Location: San Francisco; New York
Patreon is a media and community platform where over 300,000 creators give their biggest fans access to exclusive work and experiences. We offer creators a variety of ways to engage with their fans and build a lasting business including: paid memberships, free memberships, community chats, live video, and selling to fans directly with one-time purchases.
Ultimately our goal is simple: fund the creative class. And we're leaders in that space, with:
- $10 billion+ generated by creators since Patreon's inception
- 100 million+ free memberships for fans who may not be ready to pay just yet, and
- 25 million+ paid memberships on Patreon today.
We're continuing to invest heavily in building the best creator platform with the best team in the creator economy and are looking for a Director of Platform Engineering to support our mission.
This role is based in New York or San Francisco and open to those who are able to be in-office 2 days per week on a hybrid work model.
About the Team
We’re looking for a Director of Platform Engineering to lead the teams responsible for the core infrastructure and platform services that power Patreon’s product and ecosystem.
This role is ideal for a senior engineering leader who has:
- Scaled a consumer, UGC, or marketplace platform to millions of users
- Led infrastructure and/or platform teams supporting product engineering at scale
- Operated in environments with high availability, bursty traffic, and complex distributed systems
You’ll partner closely with Product, Security, Data, and Product Engineering leaders to ensure Patreon’s platform is reliable, scalable, and developer-friendly.
About the Role
- Lead and grow multiple Platform & Infrastructure Engineering teams, including backend services, APIs, developer platforms, reliability foundations and AI enablement
- Own the technical strategy and roadmap for scaling Patreon’s platform as usage, content, and integrations grow
- Drive platform initiatives across:
- Core backend services & APIs
- Service reliability, availability, and performance
- Platform scalability and capacity planning
- Internal developer tooling and productivity
- Partner with product engineering teams to enable faster feature delivery while maintaining platform stability
- Establish strong operational excellence (on-call, incident response, SLAs, SLOs)
- Hire, develop, and mentor senior engineering managers and technical leaders
- Communicate clearly with executives and cross-functional partners about risks, tradeoffs, and platform investments
About You
Required:
- 10+ years of engineering experience with 5+ years in engineering management
- Experience leading platform, infrastructure, or foundational backend teams
- Proven track record scaling a consumer product, UGC platform, or marketplace to millions of users
- Strong background in distributed systems, APIs, and service-oriented architectures
- Experience operating production systems with high availability and performance requirements
Preferred:
- Background in developer platforms, internal tooling, or platform abstractions
- Familiarity with cloud infrastructure (AWS/GCP), data pipelines, and event-driven systems
- Experience leading through major architectural transitions or scale inflection points
About Patreon
Patreon powers creators to do what they love and get paid by the people who love what they do. Our team is passionate about making this mission and our core values come to life every day in our work. Through this work, our Patronauts:
- Put Creators First | They’re the reason we’re here. When creators win, we win.
- Build with Craft | We sign our name to every deliverable, just like the creators we serve.
- Make it Happen | We don’t quit. We learn and deliver.
- Win Together | We grow as individuals. We win as a team.
We hire talented and passionate people from different backgrounds because workplace diversity and inclusion is critical to our ability to serve creators worldwide. If you’re excited about a role but your past experience doesn’t match with every bullet point outlined above, we strongly encourage you to apply anyway. If you’re a creator at heart, are energized by our mission, and share our company values, we’d love to hear from you.
Patreon is proud to be an equal opportunity employer. We provide employment opportunities without regard to age, race, color, ancestry, national origin, religion, disability, sex, gender identity or expression, sexual orientation, veteran status, or any other protected class. If you need a reasonable accommodation during the interview process, please let us know via email at accommodations@patreon.
Patreon offers a competitive benefits package including and not limited to salary, equity plans, healthcare, flexible time off, company holidays and recharge days, commuter benefits, lifestyle stipends, learning and development stipends, patronage, parental leave, and 401k plan with matching.
Patreon operates under a hybrid work model, where employees based in office locations are expected to come into the office two days per week, excluding sick time and paid leave. The goal of this policy is to be intentional about the in-person time we spend together to strengthen the feeling of community at Patreon. Candidates hired into remote-eligible roles are not expected to meet the same requirements.
At Patreon, we believe in fair and transparent pay. In compliance with New York and California pay transparency laws, we are sharing the expected salary range for this role.
The posted salary range is dependent on the location and the level. This range may encompass multiple levels within the role’s job family. The final offer will be based on candidate’s experience, skills, competencies, and geographic location, aligning with the appropriate job level within Patreon’s leveling framework. For remote employees located outside CA and NY, salary may vary based on location and local market conditions.
Patreon reserves the right to modify or update compensation and benefits at any time.
San Francisco; New York; For remote employees located outside CA and NY, salary may vary based on location and local market conditions
Location: Canada, Vancouver, BC
Workplace: OnSite
Here at Humanoid, we believe in a future where robots amplify human potential. That’s why we’ve set out on a mission to build the world’s most capable, commercially-scalable, and safe humanoid robots. We’re bringing that mission to life with HMND‑01 - our rapidly developed humanoid platform being deployed in real industrial environments - and we’re growing the team to take it even further.
ABOUT THE ROLE
We are looking for a Senior Prototype Engineer to drive the development, assembly, integration, and validation of advanced humanoid robotic systems, with a particular focus on robotic hands, arms, end-effectors, and complex electromechanical mechanisms.
This role combines deep hands-on engineering expertise with proven technical leadership and team management experience. You will take ownership of complex prototype development activities, guide engineering execution, and work closely with Mechanical, Electrical, Embedded Software, and Controls teams to transform innovative designs into reliable, high-performance robotic systems.
We are looking for someone who has successfully led and developed technical teams, established efficient prototyping processes, and delivered complex hardware projects in fast-moving engineering environments.
As our Vancouver robotics operations continue to grow, this position offers the opportunity to progress into a broader technical leadership role, helping shape our prototyping capabilities, engineering standards, and team development.
Your work will directly influence the technical quality, development speed, and scalability of Humanoid's next-generation robotic platforms.
WHAT MAKES THIS ROLE EXCITING
- Take ownership of complex humanoid robot prototyping, including dexterous hands, arms, actuators, and advanced electromechanical assemblies.
- Combine hands-on engineering with technical leadership, guiding complex prototype development from concept through validation.
- Apply your previous team management and mentoring experience to strengthen engineering execution and build effective prototyping practices.
