected devices (Arduino, IoT) Pop quiz, Hot Shot: A client's CEO wants "an AI agent in the app by Q2." What's your first question for the CEO? What's a piece of software that's beautifully built under the hood, not just on the surface? What makes it good? Application requirements and instructions: Résumé, LinkedIn + Cover Letter (Optional) GitHub or two to three work samples (code, architecture docs, side projects or technical writing) A short response of about 300 words: "Tell us about a technical decision you made that shaped how people experienced a product. What were the tradeoffs, and what would you do differently today?” Payscale & Benefits IDEO is committed to paying its employees, contractors & vendors equitably, regardless of gender identity or race/ethnicity/cultural identity. The salary range for this role is $130,000-150,000 USD. Final offers may vary based on candidate experience, location, and other factors. As a full-time position, this role is eligible for employee benefits. In addition to base compensation, IDEO offers a rich benefits package including the following: Hybrid Model: Team members based in the San Francisco Bay Area are expected to work in the studio on Tuesdays, Wednesdays, and Thursdays, with flexibility based on client travel, project needs, and deliverables. Candidates based outside the Bay Area are not expected to be in the studio regularly, but should be available to travel as needed for IDEO and client work. Health Benefits: Vision, Dental, Healthcare Financial Benefits: 401K with Employer Contribution Family & Caregiving: Parental Leave, Adoption Assistance, Fertility Assistance, Transgender Medical Assistance, Breastmilk shipment, Back-up/Dependent Care, Pet Insurance Other Benefits: Flexible Work, Flexible Time Off, Paid Holidays, Annual Education Stipend, Home Office Stipend, Commuter Reimbursement, and Phone & Internet Reimbursement Commitment to Diversity At IDEO, we are committed to the work of advancing diversity, equity and inclusion within our organization and in the world. Our purpose of having disproportionate, world-changing impact through design is directly connected to our ability to bring many different perspectives to the table. This applies to our candidates, the people we hire, the clients and customers we serve, and the communities we learn from in our research. Please note that the successful candidate must be based in the United States and have the unrestricted right to work in the US. Unfortunately, we are unable to offer sponsorship for this role. IDEO proudly supports flexible work.

Location: Australia, Adelaide 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. Site Reliability Engineer Our Site Reliability Engineering team plays a significant role in delivering on the promise of a great cloud gaming experience to our customers.  We do this by influencing design and operational decisions towards the overall stability of the gaming service. Our SREs focus on three main things: overall ownership of production, production code quality, and deployments. The successful candidate will be self-directed and able to participate in the way we make decisions at different levels. We expect our SREs to have opinions on the state of our service and provide critical feedback during different phases of the operational lifecycle. We are engaged throughout the software development lifecycle, ensuring the operational readiness and stability. Requirements Minimum of 5+ years working experience in Software Development and/or Linux Systems Administration role. Strong interpersonal, written and verbal communication skills. Available to be scheduled in on-call rotation. Skills & Knowledge Proficient as a Linux Production Systems Engineer, with experience managing large scale Web Services infrastructure. Development experience in one or more of the following programming languages: Python (preferred) Bash, Go, Java, C++, or Rust In addition, experience with at least 3 of the following topics: Distributed data storage at scale (Hadoop, Ceph) NoSQL at scale (MongoDB, Redis, Cassandra) Data Aggregation technologies. (ElasticSearch, Kafka) Scaling and running traditional RDBMS (PostgreSQL, MySQL) with High Availability Monitoring & Alerting (Prometheus, Grafana), and Incident Management toolsets Kubernetes and/or AWS (deployment and management) Software Distribution (Package management and distribution at scale) Configuration Management (ansible, saltstack, puppet, chef) S/W Performance analysis and load testing (QA or SDET experience: a plus) Responsibilities Lead team technical discussions, especially around ongoing improvements in Reliability and Scalability Be involved in creating High Level Designs (HLDs) for new products and platforms Mentor junior SRE staff and enable them for success Lead incident response and post-mortem activities within your assigned service team Work with other Engineers in a cross-functional team to prioritise reliability improvements to address technical debt and toil Contribute to code to improve reliability Implement automation to reduce ongoing toil 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.

Компания ••••••••••, часть DXC Technology, ищет Senior Manual QA Engineer. Задачи и подробности описания доступны на LinkedIn. Стек технологий: Linux, Bash, Python, Jenkins, TeamCity, Octopus. Важно: если работодатель просит войти в систему через iCloud или Google, отправить код, SMS или пароль Telegram, запускать код или устанавливать ПО — откажитесь, это признаки мошенничества.