- Solve challenging problems in robotic manipulation, precision actuation, sensor integration, and hardware-software interaction.
- Collaborate with a multidisciplinary robotics team spanning mechanical, electrical, embedded software, and controls.
- Play a key role in scaling Humanoid's prototyping capabilities, with potential to grow into a broader team leadership position.
WHAT YOU’LL DO
- Lead the development, assembly, integration, and commissioning of complex robotic prototypes, including hands, arms, end-effectors, actuators, sensors, and electromechanical subsystems.
- Take ownership of prototype development from initial build through calibration, testing, debugging, validation, and design iteration.
- Provide technical leadership and direction to engineers and technicians, helping establish priorities, resolve engineering challenges, and maintain execution quality.
- Apply previous team management experience to mentor technical team members, develop capabilities, and establish effective engineering workflows.
- Coordinate prototype development activities, ensuring clear technical priorities, efficient resource allocation, and alignment with engineering milestones.
- Drive advanced hardware troubleshooting, root-cause analysis, and failure investigation across mechanical, electrical, and embedded systems.
- Oversee the integration, calibration, and performance optimization of actuators, encoders, force/torque sensors, tactile sensors, and robotic control interfaces.
- Partner with Mechanical, Electrical, Embedded Software, and Controls teams to identify design limitations, integration risks, and opportunities for performance improvement.
- Lead and contribute to technical design reviews, providing practical engineering feedback on assembly, reliability, serviceability, and manufacturability.
- Establish and improve prototype assembly procedures, integration workflows, testing methodologies, and validation standards.
- Drive rapid prototyping and engineering iteration, ensuring timely resolution of technical blockers and efficient development cycles.
- Support system-level testing, performance characterization, reliability assessments, and prototype-to-production transitions.
- Develop and maintain technical documentation, including assembly instructions, test procedures, troubleshooting guides, SOPs, and engineering reports.
- Collaborate with engineering and program leadership to plan prototype builds, track technical progress, manage risks, and communicate development priorities.
- Work closely with teams across Vancouver, London, and Boston, supporting cross-site integration and technical collaboration.
- Promote a safe, organized, and efficient prototyping environment, maintaining appropriate tools, equipment, and engineering practices.
WE’RE LOOKING FOR
- 10+ years of professional experience in robotics prototyping, mechatronics, electromechanical integration, or advanced robotic hardware development.
- 5+ years of proven track record directing technician groups within a dynamic research and development setting.
- Demonstrated experience mentoring, developing, and coordinating technical team members, with the ability to establish priorities and drive engineering execution.
- Bachelor's or Master's degree in Robotics, Mechatronics, Mechanical Engineering, Electrical Engineering, or a related technical discipline, or equivalent practical engineering experience.
- Strong hands-on experience building, integrating, testing, and troubleshooting complex robotic or electromechanical systems.
- Proven track record of taking complex hardware prototypes from concept through integration, commissioning, validation, and delivery.
- Deep understanding of precision mechanical assemblies, actuators, motors, sensors, wiring, and embedded hardware interfaces.
- Experience with fine-pitch soldering, precision wiring, cable assemblies, connectors, and electrical integration.
- Strong knowledge of actuator commissioning, sensor calibration, motor control interfaces, and robotic system testing.
- Ability to interpret mechanical drawings, electrical schematics, wiring diagrams, and engineering documentation.
- Experience using laboratory and diagnostic equipment, including oscilloscopes, multimeters, power supplies, and precision measurement instruments.
- Demonstrated ability to lead complex troubleshooting, root-cause analysis, and engineering problem-solving activities.
- Experience establishing or improving prototype assembly, integration, testing, and validation processes.
- Strong project coordination skills, including technical planning, resource prioritization, risk management, and cross-functional execution.
- Excellent communication skills, with the ability to collaborate effectively with engineering teams, technical leadership, and program stakeholders.
- Ability to balance hands-on technical involvement with broader engineering leadership responsibilities in a fast-paced R&D environment.
BONUS QUALIFICATIONS
- Experience developing humanoid robots, dexterous robotic hands, robotic arms, or advanced manipulation systems.
- Previous experience building, scaling, or managing a robotics prototyping, integration, or hardware development team.
- Experience with multi-DoF robotic mechanisms, precision actuators, transmissions, and complex joint assemblies.
- Experience with tactile sensors, force/torque sensors, encoders, IMUs, and robotic sensing technologies.
- Knowledge of motor control systems, drive tuning, actuator performance analysis, EtherCAT, or CAN communication protocols.
- Experience with rapid prototyping and fabrication, including FDM/SLA 3D printing, CNC machining, and laser cutting.
- Familiarity with SolidWorks or similar CAD tools and the ability to perform mechanical design modifications.
- Experience using Python, ROS/ROS2, or other tools for robotic hardware testing, calibration, or automation.
- Experience developing automated test setups, hardware validation infrastructure, or data acquisition systems.
- Experience supporting hardware NPI, prototype-to-production transitions, or low-volume robotic manufacturing.
- Background in robotics, autonomous systems, aerospace, automotive, or advanced hardware startups.
WHAT WE OFFER
- Comprehensive extended health benefits starting on your first day, including fully paid medical and dental, virtual care, employee and family assistance, and worldwide emergency medical support.
- Meaningful time off to rest and recharge: 23 days of annual leave (accrued), 15 days of paid sick leave, and paid company holidays.
- Paid end-of-year shutdown: annual company-wide holiday closure spanning Christmas through New Year’s Day.
- Competitive equity: stock options with meaningful upside as we scale.
- Free daily catered lunch, snacks, and drinks in‑office.
- Collaboration with top‑tier engineers, researchers, and product experts in AI and robotics.
- Freedom to influence the product and own key initiatives.
Location: Hybrid/Remote - Europe (incl. UK); Estonia (remote); Switzerland (remote); Bulgaria (remote); Serbia (remote); Luxembourg (remote); France (remote); Paris (on-site); Norway (remote); England (remote); Croatia (remote); Sweden (remote); Scotland (remote); Slovenia (remote); Czechia (remote); Florence (on-site); Milan (on-site); Spain (remote); Romania (remote); Netherlands (hybrid); Ukraine (remote); Austria (remote); Denmark (remote); Ireland (remote); Slovakia (remote); EU (remote); Leuven (on-site); Belgium (remote); Northern Ireland (remote); Athens (on-site); Amsterdam (hybrid); Lithuania (remote); UK (remote); Hungary (remote); Finland (remote); Poland (remote); Wales (remote); Germany (remote); Eindhoven (hybrid); Moldova (remote); Albania (remote); Greece (remote); Zurich (on-site); Portugal (remote); Bristol (on-site); Italy (remote); Latvia (remote)
Workplace: Remote
About Us
Axelera AI https://www.axelera.ai is not your regular deep-tech company. We are creating the next-generation AI platform to support anyone who wants to help advancing humanity and improve the world around us.