Location: San Francisco, CA Workplace: Hybrid About Niantic Spatial AI has learned from almost everything online, but it still struggles with the physical world, where 99.5% of economic activity happens. It doesn’t know where things are, how spaces fit together, or what changes over time. That’s one of the biggest unsolved problems in AI, and it’s the one we’re solving at Niantic Spatial. We build spatial AI models and services so people and machines can reason, learn and act safely in the physical world. We turn camera imagery, video, sensor data and archives into accurate, AI-readable representations of real places that robots can train in and machines can use to know exactly where they are. In physical AI, mistakes have real consequences. That’s why we build AI grounded in measured reality, not guesswork, so the people and machines relying on it can trust it to get things right. Our team built some of the world’s most widely used maps. Now we’re building something bigger: a living, measured record of the real world that machines can query, learn from and use to understand how places change over time. If you want to help AI understand the world we actually live in, come build with us. About the Real-World Test Lab (RWTL) Physical AI doesn't get graded on a leaderboard. It gets graded on a factory floor at shift change, in a substation with no GPS, on a site where the lighting is wrong and the stakes are real. The Real-World Test Lab closes the gap between the benchmark and the field. We bring the customer's world inside our walls — their devices, their environments, their hardest conditions, and their definition of success — and make it the bar every release has to clear. As Niantic Spatial's first and most demanding customer, we push our reconstruction, localization, and spatial understanding to their limits, find where they shine and where they break, and turn that into evidence that shapes what we build next. It's a new team at the frontier of physical AI, and you'll help invent how the job is done. About the Role We're hiring a Hardware Operations Engineer with a robotics specialism, reporting to the Director of the Real-World Test Lab, to own the physical layer of our evidence. You'll manage our fleet of capture devices and robots, engineer the test environments and the instrumentation inside them, support the captures our data programs depend on, and run physical test sessions, including the real-robot side of our sim-to-real work. A physical run only counts as evidence if the hardware is characterized, the environment is controlled, the ground truth is trustworthy, and someone can reproduce the run months later. You'll build what makes that possible: fixtures, ground-truth references, calibration and data-offload tooling, and the integrations that feed physical runs into the same automated evaluation system as everything else. You engineer the conditions, not just the setup. When a physical result didn't match expectations, you've worked out whether the hardware, calibration, environment, or system under test was responsible. Then you built the rig, reference, or logging that kept the question from coming back. You know an uninstrumented physical test is an anecdote, and in the Lab you'll be the person who can say with authority whether a strange result is real. What Success Looks Like - 30 days: Fleet inventory and characterization baseline complete, safe operating procedures defined for current spaces, and first instrumentation gaps identified. - 60 days: First physical robot run executed with ground-truth instrumentation, documented well enough to repeat, with results in the Lab's evaluation system. - 90 days: Test environments in regular use with controlled, documented conditions; capture support running smoothly with our data acquisition team; and the physical side of sim-to-real running on a standing instrumented protocol. What You'll Do - Own and Characterize the Fleet - Manage our capture devices, sensors, and robots end to end: inventory, calibration, firmware, maintenance, logistics, and spares. Characterize what each platform can and cannot do, so a device's limits are a known quantity rather than a surprise inside a result. - Engineer the Physical Test Environments - Design, build, and operate the spaces we test in, covering layout, lighting control, markers, obstacle configurations, and survey-grade ground truth references, across our office, rented or workplace-services-supported space, and partner or customer sites. Make each environment a controlled variable rather than an improvisation. - Build the Instrumentation - Create the rigs, mounts, references, and measurement setups that turn a physical run into quantitative data: trajectory ground truth, positional accuracy references, timing and synchronization across sensors, and automated condition logging. - Integrate Physical Runs Into the Evaluation System - Work with the AI Automation Engineer so physical results flow into the same scorecards and comparisons as software-only evaluations, through automated data offload, run metadata, and structured condition capture rather than a spreadsheet and a folder of files. - Run the Physical Test Sessions - Execute robot runs and device-in-the-loop tests: robot bring-up, teleoperation, scripted trials, and the repetition that makes a result statistically meaningful rather than anecdotal. - Own the Physical Half of Sim-to-Real - When a scenario has been evaluated in simulation, engineer and execute its physical counterpart so the comparison is valid, matching environment, route, lighting, sensor configuration, and robot setup, and instrumenting the run so divergence is explainable rather than mysterious. - Own Safety - Define and maintain safe operating procedures for robots and powered equipment in our test spaces, and coordinate with workplace services, IT, Legal, and site owners on access and safety requirements. - Automate Your Own Work - Script the repetitive parts, including configuration, calibration checks, data offload, and condition logging, so setup time falls and a colleague can run a session without you. What You'll Bring - Hands-on engineering experience with robotic hardware, covering bring-up, integration, teleoperation, troubleshooting, and maintenance. Mobile or wheeled platforms preferred. - Built test rigs, fixtures, or instrumentation that produced quantitative measurements of a physical system's behavior. - Designed and run structured physical experiments, including ground truth methodology, and documented conditions rigorously enough for someone else to reproduce them. - Worked with sensor calibration, multi-sensor synchronization, and coordinate frame conventions, and understand how errors in each propagate into a result. - Strong Python and command-line proficiency, with a track record of automating hardware configuration, data offload, or test execution. - Managed hardware fleets operationally: inventory, firmware, calibration schedules, logistics, and repair coordination. - Practical safety experience operating powered equipment or robots around people. - A bachelor's degree in a relevant field, or equivalent experience. Nice to Have - Worked with ROS or ROS 2, including writing nodes or integrating sensor drivers. - Operated NVIDIA Isaac Sim or Isaac Lab as a user, running scenarios someone else authored. - Worked with professional capture hardware such as terrestrial laser scanners, NavVis or Leica systems, LiDAR, 360 cameras, or drones. - Established survey-grade or motion-capture ground truth for localization or navigation evaluation. - Supported photogrammetry, Gaussian splat, or 3D reconstruction capture workflows. - Run test operations at customer or partner sites, with the coordination and discretion that requires. Competencies - Engineering judgment about physical systems. You reason about error sources: calibration drift, timing offsets, mounting rigidity, lighting variation, floor surface. When results disagree, you form a hypothesis and design the measurement that settles it. - Hands-on and unfazed. You'd rather be in the space with the robot than reading about it. Cables, mounts, batteries, firmware, and a session that starts at 7am are all part of the job. - Meticulous about conditions. You instrument rather than remember. You know the value of a physical test lives in how precisely its conditions were captured. - Safety-minded without being slow. You take real responsibility for operating robots around people, and you build procedures where the safe path is also the fast path. - Builds for other people. Your fixtures, scripts, and procedures get used by colleagues without you in the room. - Comfortable in an unfinished environment. Much of this does not exist yet. You'll be selecting equipment, designing the first rigs, and defining how a space gets configured, with support but without a playbook. Compensation & Benefits Base salary range of $142,200 to $193,000. Compensation also includes an annual bonus, equity, and a comprehensive benefits package including medical, dental, and vision coverage, 401(k), and more. Location & Work Model This role is based in our San Francisco office, with three days per week in office. Inclusive Application We know the strongest candidates don't always tick every box. If you're excited about this role and believe you could do it well, we encourage you to apply even if your experience doesn't match every qualification listed - you may be exactly who we're looking for. Equal Opportunity Niantic Spatial is an equal opportunity employer. Individuals seeking employment at Niantic Spatial are considered without regard to race, color, ancestry, national origin, religion, creed, age, gender (including pregnancy, childbirth, breastfeeding or related medical conditions), marital status, physical or mental disability, medical condition, genetic information, military or veteran status, gender identity, gender expression, sexual orientation, or any other protected category under applicable laws. Niantic Spatial will also consider qualified applicants with criminal histories in accordance with applicable laws. Please contact your recruiter if you want to request an accommodation for the job application or interview process. Candidate Privacy I understand that by submitting my job application, the information I provide as part of that application will be used in accordance with Niantic Spatial's Privacy Notice for Job Applicants and Candidates https://www.nianticspatial.com/applicant-privacy-notice.

Location: San Francisco, CA Workplace: Hybrid About Niantic Spatial AI has learned from almost everything online, but it still struggles with the physical world, where 99.5% of economic activity happens. It doesn’t know where things are, how spaces fit together, or what changes over time. That’s one of the biggest unsolved problems in AI, and it’s the one we’re solving at Niantic Spatial. We build spatial AI models and services so people and machines can reason, learn and act safely in the physical world. We turn camera imagery, video, sensor data and archives into accurate, AI-readable representations of real places that robots can train in and machines can use to know exactly where they are. In physical AI, mistakes have real consequences. That’s why we build AI grounded in measured reality, not guesswork, so the people and machines relying on it can trust it to get things right. Our team built some of the world’s most widely used maps. Now we’re building something bigger: a living, measured record of the real world that machines can query, learn from and use to understand how places change over time. If you want to help AI understand the world we actually live in, come build with us. About the Real-World Test Lab Physical AI doesn't get graded on a leaderboard. It gets graded on a factory floor at shift change, in a substation with no GPS, on a site where the lighting is wrong and the stakes are real. The Real-World Test Lab closes the gap between the benchmark and the field. We bring the customer's world inside our walls - their devices, their environments, their hardest conditions, and their definition of success - and make it the bar every release has to clear. As Niantic Spatial's first and most demanding customer, we push our reconstruction, localization, and spatial understanding to their limits, find where they shine and where they break, and turn that into evidence that shapes what we build next. It's a new team at the frontier of physical AI, and you'll help invent how the job is done. About the Role We're hiring an AI Automation Engineer, reporting to the Director of the Real-World Test Lab, to build the system that produces our evidence. Today our evaluations are manual, inconsistent, and slow. You'll design and own the automation that runs them end to end on every relevant release, with no human driving it, turning one-off experiments into an always-on service the whole company relies on. This role is about owning the evidence, not executing a test plan. You'll decide how each workflow gets exercised, and you'll be measured on whether the company can trust the results, not on how much automation exists. The work counts when it keeps running correctly months later, without you in the loop. You believe evaluation is engineering, not process. You've built systems that test other systems, and you know the difference between a script that works on your laptop and infrastructure a team can trust. When a result couldn't be reproduced, you fixed the tooling instead of arguing about the number. What You'll Do - Build the Evaluation Machine - Own the automation that executes Lab evaluations end to end: environment setup, run orchestration, artifact capture, and result collection. Make reruns free so we test constantly rather than occasionally. - Make Results Comparable - Instrument scorecards so a result can be compared across product versions, devices, and capture conditions. A number without its lineage is not evidence. - Automate the Agent-Driven Layer - Build the agent workflows that exercise priority customer use cases at realistic scale, across the graded difficulty spectrum from easy to frontier, and be honest about where agents cannot yet replace human judgment. - Kill Manual Work Permanently - Convert one-off experiments into standing protocols that run on every relevant release. Automate recurring inspection wherever it can be automated, and route what genuinely cannot to scalable human review. - Make Failures Actionable - Produce diagnostics precise enough that a finding reaches its owner with a reproducible case and data attached. Findings that need re-investigation before anyone can act on them are half-finished. - Keep Data From Being the Bottleneck - Work with the AI Data Manager so every run is reproducible from a known dataset state, without anyone downloading and re-uploading data by hand. What Success Looks Like - 30 days: One priority workflow evaluated end to end with no manual steps, with results in a comparable scorecard. - 60 days: Evaluations trigger automatically on relevant releases, with diagnostics that route failures to the right owners. - 90 days: Three priority workflows under standing automated evaluation, with version-over-version comparison for leadership. What You'll Bring - Built and maintained production-grade automation or test infrastructure that other engineers relied on daily. - Experience evaluating systems where correctness is graded rather than binary, meaning quality, accuracy, or latency thresholds rather than pass/fail assertions. - Strong Python, with fluency in orchestration, CI-style pipelines, cloud storage, and reproducible environments. - Worked with backend services and APIs you did not own, integrating against them without becoming a bottleneck for their team. - Written up a technical finding clearly enough that a non-author could act on it without a meeting. - A bachelor's degree in a relevant field, or equivalent experience. Nice to Have - Built agent-based or LLM-driven automation for a task that previously required human judgment. - Worked on evaluation or benchmarking for computer vision, 3D reconstruction, or spatial systems. - Instrumented dashboards or scorecards that leadership used for release decisions. - Operated data provisioning or registry infrastructure across multiple environments and access models. Competencies - Intellectually honest. You design tests that produce answers people can trust, and you refuse to let a benchmark imply more than the evidence supports — including when the honest answer is inconvenient for your own work. - Pragmatic. You find the fastest path to a defensible result without going through one-way doors that undermine scaling. You have no patience for building infrastructure nobody has asked for yet. - AI-forward by instinct. You reach for agents and LLM-driven automation before you reach for headcount, and you're rigorous about verifying what they produce. - Builds for other people. You've maintained something after the initial excitement wore off. Your tooling is documented, your failures are legible, and colleagues use what you build without asking you to run it for them. Compensation & Benefits Base salary range of $158,400 to $210,000 per year. Compensation also includes an annual bonus, equity, and a comprehensive benefits package including medical, dental, and vision coverage, 401(k), and more. Location & Work Model This role is based in our San Francisco office, with three days per week in office. Inclusive Application We know the strongest candidates don't always tick every box. If you're excited about this role and believe you could do it well, we encourage you to apply even if your experience doesn't match every qualification listed - you may be exactly who we're looking for. Equal Opportunity Niantic Spatial is an equal opportunity employer. Individuals seeking employment at Niantic Spatial are considered without regard to race, color, ancestry, national origin, religion, creed, age, gender (including pregnancy, childbirth, breastfeeding or related medical conditions), marital status, physical or mental disability, medical condition, genetic information, military or veteran status, gender identity, gender expression, sexual orientation, or any other protected category under applicable laws. Niantic Spatial will also consider qualified applicants with criminal histories in accordance with applicable laws. Please contact your recruiter if you want to request an accommodation for the job application or interview process. Candidate Privacy I understand that by submitting my job application, the information I provide as part of that application will be used in accordance with Niantic Spatial's Privacy Notice for Job Applicants and Candidates https://www.nianticspatial.com/applicant-privacy-notice.