In just five years, we have raised a total of $450 million and have built a world-class team of 250+ employees (including 60+ PhDs with more than 40,000 citations), both remotely from 21 different countries and with offices in Belgium, France, Switzerland, Italy, the UK, headquartered at the High Tech Campus in Eindhoven, Netherlands.
We have also launched our Metis™ AI Platform, which achieves a 3-5x increase in efficiency and performance, and have visibility into a strong business pipeline exceeding $100 million.
Our unwavering commitment to innovation has firmly established us as a global industry pioneer.
Are you up for the challenge?
Position Overview
As a Senior/Staff Software Engineer in the Model Enablement team, you will build our model enablement platform and decide how each model runs across the Metis or Europa AIPU, the host CPU and the host's accelerators. How this work is split and scheduled determines most of the end-to-end performance a customer sees in the Voyager SDK.
The role combines architecture and hands-on work across the model frontend, the graph compiler, the runtime and the application pipeline. During model bring-up, you cover the frontend and graph side, and a device-side engineer covers the backend.
Our team uses AI coding agents in daily work, and you will use them to build, test and verify your own work. We also welcome experienced field application engineers, technical leads and engineering managers who want to return to hands-on engineering. Experience in planning, delegating and reviewing work transfers well to working with AI agents.
Key responsibilities:
- Model enablement platform and agent harness: Build the platform that engineers and AI agents use to bring up models: reusable tools to partition, compile, run and profile; environments with access to real hardware; closed-loop runs that apply a change, measure it against the reference and repeat; and verification (differential testing, regression detection) integrated into CI.
- Model ingestion: Own how a PyTorch or ONNX model is captured, analysed and split across the AIPU's matrix and RISC-V vector units, the host CPU and the host's accelerators (GPU, NPU, video decoder), with placement tags that users can read and override. Build this with the graph compiler team so it works for every model.
- Heterogeneous execution: Extend that split to large models, cascades and multi-model pipelines, for example with expert offloading and caching across device and host memory. Agree the plan with the graph compiler team.
- End-to-end performance: With the application team (pipeline builder) and the runtime team, bring each model to its best end-to-end performance: overlap of host and device work, zero-copy buffers, batching, multi-core scheduling, and models with several inputs. Profile device time, host time and data movement, and confirm each speedup in the full pipeline.
- Critical models: Study the models that matter most to customers, on Metis today and on Europa, and turn each lesson into a platform improvement.
Qualifications:
Essential
- 8+ years of engineering experience in systems, compilers, runtimes or performance, including system-level design ownership.
- Hands-on experience with heterogeneous computing across CPU, GPU, DSP, NPU or FPGA, including reasoning about compute, memory bandwidth, latency and data movement.
- Solid fundamentals in computer architecture, operating systems and parallel programming.
- Familiarity with ML graph representations and compiler concepts: graph capture (torch.export, FX, ONNX), lowering, operator fusion, partitioning, memory planning.
- Strong C++ and Python.
- Daily use of AI coding agents on real engineering work, with the judgment to review and test their output.
Preferred
- Hands-on experience with MLIR or another compiler framework (TVM, XLA, TorchInductor, IREE).
- Experience with an inference runtime or execution engine (ONNX Runtime execution providers, a custom NPU or DSP runtime).
- Experience with GPU compute (CUDA, OpenCL, Vulkan) or SIMD optimization on Arm or x86.
- Experience with large-model execution techniques such as offloading, expert caching, speculative decoding, pipelining across devices.
- Experience building agent environments, evaluation harnesses or verification frameworks (differential testing, regression detection), or MCP-based tooling.
Location
We offer a flexible working arrangement, with options to:
- Work from one of our Axelera AI offices (Leuven in Belgium, Amsterdam and Eindhoven in the Netherlands, Zurich in Switzerland, Florence and Milan in Italy or Bristol in the United Kingdom) if you're already based in the vicinity.
- Work fully remotely from any European country (incl. the UK) you are already in.
- Relocate with us and work from Italy (Florence or Milan) or the Netherlands (Amsterdam or Eindhoven).
Kindly note that priority will be given to candidates who are [interested in being] based in Belgium or Italy.
What we offer
This is your chance to shape and be part of a dynamic, fast-growing, international organization. We offer an attractive compensation package, including a pension plan, extensive employee insurances and the option to get company shares.
An open culture that supports creativity and continual innovation is awaiting you. Collaborative ownership and freedom with responsibility is characteristic for the way we act and work as a team.
At Axelera AI, we wholeheartedly embrace equal opportunity and hold diversity in the highest regard. Our steadfast commitment is to cultivate a warm and inclusive environment that empowers and celebrates every member of our team. We welcome applicants from all backgrounds to join us in shaping the future of AI.
Axelera AI may at times work with external parties (such as recruiters, headhunters, or executive search firms) to support both publicly posted and confidential searches. Our hiring process never involves any payment from candidates, and we will never require you to use a particular paid service to be considered for a role. If you have been approached by someone on our behalf and want to confirm it’s legitimate, please contact us directly at careers@axelera.ai.
Location: New York, New York, United States
About Turing
Turing’s mission is to accelerate superintelligence to drive real economic progress. Headquartered in San Francisco, Turing works with frontier AI labs to generate high-quality datasets, reinforcement learning environments, and frontier research benchmarks that improve model capabilities in software engineering, enterprise knowledge work, and advanced STEM reasoning. In software engineering, Turing is the largest and longest-running data provider in the category. Turing also works with Fortune 500 enterprises across financial services, life sciences, healthcare, retail, automotive, and CPG to build and deploy end-to-end agentic AI systems inside mission-critical workflows. By operating on both sides, Turing closes the loop between frontier research and enterprise deployment, turning real-world deployment signals into better data, evaluations, and more capable models. Learn more at
www.turing.com
.