Requirements: — 4–5+ years of AI/ML or Data Science experience — Advanced Python, SQL, and ML experience — Hands-on experience with LLMs, RAG, embeddings, and transformers — Experience with PyTorch/TensorFlow, Hugging Face, LangChain or LlamaIndex — Experience with vector databases such as Pinecone, FAISS, Milvus, or OpenSearch — MLOps experience with Docker, Kubernetes, MLflow, and CI/CD — AWS and production AI/ML deployment experience The role involves building production GenAI systems, RAG pipelines, recommendation/personalization models, and scalable ML infrastructure from development through deployment. The position is fully remote, with full-time or contract options.

Location: Boston, MA Workplace: OnSite At WHOOP, we're on a mission to unlock and inspire performance for life. WHOOP empowers members to perform at a higher level through a deeper understanding of their bodies and daily lives. WHOOP is hiring a Staff Hardware Test Engineer (Manufacturing) to lead manufacturing test strategy and deliver scalable test solutions that ensure every WHOOP device meets the highest standards of quality and reliability. You will take ownership of complex manufacturing test challenges from high-level objectives through strategy, execution, and production outcomes. Working across Manufacturing Engineering, Electrical Engineering, Firmware, Mechanical Engineering, Quality, and Reliability, you will drive alignment on test strategy, influence product and manufacturing decisions, and deliver measurable improvements in test coverage, quality, yield, cycle time, and cost. You will also establish best practices, raise engineering standards, and mentor engineers across the team.   RESPONSIBILITIES: - Lead the architecture, development, validation, and deployment of automated test equipment (ATE), electrical test fixtures, and control systems for engineering validation, NPI, and mass production. - Develop and establish scalable automated test methodologies and applications using Python to validate electrical, functional, RF, and sensor performance on manufacturing lines. - Drive cross-functional alignment across Hardware, Firmware, Manufacturing, and Quality engineering teams to make tradeoffs around test coverage, product quality, cycle time, yield, cost, and schedule. - Lead complex and ambiguous manufacturing test investigations, structuring problems, identifying technical and operational risks, and driving cross-functional teams to root cause and resolution. - Own manufacturing test readiness and outcomes through factory bring-up and production ramp with manufacturing partners, including fixture qualification, calibration, correlation, troubleshooting, and continuous improvement. - Analyze manufacturing and test data to identify systemic opportunities and deliver measurable improvements in yield, process capability, test coverage, quality, cycle time, and cost. - Establish manufacturing test best practices, methodologies, and standards that improve execution quality and scalability across programs. - Identify technical and execution risks proactively and drive mitigation strategies with engineering teams and manufacturing partners. - Contribute to product and manufacturing roadmaps by identifying test technology, infrastructure, and capability investments required for future products. - Mentor engineers and provide technical guidance on test architecture, debugging, automation, and manufacturing test methodologies. QUALIFICATIONS: - Bachelor's degree in Electrical Engineering, Computer Engineering, or a related technical discipline. - 10+ years of experience developing hardware test systems, automated test equipment, or manufacturing test solutions for high-volume consumer electronics or similarly complex products. - Demonstrated experience independently owning manufacturing test strategy and execution from product development through NPI, ramp, and mass production. - Proven ability to lead complex, ambiguous technical initiatives and drive outcomes across multidisciplinary engineering and operations teams. - Deep understanding of electronics, instrumentation, measurement systems, and communication protocols such as UART, I²C, SPI, USB, Bluetooth, and NFC. - Experience designing electronic circuits and PCBAs using tools such as Altium Designer. - Strong proficiency in Python for developing automated manufacturing test applications; experience with C/C++ or other systems languages is a plus. - Extensive experience designing, validating, and debugging manufacturing test fixtures, instrumentation, and production hardware. - Strong understanding of manufacturing test methodologies and data analysis, including test coverage, calibration, correlation, GR&R, process capability, yield analysis, and failure analysis. - Extensive experience working with CMs and JDMs, including factory bring-up, NPI, production ramp, and sustaining support. - Demonstrated ability to influence technical decisions, align stakeholders, and communicate effectively across engineering, operations, and leadership. - Track record of delivering measurable improvements in manufacturing quality, yield, efficiency, test coverage, or cost. - Experience mentoring engineers and improving engineering practices within a team. - Ability to travel up to 15%, both domestically and internationally. - Strong commitment to leveraging AI tools in day-to-day tasks, ensuring AI-assisted work aligns with the same high-quality standards as personal contributions. This role is based in the WHOOP office located in Boston, MA. The successful candidate must be prepared to relocate if necessary to work out of the Boston, MA office. Interested in the role, but don’t meet every qualification? We encourage you to still apply! At WHOOP, we believe there is much more to a candidate than what is written on paper, and we value character as much as experience. As we continue to build a diverse and inclusive environment, we encourage anyone who is interested in this role to apply. WHOOP is an Equal Opportunity Employer and participates in E-verify https://www.e-verify.gov/ to determine employment eligibility. It is unlawful in Massachusetts to require or administer a lie detector test as a condition of employment or continued employment. An employer who violates this law shall be subject to criminal penalties and civil liability. The WHOOP compensation philosophy is designed to attract, motivate, and retain exceptional talent by offering competitive base salaries, meaningful equity, and consistent pay practices that reflect our mission and core values. At WHOOP, we view total compensation as the combination of base salary, equity, and benefits, with equity serving as a key differentiator that aligns our employees with the long-term success of the company and allows every member of our corporate team to own part of WHOOP and share in the company’s long-term growth and success. The U.S. base salary range for this full-time position is $180,000.00 - $215,000.00 Salary ranges are determined by role, level, and location. Within each range, individual pay is based on factors such as job-related skills, experience, performance, and relevant education or training. In addition to the base salary, the successful candidate will also receive benefits and a generous equity package. These ranges may be modified in the future to reflect evolving market conditions and organizational needs. While most offers will typically fall toward the starting point of the range, total compensation will depend on the candidate’s specific qualifications, expertise, and alignment with the role’s requirements. Learn more about WHOOP https://www.whoop.com/us/en/careers/?srsltid=AfmBOopKmph9d0DLBlogZu8mx6do0dzjNS8eJlfc4PQqQtdU6F8DGKBg.

Location: San Francisco, CA | New York City, NY | Seattle, WA About Anthropic Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems. About the role Data Center Security Engineering (DCSE) owns physical security across Anthropic's data center footprint: the sites, the hardware inside them, and the supply chain that delivers it. The Security Fusion Platform is where physical security signals from across that footprint are correlated and put in front of the analysts who act on them. It carries continuous monitoring, audit evidence, metrics, and analyst response, along with the classification and analytics pipelines that turn sensor data into events. You'll build and own the platform analysts work from. This is a foundational role. You'll build the software platform that carries a signal from source to analyst decision, ship its first versions, and keep them running in production. The sensors and systems at each site belong to other DCSE engineers; you define the event contract their owners deliver to, and you build the classification and analytics pipelines that run over what those sources produce. Physical security systems engineers own the operations spaces; you own the software that runs in them. You'll work alongside engineers focused on the sites themselves. What you lead, you own end to end: the design, the technical approach, and the result in production. You'll work with external vendors and integrators, setting requirements and acceptance criteria for what you own and deciding what the team builds itself. We use Claude throughout our engineering workflow and expect you to as well. This role involves regular travel to data center sites. No security clearance is required. These are critical services, and you'll take part in on-call for them. Key responsibilities Design and build the event bus and normalized event schema: source identity, sequencing, health state, and the adapter contract that owners of each sensor and system feed build to, plus adapters where needed Build correlation and alerting: the rules and services that turn events from independent sources into alerts an analyst can act on Build the classification and analytics pipelines: computer vision and multi-sensor fusion, with results landing on the event bus Build analyst surfaces: the queue, escalation tooling, dashboards, camera viewers, and video wall content a 24/7 watch works from, plus video analytics integration so camera streams arrive on the bus as events Deliver audit evidence: durable, tamper-evident event history, evidence exports, and the retention and access controls around them Instrument metrics and self-monitoring: detection rates, nuisance ratios, alert aging, time to resolve; heartbeats and loss-of-telemetry alarms on every feed, tested fail-secure behavior, and change audit on every rule Deploy across sites: site-local components, central services, and the sync that keeps configuration, rules, and state consistent, then commission each site and hand over to the analysts Specify the compute the platform runs on (servers, storage, and the platform's network segment) to the team's segmentation and hardening baseline Write requirements and acceptance criteria for what vendors and integrators deliver, review deliverables against them, and make the call on accepting or rejecting the work Minimum qualifications Have built and run event-driven backend systems in production Are proficient in at least one backend language such as Python, Go, or Rust, and have worked with a message bus or event streaming system such as Kafka Have owned a system end to end: set the design, made the tradeoffs, shipped it, and answered for it in production Have integrated with external systems you did not control (vendor APIs, on-prem appliances, video platforms, building systems) and designed for a life beyond any one vendor Have directed vendor or integrator work: written requirements and acceptance criteria, reviewed deliverables against them, and decided what gets accepted Design for the failure you won't see coming (a silent feed, a lost network path, a replayed event, a de-tuned rule), know how your system behaves when a source lies or goes quiet, and test for it Build tools for people who use them under pressure and iterate with those operators Preferred qualifications Experience building or operating a security operations platform (SOC, physical security operations, or the detection engineering behind one) Experience building ML or computer vision inference pipelines over video and sensor streams PSIM (physical security information management) or VMS (video management system) integration experience, or familiarity with physical access control and building systems data Experience producing evidence for auditors or assessors: log integrity, retention, chain of custody Experience using AI-assisted development tooling as a core part of your workflow The annual compensation range for this role is listed below. For sales roles, the range provided is the role’s On Target Earnings ("OTE") range, meaning that the range includes both the sales commissions/sales bonuses target and annual base salary for the role. Annual Salary: $320,000 — $405,000 USD Logistics Minimum education: Bachelor’s degree or an equivalent combination of education, training, and/or experience Required field of study: A field relevant to the role as demonstrated through coursework, training, or professional experience Minimum years of experience: Years of experience required will correlate with the internal job level requirements for the position Location-based hybrid policy: Currently, we expect all staff to be in one of our offices at least 25% of the time. However, some roles may require more time in our offices. Visa sponsorship: We do sponsor visas! However, we aren't able to successfully sponsor visas for every role and every candidate. But if we make you an offer, we will make every reasonable effort to get you a visa, and we retain an immigration lawyer to help with this. We encourage you to apply even if you do not believe you meet every single qualification. Not all strong candidates will meet every single qualification as listed.  Research shows that people who identify as being from underrepresented groups are more prone to experiencing imposter syndrome and doubting the strength of their candidacy, so we urge you not to exclude yourself prematurely and to submit an application if you're interested in this work. We think AI systems like the ones we're building have enormous social and ethical implications. We think this makes representation even more important, and we strive to include a range of diverse perspectives on our team. Your safety matters to us. To protect yourself from potential scams, remember that Anthropic recruiters only contact you from @anthropic.com email addresses. In some cases, we may partner with vetted recruiting agencies who will identify themselves as working on behalf of Anthropic. Be cautious of emails from other domains. Legitimate Anthropic recruiters will never ask for money, fees, or banking information before your first day. If you're ever unsure about a communication, don't click any links—visit anthropic.com/careers directly for confirmed position openings. How we're different We believe that the highest-impact AI research will be big science. At Anthropic we work as a single cohesive team on just a few large-scale research efforts. And we value impact — advancing our long-term goals of steerable, trustworthy AI — rather than work on smaller and more specific puzzles. We view AI research as an empirical science, which has as much in common with physics and biology as with traditional efforts in computer science. We're an extremely collaborative group, and we host frequent research discussions to ensure that we are pursuing the highest-impact work at any given time. As such, we greatly value communication skills. The easiest way to understand our research directions is to read our recent research. This research continues many of the directions our team worked on prior to Anthropic, including: GPT-3, Circuit-Based Interpretability, Multimodal Neurons, Scaling Laws, AI & Compute, Concrete Problems in AI Safety, and Learning from Human Preferences. Come work with us! Anthropic is a public benefit corporation headquartered in San Francisco. We offer competitive compensation and benefits, optional equity donation matching, generous vacation and parental leave, flexible working hours, and a lovely office space in which to collaborate with colleagues. Guidance on Candidates' AI Usage: Learn about our policy for using AI in our application process.