Role:
Enterprise AI - Platform Engineer
Location:
New York City, Madison Square Park area
Reports to:
Head of Engineering
Experience Level:
6+ years
Work Model:
Hybrid, with regular in-office presence
Why Join Enterprise AI?
Enterprise AI is building the platform that enables reliable deployment of AI systems across enterprise environments.
As a Platform Engineer, you will build and operate the core of Turing’s Enterprise AI platform, including platform services, the model gateway, multi-tenant infrastructure, CI/CD, infrastructure as code, and observability.
The central challenge is creating repeatable deployments across environments Turing may not control, including client cloud accounts, dedicated instances, and sovereign installations.
As a Platform Engineer, You Will
Build Core Platform Services
Design, build, and operate the services that support the Enterprise AI platform, including platform infrastructure and the model gateway.
Enable Enterprise Deployment
Create reliable deployment systems for client cloud accounts, dedicated instances, and sovereign environments.
Own Infrastructure and Delivery Systems
Build and maintain CI/CD, infrastructure as code, and deployment tooling across environments using Kubernetes, Helm, Terraform or Pulumi, and GitOps practices.
Build for Reliability
Design systems that support secure multi-tenant operation, reliable service ownership, and clear operational accountability.
Establish Observability
Build observability across the platform using service-level objectives, structured logging, metrics, tracing, and alerting.
Build FinOps Visibility
Develop telemetry that tracks model and compute costs by tenant.
The Anti-Sell
Hands-on software engineering:
This is not a cloud-operations or ticket-queue DevOps role. You must be able to write production software and contribute deeply to platform services.
Operational ownership:
You will participate in on-call operations and incident response and help build the processes required to operate a production platform.
Deployment complexity:
The platform must operate across client cloud accounts, dedicated instances, and sovereign environments.
Team accountability:
The platform must be observable, operable, and maintainable by the team rather than dependent on one individual.
Our Enterprise AI Platform Engineers Typically Have
Production platform engineering experience:
Six or more years of platform or infrastructure engineering experience supporting production SaaS.
Customer-controlled deployment experience:
Experience shipping a product that customers run in their own environment, including in a VPC, a self-hosted environment, or an air-gapped environment.
Strong software engineering depth:
Deep expertise in at least one backend language, such as Go, Ruby, Python, or TypeScript, with the ability to read and work across the others.
Cloud and infrastructure expertise:
Deep experience in at least one cloud and comfort working in a second, including private networking, KMS, IAM, Kubernetes, Helm, Terraform or Pulumi, and GitOps.
Reliability and observability ownership:
Experience carrying a pager, running incidents, and working with tools such as OpenTelemetry, Prometheus, or Datadog.
Data and platform systems experience:
Familiarity with Postgres, object storage, queues, and vector stores.
Candidates whose experience is limited to cloud operations or ticket-queue DevOps without software engineering, or who have never operated multi-tenant systems, will not be a fit for this role.
Top-Tier Candidates Will Have
Experience with model serving and model gateways, including vLLM, LiteLLM, or Triton.
Experience implementing SOC 2 controls in CI.
Experience deploying and operating AI systems in regulated or highly controlled environments.
Values
We are client first:
We put our clients at the center of everything we do, because their success is the ultimate measure of our value.
We work at Start-Up Speed:
We move fast, stay agile and favor action because momentum is the foundation of perfection
We are AI forward:
We help our clients build the future of Al and implement it in our own roles and workflow to amplify productivity.
Advantages of joining Turing
Work at the frontier of AI
, helping the world’s leading AI labs improve their most advanced models by building expert datasets, RL environments, and first-of-a-kind benchmarks.
Contribute to leading-edge AI research
and showcase your work at top conferences such as ICLR, ICML, and NeurIPS.
Bring frontier AI innovation to the enterprise
, applying lessons learned from leading AI labs to solve real-world business challenges.
Collaborate with and learn from exceptional colleagues
with deep AI experience from Google, Meta, Amazon, and other leading technology companies.
Move at the pace of AI innovation
, with the speed, ownership, and impact of a startup.
Turing is proud to be an equal opportunity employer. We do not discriminate on the basis of race, religion, color, national origin, gender, gender identity, sexual orientation, age, marital status, disability, protected veteran status, or any other legally protected characteristics. At Turing we are dedicated to building a diverse, inclusive and authentic workplace and celebrate authenticity, so if you’re excited about this role but your past experience doesn’t align perfectly with every qualification in the job description, we encourage you to apply anyways. You may be just the right candidate for this or other roles.
For applicants from the European Union, please review
Turing's GDPR notice here
.
Location: Limassol, Cyprus
Workplace: hybrid
We are looking for an Application Security Engineer in the AppSec team.
As a Senior Application Security Engineer at inDrive, you will own whole areas of our AppSec program, from security requirements and design reviews to controls that work in production. You will work with product, platform and infrastructure teams on new architecture, AI systems and the security of our apps and services. The role fits a hands-on engineer who reviews code and architecture, leads work across several teams and makes security decisions based on data.
Key Responsibilities
Own one or more areas of the AppSec program end to end, including the plan, the risks and the metrics.
Run security design reviews and threat modeling of new features, services and AI systems, and defend security requirements at the architecture committee.
Assess the security of web, mobile and API applications and find variants of confirmed vulnerabilities across other services.
Coordinate external security assessments and bug bounty findings, from triage and prioritisation with teams to fix verification.
Design secure authentication and authorization between users, services and internal tools together with platform teams.
Choose, pilot and tune security tools, and keep only those that pay off in real findings.
Turn incidents and assessment results into lasting controls, policies and secure defaults for developers.
Consult development teams on complex security issues and take shifts in AppSec duty for Critical and High incidents.
Skills, Knowledge and Expertise
Higher education in IT, preferably in information security.
Knowledge of mobile application security on Android and iOS, client-server trust, app integrity and protection against bots and account takeover.
Deep knowledge of web and API security, including authentication and authorization flaws, SSRF, injections and business logic issues, and the ability to find variants of a vulnerability class across many services.
Strong understanding of identity and access, OAuth 2.0, OIDC, JWT, signing key rotation, mTLS, service-to-service authentication and SSO.
Understanding of security risks of AI systems and LLM agents, such as prompt injection, excessive tool permissions and agent access to sensitive data, and practical use of AI in your own security work.
Practical understanding of cloud-native infrastructure, Kubernetes, containers, API gateways and AWS.
Experience in threat modeling and security design review of new features and architecture.
Understanding of vulnerability management and risk, including severity, SLA, compensating controls and risk acceptance.
Ability to read and review code in at least one language, preferably Golang.
Track record of leading projects with two or more teams from a requirement to a verified fix, and clear written English.
Experience with secrets and key management, rotation and response to credential leaks.
Nice to have
Knowledge of software supply chain security, dependencies, CI/CD pipelines and source control platform hardening.
Experience tuning SAST, DAST and SCA tools for precision and judging a tool by noise and cost per real finding.
Why join us
Help us challenge injustice by creating fair choices for millions of people across 47 countries.
Develop your professional skills with access to mentoring, career consulting, and learning programs.
Collaborate with teams around the world and gain international experience through our Global Talent Exchange Program.
Engage in company-wide challenges, awards, sports activities, employee-led social impact and volunteering projects.
Work alongside people who take initiative, speak openly, and challenge themselves to grow.
Improve your language skills through co-financed courses and internal speaking clubs.
Annual salary from 70 700 EUR gross.
Final benefits may vary depending on the location.
Location: Islamabad, Pakistan
Workplace: hybrid
Our AI Customer Support and Safety team is working on many areas including Computer Vision, NLP, RL etc, bringing business value to our products and improving the lives of our customers. Now we are aiming to strengthen several streams and looking for an experienced middle
Machine Learning Engineer
, who will help us in our journey. The primary focus for this position will be automation of the driver verification process, for which experience in
computer vision
is essential.
Key Responsibilities
Develop, deploy, and maintain machine learning models and systems
Conduct in-depth data analysis, preparation, and processing
Contribute to the design of ML systems, focusing on data-related aspects like annotation and processing
Follow established security guidelines and identify potential risks
Collaborate with peers to review and ensure designs are effective, flexible, and reusable
Solve issues and provide feedback for engineering design and delivery processes
Ensure that features are deployed according to requirements and operate without unintended side effects
Create and maintain comprehensive documentation for models and processes
Monitor and support deployed solutions to ensure they meet performance expectations
Actively seek out information and internal solutions to ensure reusability and adoption of common technologies
Our expectations
3+ years of experience in the Field. Previous software engineering experience is preferrable
Strong proficiency in Python and its common data processing frameworks (e.g., for streaming, batch, and asynchronous data).
Strong foundational knowledge in classical machine learning, deep learning, and advanced mathematics.
Familiarity with MLOps principles and experience using MLOps tools for managing the ML model lifecycle.
Experience with backend systems and infrastructure, and a willingness to learn new technologies (like Golang) for integration purposes.
The ability to design solutions using common design patterns and various design tools.
Experience in experiment design and the ability to validate the credibility of information from various sources.
A solid understanding of the business value of delivered features.
Strong problem-solving skills, with an emphasis on using technical facts and reasoning.
Excellent communication and collaboration skills, including active listening and the ability to build consensus.
A strong drive for continuous self-development and a collaborative mindset for contributing to communities.
Ability to balance quality and speed depending on the case, and justify your decision
Ability to explain the background and consequences of your technical decisions to non-technical stakeholders, including product and business managers
English communication skills
Why join us
Help us challenge injustice by creating fair choices for millions of people across 47 countries.
Develop your professional skills with access to mentoring, career consulting, and learning programs.
Collaborate with teams around the world and gain international experience through our Global Talent Exchange Program.
Engage in company-wide challenges, awards, sports activities, employee-led social impact and volunteering projects.
Work alongside people who take initiative, speak openly, and challenge themselves to grow.
Improve your language skills through co-financed courses and internal speaking clubs.
Final benefits may vary depending on the location.
Location: Cairo, Egypt
Workplace: hybrid
Our AI Customer Support and Safety team is working on many areas including Computer Vision, NLP, RL etc, bringing business value to our products and improving the lives of our customers. Now we are aiming to strengthen several streams and looking for an experienced middle
Machine Learning Engineer
, who will help us in our journey. The primary focus for this position will be automation of the driver verification process, for which experience in
computer vision
is essential.
Key Responsibilities
Develop, deploy, and maintain machine learning models and systems
Conduct in-depth data analysis, preparation, and processing
Contribute to the design of ML systems, focusing on data-related aspects like annotation and processing
Follow established security guidelines and identify potential risks
Collaborate with peers to review and ensure designs are effective, flexible, and reusable
Solve issues and provide feedback for engineering design and delivery processes
Ensure that features are deployed according to requirements and operate without unintended side effects
Create and maintain comprehensive documentation for models and processes
Monitor and support deployed solutions to ensure they meet performance expectations
Actively seek out information and internal solutions to ensure reusability and adoption of common technologies
Our expectations
3+ years of experience in the Field. Previous software engineering experience is preferrable
Strong proficiency in Python and its common data processing frameworks (e.g., for streaming, batch, and asynchronous data).
Strong foundational knowledge in classical machine learning, deep learning, and advanced mathematics.
Familiarity with MLOps principles and experience using MLOps tools for managing the ML model lifecycle.
Experience with backend systems and infrastructure, and a willingness to learn new technologies (like Golang) for integration purposes.
The ability to design solutions using common design patterns and various design tools.
Experience in experiment design and the ability to validate the credibility of information from various sources.
A solid understanding of the business value of delivered features.
Strong problem-solving skills, with an emphasis on using technical facts and reasoning.
Excellent communication and collaboration skills, including active listening and the ability to build consensus.
A strong drive for continuous self-development and a collaborative mindset for contributing to communities.
Ability to balance quality and speed depending on the case, and justify your decision
Ability to explain the background and consequences of your technical decisions to non-technical stakeholders, including product and business managers
English communication skills
Why join us
Help us challenge injustice by creating fair choices for millions of people across 47 countries.
Develop your professional skills with access to mentoring, career consulting, and learning programs.
Collaborate with teams around the world and gain international experience through our Global Talent Exchange Program.
Engage in company-wide challenges, awards, sports activities, employee-led social impact and volunteering projects.
Work alongside people who take initiative, speak openly, and challenge themselves to grow.
Improve your language skills through co-financed courses and internal speaking clubs.
Final benefits may vary depending on the location.
Location: Cairo, Egypt
Workplace: hybrid
inDrive runs many ML models across 48 countries, and the platform under them is growing fast. Teams ship to Databricks, SageMaker and self-managed GPU inference, and LLM workloads are moving into production.