QA Tester уровня Junior в образовательную компанию ITK academy. Удалённая работа, тестирование веб‑продуктов с использованием Python, REST API и SQL. Требования: — Python — REST API — HTTP — HTML — SQL Условия: — Удалённая работа — Зарплата 75 000 – 90 000 ₽ в месяц

Задачи: — Разработка финансовых инструментов и сервисов, которые улучшат опыт клиентов и партнёров по всему миру, помогут Яндексу готовить финансовую отчётность и управлять финансовыми потоками. Требования: — Опыт работы с базой данных Oracle. — Хорошее владение SQL, Python, PL/SQL и готовность к обучению. — Умение диагностировать производительность систем и оптимизировать SQL-запросы. — Знакомство с микросервисной архитектурой, системами контроля версий и разработкой через тестирование. — Желание развиваться в построении масштабируемых систем на миллионы пользователей. Будет плюсом: — Опыт работы с Python и PostgreSQL. — Опыт работы с высоконагруженными системами на базе микросервисной архитектуры, способными масштабироваться до 100 тыс. RPS. — Опыт разработки и поддержки распределённых систем хранения финансовых данных. Условия: — В компании есть профессиональные сообщества для разных профилей, включая разработчиков и дизайнеров. — Книжные и языковые клубы.

Задачи: — Общаться с бизнес-заказчиками и пользователями, выявлять реальные проблемы и переводить их в технические задачи. — Проводить discovery и анализировать процессы AS-IS / TO-BE. — Формализовывать требования к внутренним сервисам, AI/Data-решениям, CRM, BI и интеграциям. — Описывать ТЗ и функциональные требования, User Story, BPMN, CJM, sequence-диаграммы и критерии приёмки. — Прорабатывать требования к данным: определять источники, необходимые поля, правила расчётов, периодичность обновления и проверки качества. — Работать с SQL и данными для проверки гипотез, сверки результатов и анализа работы решений. — Проектировать и описывать интеграции через REST API, webhooks и внешние сервисы; работать с Postman и Swagger/OpenAPI. — Формировать и вести backlog инициатив, декомпозировать решения на задачи для разработки и определять приоритеты. — Взаимодействовать с разработчиками, аналитиками и другими участниками команды на всём цикле реализации. — Проводить функциональное тестирование, принимать реализованные задачи и сопровождать внедрение. — Участвовать в запуске AI/Data-проектов: скоринг, прогнозирование, аналитика, автоматизация процессов, LLM/AI-ассистенты и другие прикладные решения. — Оценивать не только технический результат, но и эффект решения для бизнеса: снижение ручного труда, скорость процессов, качество работы, конверсию, retention и другие продуктовые показатели. Требования: — Опыт работы системным аналитиком, solution engineer, техническим аналитиком или специалистом по внедрению от 4 лет. — Опыт самостоятельного ведения сложных задач от discovery и сбора требований до внедрения решения в production. — Умение работать с неоднозначными бизнес-задачами, где готового технического задания изначально нет. — Хорошее понимание интеграций и клиент-серверного взаимодействия. — Практический опыт работы с REST API, webhooks, JSON, Postman, Swagger/OpenAPI. — Уверенный SQL и понимание принципов работы с данными. — Опыт описания AS-IS / TO-BE, BPMN, User Story, функциональных требований и критериев приёмки. — Опыт работы с Jira / Confluence или аналогичными инструментами. — Умение разговаривать как с бизнесом и руководителями, так и с техническими специалистами. — Способность самостоятельно структурировать проблему, предлагать варианты решения и доводить договорённости до результата. Будет большим плюсом: — Опыт реализации AI/ML/LLM-проектов в production. — Опыт работы с CRM, BI, Data-платформами или системами автоматизации. — Знакомство с Kafka и event-driven интеграциями. — Опыт работы с n8n или другими automation/low-code инструментами. — Знакомство с Python. — Опыт работы с Superset, ELK, Trino или аналогичными инструментами. — Опыт взаимодействия с ML/Data Science-командами. — Опыт проектов, где можно показать конкретный измеримый эффект после внедрения. Условия: — Возможность удалённой работы. — Полный рабочий день. — Конкурентная заработная плата по итогам собеседования. — Спорт вместе с компанией, возможность заниматься спортом с командой. — Участие в стратегически важных проектах по цифровой трансформации крупных игроков рынка.

Задачи: — развитие и тюнинг моделей ранжирования и рекомендаций — разработка новых ML-решений и feature engineering — прототипирование и внедрение моделей в production на Python — работа с обучающими выборками и анализ результатов A/B-тестов — улучшение продуктовых метрик Требования: — опыт внедрения ML-моделей в production — знание основных методов и алгоритмов ML — понимание нейросетевых моделей для обработки текста — уверенный Python (NumPy, SciPy, PyTorch, scikit-learn) — знание алгоритмов и структур данных — опыт с A/B-тестами Условия: — работа в команде экспертов — широкий технологический стек, сотни проектов — гибкий график работы — льготы и меры господдержки, предусмотренные для IT-компаний (аккредитованы Минцифры) — возможность разрабатывать IT-решения для разных отраслей, пробовать новые технологии — процессы удаленной работы и просторные офисы с зонами коворкинга — возможность профессионального роста: стать тимлидом, архитектором, разработчиком full-stack — развитая система наставничества, сертификация за счёт компании, участие в конференциях, изучение английского языка — активный обмен опытом, митапы, хакатоны, доклады по развитию hard и soft skills — 50+ клубов по интересам и профессиональных сообществ внутри компании — уютный мерч — полный соцпакет и «белая» зарплата — корпоративная база знаний и социальная сеть для общения с 1500+ сотрудниками