We are hiring a Backend Engineer to build and run the services and tooling that ML teams depend on. You will write production-grade Go and Python, own your services from design to on-call, and learn the MLOps stack (Terraform, Kubernetes, Databricks, SageMaker) on the job. You will work with data scientists, ML engineers, and software and data engineers.
Key Responsibilities
Design, build and run backend services and internal tooling for the ML platform, in Go and Python, and own them in production: delivery, monitoring, on-call.
Drive technical research and design for new platform features. Weigh trade-offs and write them down.
Build and extend the deployment tooling and SDKs ML teams use to ship models, including serving, autoscaling and data capture.
Integrate ML services with the wider backend: sync (HTTP, gRPC) and async (Kafka, RabbitMQ) communication, built for reliability under high load.
Support the full ML delivery cycle with data scientists and ML engineers: CI/CD for models and pipelines, monitoring, and model performance metrics.
Build observability end to end: metrics, logs, dashboards, tracing, alerting and runbooks.
Write infrastructure as code with Terraform and Terragrunt, and ship it through reviewed pull requests.
Take part in code and design reviews and help raise the engineering bar. Mentor engineers and hold AI-generated changes to the same review standard as human ones.
Skills, Knowledge and Expertise
3+ years of commercial backend engineering, including high-load systems.
Strong Go: you know and have used its advanced features in production.
Solid Python for data processing (batch, streaming and async) and for service code.
Strong SQL: schema design, indexes, query optimisation, and scaling approaches such as replication and partitioning. Experience with Redis or similar key-value stores.
Experience with sync and async service communication (HTTP, gRPC, Kafka or RabbitMQ), with a focus on reliability.
Docker, CI/CD pipelines, and working Kubernetes knowledge. Experience with AWS/GCP core services (compute, storage, networking, IAM).
Understanding of the ML lifecycle: data preparation, model performance metrics, non-functional requirements, and concept drift in production. Familiarity with scikit-learn, PyTorch/TensorFlow.
Experience designing and running observability: metrics, logging, dashboards, tracing (Prometheus, Grafana, ELK or similar).
Git workflows and code review, comfortable Linux command line, and networking basics (TCP/IP, DNS, VPC, load balancers).
Ability to explain a trade-off clearly to a data scientist, a product owner and an on-call engineer.
Nice to have:
Hands-on experience with MLOps tooling: Databricks (Unity Catalog, Asset Bundles), SageMaker, Kubeflow, MLflow or Airflow.
GPU or LLM inference experience (vLLM, Triton, Langfuse).
Experience using AI coding agents with full review of generated changes.
Experience in emerging markets, or building where there is no playbook yet.
Experience with Infrastructure-as-Code (IaC) tools
Why join us
Real scale and real constraints: models serving a global fleet, GPU budgets that matter, LLM workloads reaching production.
A strong backend engineer can grow into the MLOps domain here. We will teach you the platform side; you bring the engineering craft.
Help us challenge injustice by creating fair choices for millions of people across 48 countries.
Develop your professional skills with access to mentoring, career consulting, and learning programs.
Collaborate with teams around the world and gain international experience through our Global Talent Exchange Program.
Engage in company-wide challenges, awards, sports activities, employee-led social impact and volunteering projects.
Work alongside people who take initiative, speak openly, and challenge themselves to grow.
Improve your language skills through co-financed courses and internal speaking clubs.
Final benefits may vary depending on the location.
Location: Islamabad, Pakistan
Workplace: hybrid
inDrive runs many ML models across 48 countries, and the platform under them is growing fast. Teams ship to Databricks, SageMaker and self-managed GPU inference, and LLM workloads are moving into production.
We are hiring a Backend Engineer to build and run the services and tooling that ML teams depend on. You will write production-grade Go and Python, own your services from design to on-call, and learn the MLOps stack (Terraform, Kubernetes, Databricks, SageMaker) on the job. You will work with data scientists, ML engineers, and software and data engineers.
Key Responsibilities
Design, build and run backend services and internal tooling for the ML platform, in Go and Python, and own them in production: delivery, monitoring, on-call.
Drive technical research and design for new platform features. Weigh trade-offs and write them down.
Build and extend the deployment tooling and SDKs ML teams use to ship models, including serving, autoscaling and data capture.
Integrate ML services with the wider backend: sync (HTTP, gRPC) and async (Kafka, RabbitMQ) communication, built for reliability under high load.
Support the full ML delivery cycle with data scientists and ML engineers: CI/CD for models and pipelines, monitoring, and model performance metrics.
Build observability end to end: metrics, logs, dashboards, tracing, alerting and runbooks.
Write infrastructure as code with Terraform and Terragrunt, and ship it through reviewed pull requests.
Take part in code and design reviews and help raise the engineering bar. Mentor engineers and hold AI-generated changes to the same review standard as human ones.
Skills, Knowledge and Expertise
3+ years of commercial backend engineering, including high-load systems.
Strong Go: you know and have used its advanced features in production.
Solid Python for data processing (batch, streaming and async) and for service code.
Strong SQL: schema design, indexes, query optimisation, and scaling approaches such as replication and partitioning. Experience with Redis or similar key-value stores.
Experience with sync and async service communication (HTTP, gRPC, Kafka or RabbitMQ), with a focus on reliability.
Docker, CI/CD pipelines, and working Kubernetes knowledge. Experience with AWS/GCP core services (compute, storage, networking, IAM).
Understanding of the ML lifecycle: data preparation, model performance metrics, non-functional requirements, and concept drift in production. Familiarity with scikit-learn, PyTorch/TensorFlow.
Experience designing and running observability: metrics, logging, dashboards, tracing (Prometheus, Grafana, ELK or similar).
Git workflows and code review, comfortable Linux command line, and networking basics (TCP/IP, DNS, VPC, load balancers).
Ability to explain a trade-off clearly to a data scientist, a product owner and an on-call engineer.
Nice to have:
Hands-on experience with MLOps tooling: Databricks (Unity Catalog, Asset Bundles), SageMaker, Kubeflow, MLflow or Airflow.
GPU or LLM inference experience (vLLM, Triton, Langfuse).
Experience using AI coding agents with full review of generated changes.
Experience in emerging markets, or building where there is no playbook yet.