Задачи: • Разрабатывать и поддерживать свою часть GrapheneOS. • Портировать изменения при выходе новой версии Android, особенно в периоды высокой нагрузки. • Проводить ревью кода. • Исправлять баги, уязвимости и проблемы с производительностью вместе с командой. • Следовать руководствам по разработке проекта. • Самостоятельно сдавать готовые функции и исправления без необходимости постоянного контроля. Требования: • Реальный опыт разработки на базе AOSP, ядра Linux и его защиты, аллокаторов памяти или платформенного ПО Android. • Достаточный уровень seniority для самостоятельной работы и сдачи кода на ревью. • Знание языков программирования (в порядке приоритета): Java, Kotlin, C++, C, Rust, JavaScript, TypeScript, arm64 assembly, Bash, Python. Желательно: • Опыт вклада в свободные и открытые проекты. • Комфортная работа с вопросами приватности и безопасности. Условия: • Полностью удалённая работа из любой страны, кроме России и Беларуси. • Оплата в криптовалюте. • Формат сотрудничества B2B, предпочтительно full-time, но возможен part-time. • Гибкий график, от 80 часов в месяц, асинхронная работа. Ограничения: • Английский язык не ниже уровня B1. • Отсутствие опыта работы в государственных или близких к ним компаниях России за последние 5 лет. • Не рассматриваются кандидаты, занимающиеся только прошивкой, сборкой чужих деревьев и моддингом без собственных изменений в коде ОС.

Location: Americas; EMEA Workplace: Remote RevenueCat gives app businesses the infrastructure and tools to build, run, and improve their monetization. Since graduating from YC's S18 batch, we've grown into the default monetization platform for mobile. We're in 50%+ of newly shipped subscription apps, we process $16B+ in annual purchase volume, and we help everyone from a solo developer to the OpenAI mobile team understand and grow their revenue. We're a remote-first team of 150+ people across 25+ countries, guided by values we actually practice: Customer Obsession, Always Be Shipping, Own It, and Balance. This isn't the right fit for everyone. The systems you’ll build here manage billions of dollars and touch hundreds of millions of end users, and we don’t take that lightly. You'll be expected to work hard and hold a high bar for what you ship, alongside some of the sharpest, most driven people you've worked with. If that sounds energizing rather than exhausting, keep reading. THE ROLE We are looking for a Senior SDK Engineer to join our team to help design, build, ship, and support end-to-end features and products that will help developers make more money. We want to bring somebody onboard that is product focused, customer focused, and passionate about building mobile SDKs with a great developer experience. This role will primarily focus on either our iOS or Android SDK (depending on your core experience), but also requires contributions to our other platform + hybrid SDKs, from time to time. Experience in these other platforms is not a requirement but the desire and excitement to work on them is. ABOUT YOU: - You have 8+ years of experience in software, and have shipped mobile apps to Apple App Store or Google Play Store - You are very experienced in Swift and Swift UI (iOS) or Java/Kotlin and Compose UI (Android) - You are not afraid of iOS, Android, cross platform, or hybrid platforms. - You love making products for developers. - You love shipping good products. - You have deep empathy for customers. - You know how to ship high quality code using good testing practices in iterative cycles. - You collaborate well with others and can communicate effectively in a fully-remote culture. - You are proactive. When you see something broken, you jump on it to fix it or suggest improvements. - You move fast, test, and iterate quickly. - You ask questions when you're stuck on something. - You are excited to be part of a growing team. BONUS: - You have experience building SDKs. - You have experience in server driven user interfaces. - You have shipped your own app to Google Play or the App Store. - You have experience in Python or other backend development. IN THE FIRST MONTH, YOU'LL: - Learn about our customers, our SDK, the in-app subscription ecosystem, the problems RevenueCat helps solve, and how our SDKs fit into that. - Open your first pull request and deploy changes to be used by 32,000+ apps. - Onboard into our codebase by working on your very first project and shipping it 😺 - Learn how we maintain as much transparency as possible with our work in GitHub. - Begin reviewing code and add to our engineering culture. WITHIN THE FIRST 3 MONTHS, YOU'LL: - Be able to scope and work on projects self-sufficiently. - Participate in code reviews, and contribute in other ways (testing, visibility, etc.) to improve reliability and quality of the codebase. - Understand every product owned by your team. - Understand and improve many of the codebases that the team works with. WITHIN THE FIRST 6 MONTHS, YOU'LL: - Be the most knowledgeable person on several key features. - Have your own initiatives for improving the code base and our infrastructure. - Contribute to the team roadmap and technical direction. - Have shipped a major product or feature. WITHIN THE FIRST 12 MONTHS, YOU'LL: - Know more about mobile subscriptions than anyone ever should. - Use your knowledge to contribute ideas to RevenueCat’s suite of solutions. - Be able to spec, architect and build any feature we might want to build. - Have experience in all areas of technology stack related to SDKs and be the primary expert/owner of at least one area. - Help mentor and onboard new engineers, shaping the RevenueCat engineering culture as we continue to expand the team. - Help prioritize the roadmap to provide maximum value to developers, while pushing the team and technology forward. WHAT WE OFFER: - Competitive equity in a fast-growing, Series C startup backed by top-tier investors, including Y Combinator - 10-year window to exercise vested equity options - A fully remote environment designed around autonomy and flexibility - 4-5 weeks of flexible time off annually - Paid desk at a co-working space - Workspace budget and continuous learning stipend INTERVIEWING AT REVENUECAT Our interview process is rigorous on purpose. We want to be sure that everyone we bring on is genuinely excited about this work, aligned with how we operate, and ready to meet our high bar for performance. Curious what that actually looks like? Read more in our blog post on how we hire at RevenueCat https://www.revenuecat.com/blog/company/how-we-hire-at-revenuecat/, including tips to help you succeed. MORE ON HOW WE WORK: - How we think about hiring and team design: Building a Winning Team https://app.notion.com/p/Building-a-Winning-Team-69e431c4c3df4b13961523cd93c0e9bf?pvs=21 - The values that guide how we work: Our Values https://www.revenuecat.com/blog/values/ - How we work as a remote team: How We Work Remotely at RevenueCat https://www.revenuecat.com/blog/how-we-work-remotely-at-revenuecat/ - What to expect from our interview process: Interview Expectations https://app.notion.com/p/RevenueCat-Interview-Expectations-1bdcb1a108b0800c8941def0f076f9e2?pvs=21 - What to expect from comp and employment: Employment FAQ https://app.notion.com/p/RevenueCat-Compensation-Employment-Overview-184cb1a108b0801398d4e525928597ab?pvs=21

Задачи: — Руководить командой NLP-инженеров и backend-разработчиков. — Планировать и приоритизировать задачи, проводить ревью и контролировать качество решений. — Определять архитектуру и технологический стек LLM/NLP-разработки. — Вести ключевой проект направления — банковскую платформу парсинга документов. — Формулировать технические требования и декомпозировать задачи. — Вести технический бэклог и приоритизировать запросы от нескольких заказчиков. — Контролировать реализацию решений на всех этапах разработки. — Взаимодействовать с бизнес-заказчиками и смежными IT-командами. — Развивать экспертизу команды, организовывать обмен знаниями и формировать лучшие практики. — Заниматься наставничеством и профессиональным развитием сотрудников. — Участвовать в выборе, интеграции и оптимизации ML-, OCR-, LLM- и VLM-моделей. — Проектировать масштабируемые решения для промышленной эксплуатации моделей. Требования: — От пяти лет коммерческого опыта в NLP, LLM или backend-разработке на Python. — Не менее трёх лет опыта в роли Tech Lead. — Практический опыт управления инженерной командой и технической частью продукта. — Глубокое понимание ML-алгоритмов, OCR-моделей, архитектуры LLM и VLM-моделей. — Уверенное владение Python. — Практический опыт работы с PyTorch, Hugging Face, sentence-transformers и FastAPI. — Опыт интеграции и оптимизации моделей Qwen, DeepSeek, Llama, Mistral или аналогичных. — Понимание принципов оптимизации inference. — Знание подходов к масштабированию, кэшированию и распределённому inference. — Опыт взаимодействия с бизнес-заказчиками и смежными IT-командами. — Умение формировать и вести технический бэклог. — Навыки сбора, декомпозиции и приоритизации требований от нескольких заказчиков. Будет плюсом: — Опыт интеграции LLM с корпоративными системами и BI-платформами. — Знание инструментов мониторинга LLM по показателям latency, quality и cost. — Опыт работы с библиотеками Docling и Kreuzberg. — Опыт проектирования и разработки высоконагруженных систем. Условия: — Полный рабочий день. — Можно удалённо. — Работа в команде экспертов. — Широкий технологический стек, сотни проектов. — Гибкий график работы. — Возможность разрабатывать IT-решения для разных отраслей, пробовать новые технологии. — Процессы удаленной работы и просторные офисы с зонами коворкинга. — Возможность профессионального роста: тимлид, архитектор, full-stack разработчик. — Система наставничества, сертификация, участие в конференциях, изучение английского. — Внутренние и внешние митапы, хакатоны, доклады по развитию hard и soft skills. — 50+ клубов по интересам и профессиональных сообществ. — Корпоративная база знаний и социальная сеть для сотрудников.

Задачи: — Разрабатывать Python / SQL-пайплайны загрузки и обработки данных из файлов, API и информационных систем. — Проектировать таблицы хранилища: сущности, связи, правила преобразования. — Встраивать псевдоанонимизацию и проверки качества в потоки данных. — Готовить признаки и версионируемые train/validation/test-выборки. — Обеспечивать прослеживаемость и воспроизводимость ML / LLM-экспериментов. — Вместе с ML-командой развивать оценку моделей, подготовку данных для RAG и дообучения. Требования: — Уверенные Python и SQL, поддерживаемый код за пределами ноутбуков. — Практический опыт ETL / ELT: оркестрация, инкрементальная обработка, повторные запуски. — Опыт с S3, Parquet, pandas / Polars / PyArrow. — Понимание жизненного цикла ML: выборки, baseline, метрики, проверка утечек. — Опыт с MLflow, ClearML или аналогами. — Linux, Git, Docker, CI/CD. — Умение работать с чувствительными данными. Условия: — Занятость: full-time — Формат работы: гибридный / удаленный по РФ

Задачи: — поддерживать и развивать инфраструктуру продукта на Linux; — обеспечивать стабильную работу серверов, прокси и CDN; — обеспечивать доступность продукта для пользователей из разных стран; — автоматизировать инфраструктурные процессы и развивать CI/CD; — развивать мониторинг и участвовать в расследовании инцидентов; — участвовать в разработке внутренних инструментов. Требования: — уверенное знание Linux; — работа с bare-metal; — опыт эксплуатации production-инфраструктуры; — опыт работы с Nginx; — опыт автоматизации рутинных задач; — умение самостоятельно находить причины сложных технических проблем; — опыт с MongoDB, ClickHouse, Prometheus, Grafana (будет плюсом); — опыт с GitHub Actions или другими CI/CD-системами (будет плюсом); — опыт с Docker (будет плюсом); — знание Python или Bash (будет плюсом). Условия: — оформление по ТК РФ; — офис / гибрид / полная удалёнка; — ДМС со стоматологией; — всё необходимое оборудование; — компенсация профессионального развития; — компенсация изучения английского языка.

Задачи: — Тестирование хранилища данных — Проверка полноты, согласованности и точности данных — Валидация преобразований данных по принципу source-to-target — Проведение регрессионного тестирования — Анализ результатов тестирования и выявление дефектов — Автоматизация тестовых проверок на Python / PyTest — Работа с большими объёмами данных — Взаимодействие с разработчиками и системными аналитиками — Работа с Jira, Confluence, Git и инструментами DWH Требования: — Высшее экономическое или математическое образование — Опыт работы в QA от 1.5 лет, включая тестирование хранилищ данных — Понимание архитектуры DWH: схемы «звезда», «снежинка», Data Vault — Знание PL/SQL, Oracle, Python — Понимание методик тестирования хранилищ данных — Опыт проверки полноты, согласованности и точности данных — Опыт валидации преобразований source-to-target — Опыт регрессионного тестирования — Навыки автоматизации тестов на Python / PyTest — Аналитический подход и внимание к деталям при работе с данными — Умение работать с большими объёмами информации — Коммуникация с разработчиками и аналитиками Условия: — Удалённая работа по РФ — Оформление по ТК РФ или ИП — Техника предоставляется — ДМС со стоматологией — Компенсация фитнеса — Скидки на обучение, кино, Skyeng — Индексация ЗП — Зарплата 130–150 тыс. руб. на руки