Experience with Infrastructure-as-Code (IaC) tools
Why join us
Real scale and real constraints: models serving a global fleet, GPU budgets that matter, LLM workloads reaching production.
A strong backend engineer can grow into the MLOps domain here. We will teach you the platform side; you bring the engineering craft.
Help us challenge injustice by creating fair choices for millions of people across 48 countries.
Develop your professional skills with access to mentoring, career consulting, and learning programs.
Collaborate with teams around the world and gain international experience through our Global Talent Exchange Program.
Engage in company-wide challenges, awards, sports activities, employee-led social impact and volunteering projects.
Work alongside people who take initiative, speak openly, and challenge themselves to grow.
Improve your language skills through co-financed courses and internal speaking clubs.
Final benefits may vary depending on the location.
Location: Almaty, Kazakhstan
Workplace: hybrid
inDrive runs many ML models across 48 countries, and the platform under them is growing fast. Teams ship to Databricks, SageMaker and self-managed GPU inference, and LLM workloads are moving into production.
We are hiring a Backend Engineer to build and run the services and tooling that ML teams depend on. You will write production-grade Go and Python, own your services from design to on-call, and learn the MLOps stack (Terraform, Kubernetes, Databricks, SageMaker) on the job. You will work with data scientists, ML engineers, and software and data engineers.
Key Responsibilities
Design, build and run backend services and internal tooling for the ML platform, in Go and Python, and own them in production: delivery, monitoring, on-call.
Drive technical research and design for new platform features. Weigh trade-offs and write them down.
Build and extend the deployment tooling and SDKs ML teams use to ship models, including serving, autoscaling and data capture.
Integrate ML services with the wider backend: sync (HTTP, gRPC) and async (Kafka, RabbitMQ) communication, built for reliability under high load.
Support the full ML delivery cycle with data scientists and ML engineers: CI/CD for models and pipelines, monitoring, and model performance metrics.
Build observability end to end: metrics, logs, dashboards, tracing, alerting and runbooks.
Write infrastructure as code with Terraform and Terragrunt, and ship it through reviewed pull requests.
Take part in code and design reviews and help raise the engineering bar. Mentor engineers and hold AI-generated changes to the same review standard as human ones.
Skills, Knowledge and Expertise
3+ years of commercial backend engineering, including high-load systems.
Strong Go: you know and have used its advanced features in production.
Solid Python for data processing (batch, streaming and async) and for service code.
Strong SQL: schema design, indexes, query optimisation, and scaling approaches such as replication and partitioning. Experience with Redis or similar key-value stores.
Experience with sync and async service communication (HTTP, gRPC, Kafka or RabbitMQ), with a focus on reliability.
Docker, CI/CD pipelines, and working Kubernetes knowledge. Experience with AWS/GCP core services (compute, storage, networking, IAM).
Understanding of the ML lifecycle: data preparation, model performance metrics, non-functional requirements, and concept drift in production. Familiarity with scikit-learn, PyTorch/TensorFlow.
Experience designing and running observability: metrics, logging, dashboards, tracing (Prometheus, Grafana, ELK or similar).
Git workflows and code review, comfortable Linux command line, and networking basics (TCP/IP, DNS, VPC, load balancers).
Ability to explain a trade-off clearly to a data scientist, a product owner and an on-call engineer.
Nice to have:
Hands-on experience with MLOps tooling: Databricks (Unity Catalog, Asset Bundles), SageMaker, Kubeflow, MLflow or Airflow.
GPU or LLM inference experience (vLLM, Triton, Langfuse).
Experience using AI coding agents with full review of generated changes.
Experience in emerging markets, or building where there is no playbook yet.
Experience with Infrastructure-as-Code (IaC) tools
Why join us
Real scale and real constraints: models serving a global fleet, GPU budgets that matter, LLM workloads reaching production.
A strong backend engineer can grow into the MLOps domain here. We will teach you the platform side; you bring the engineering craft.
Help us challenge injustice by creating fair choices for millions of people across 48 countries.
Develop your professional skills with access to mentoring, career consulting, and learning programs.
Collaborate with teams around the world and gain international experience through our Global Talent Exchange Program.
Engage in company-wide challenges, awards, sports activities, employee-led social impact and volunteering projects.
Work alongside people who take initiative, speak openly, and challenge themselves to grow.
Improve your language skills through co-financed courses and internal speaking clubs.
Final benefits may vary depending on the location.
Location: Limassol, Cyprus
Workplace: hybrid
inDrive runs many ML models across 48 countries, and the platform under them is growing fast. Teams ship to Databricks, SageMaker and self-managed GPU inference, and LLM workloads are moving into production.
We are hiring a Backend Engineer to build and run the services and tooling that ML teams depend on. You will write production-grade Go and Python, own your services from design to on-call, and learn the MLOps stack (Terraform, Kubernetes, Databricks, SageMaker) on the job. You will work with data scientists, ML engineers, and software and data engineers.
Key Responsibilities
Design, build and run backend services and internal tooling for the ML platform, in Go and Python, and own them in production: delivery, monitoring, on-call.
Drive technical research and design for new platform features. Weigh trade-offs and write them down.
Build and extend the deployment tooling and SDKs ML teams use to ship models, including serving, autoscaling and data capture.
Integrate ML services with the wider backend: sync (HTTP, gRPC) and async (Kafka, RabbitMQ) communication, built for reliability under high load.
Support the full ML delivery cycle with data scientists and ML engineers: CI/CD for models and pipelines, monitoring, and model performance metrics.
Build observability end to end: metrics, logs, dashboards, tracing, alerting and runbooks.
Write infrastructure as code with Terraform and Terragrunt, and ship it through reviewed pull requests.
Take part in code and design reviews and help raise the engineering bar. Mentor engineers and hold AI-generated changes to the same review standard as human ones.
Skills, Knowledge and Expertise
3+ years of commercial backend engineering, including high-load systems.
Strong Go: you know and have used its advanced features in production.
Solid Python for data processing (batch, streaming and async) and for service code.
Strong SQL: schema design, indexes, query optimisation, and scaling approaches such as replication and partitioning. Experience with Redis or similar key-value stores.
Experience with sync and async service communication (HTTP, gRPC, Kafka or RabbitMQ), with a focus on reliability.
Docker, CI/CD pipelines, and working Kubernetes knowledge. Experience with AWS/GCP core services (compute, storage, networking, IAM).
Understanding of the ML lifecycle: data preparation, model performance metrics, non-functional requirements, and concept drift in production. Familiarity with scikit-learn, PyTorch/TensorFlow.
Experience designing and running observability: metrics, logging, dashboards, tracing (Prometheus, Grafana, ELK or similar).
Git workflows and code review, comfortable Linux command line, and networking basics (TCP/IP, DNS, VPC, load balancers).
Ability to explain a trade-off clearly to a data scientist, a product owner and an on-call engineer.
Nice to have:
Hands-on experience with MLOps tooling: Databricks (Unity Catalog, Asset Bundles), SageMaker, Kubeflow, MLflow or Airflow.
GPU or LLM inference experience (vLLM, Triton, Langfuse).
Experience using AI coding agents with full review of generated changes.
Experience in emerging markets, or building where there is no playbook yet.
Experience with Infrastructure-as-Code (IaC) tools
Why join us
Help us challenge injustice by creating fair choices for millions of people across in 47 countries.
Develop your professional skills with access to mentoring, career consulting, and learning programs.
Collaborate with teams around the world and gain international experience through our Global Talent Exchange Program.
Engage in company-wide challenges, awards, sports activities, employee-led social impact and volunteering projects.
Work alongside people who take initiative, speak openly, and challenge themselves to grow.
Improve your language skills through co-financed courses and internal speaking clubs.
Annual salary starting from 59 200 EUR gross
Final benefits may vary depending on the location.
Location: Abu Dhabi, United Arab Emirates
Workplace: hybrid
Autonomous vehicles are coming to the markets in which inDrive serves, but the AV industry has a blind spot: it has almost no data from the cities where we operate. inDrive runs one of the world's largest ride-hailing fleets, concentrated in exactly the emerging markets global AV players have yet to reach. We are building the team that turns that fleet into an AV grade data platform: large scale multi sensor collection from real vehicles on real trips, evaluation of driving models against human drivers, automated and human in the loop causal annotation, and camera first 3D scene reconstruction for simulation.
We are hiring the founding leader of this workstream. This is a rare true zero to one: you will hire the team, stand up the vehicles and the data platform, set the technical bar, and own delivery end to end.
Key Responsibilities
Own the AV data workstream end to end.
Roadmap, execution, quality, and outcomes, from vehicle data capture through processing, annotation, and delivery of AV-grade datasets
Build the team from zero.
Hire and lead a multi disciplinary team covering large scale data engineering, search and serving, ML engineering across divergence detection, edge inference, and VLM based auto labeling, 3D reconstruction research, and data annotation operations. Own the hiring bar and the team's growth
Management
: Manage ML engineers' charter across the cluster
Set technical direction across disciplines.
Provide design authority for the full pipeline, including onboard capture, cloud ingestion and processing at fleet scale, semantic and causal labeling, and neural scene reconstruction, and make the key technical decisions on evidence
Partner with leaders across the organization.
Work closely with the leaders of data platform, ML ops, geo and mapping, and inDrive's wider autonomous vehicle initiatives to align roadmaps, share infrastructure, and unblock delivery
Own external partnerships.
Drive the technical relationships this work depends on, including the NVIDIA AV ecosystem and prospective AV partners consuming our data
Balance research and delivery.
Several of the core questions here are open research. You frame milestones as decision gates, redirect work based on evidence, and keep the team shipping measurable results on a schedule
Skills, Knowledge and Expertise
10+ years of engineering experience in applied ML, autonomous systems, or large-scale data and ML platforms, including 3+ years in the autonomous vehicle industry or closely adjacent autonomy domains (AV data, simulation, or evaluation platforms, ADAS, robotics perception, HD mapping), and 4+ years managing engineering teams, with a track record of building teams from scratch and delivering from zero to one
Strong command of the modern AV and foundation model landscape: vision-language models, multi-modal perception, 3D reconstruction and neural rendering, and the NVIDIA AV ecosystem. Deep enough to set direction, review designs, and make evidence-based technical calls
Genuine engineering leadership range: you have successfully led data engineering, platform, or infrastructure delivery, not only ML research, and can evaluate and unblock work across those disciplines
Proven ability to take large-scale ML systems from conception to production: data pipelines, training and evaluation infrastructure, and models running under real-world constraints
Strong external presence: experience owning technical partnerships, vendor and licensing relationships, or customer-facing technical engagements at a senior level
Deep fluency in the modern ML stack (PyTorch or equivalent, large-scale training and inference, Python and Linux) and in cloud data platforms at multi-terabyte scale
Bachelor's degree in Computer Science, Engineering, or a related field
Nice to have:
AV industry experience specifically in offline evaluation, data mining, annotation platforms, or simulation, as distinct from on-vehicle stack development
Advanced degree (MS or PhD) in ML, robotics, or computer vision
Experience operating in emerging markets, and comfort building where the playbook does not exist yet
Why join us
Help us challenge injustice by creating fair choices for millions of people across 47 countries
Develop your professional skills with access to mentoring, career consulting, and learning programs.
Collaborate with teams around the world and gain international experience through our Global Talent Exchange Program
Engage in company-wide challenges, awards, sports activities, employee-led social impact and volunteering projects
Work alongside people who take initiative, speak openly, and challenge themselves to grow
Improve your language skills through co-financed courses and internal speaking clubs
Final benefits may vary depending on the location.
Location: Almaty, Kazakhstan
Workplace: hybrid
Our AI Customer Support and Safety team is working on many areas including Computer Vision, NLP, RL etc, bringing business value to our products and improving the lives of our customers. Now we are aiming to strengthen several streams and looking for an experienced middle
Machine Learning Engineer
, who will help us in our journey. The primary focus for this position will be automation of the driver verification process, for which experience in
computer vision
is essential.
Key Responsibilities
Develop, deploy, and maintain machine learning models and systems
Conduct in-depth data analysis, preparation, and processing
Contribute to the design of ML systems, focusing on data-related aspects like annotation and processing
Follow established security guidelines and identify potential risks
Collaborate with peers to review and ensure designs are effective, flexible, and reusable
Solve issues and provide feedback for engineering design and delivery processes
Ensure that features are deployed according to requirements and operate without unintended side effects
Create and maintain comprehensive documentation for models and processes
Monitor and support deployed solutions to ensure they meet performance expectations
Actively seek out information and internal solutions to ensure reusability and adoption of common technologies
Our expectations
3+ years of experience in the Field. Previous software engineering experience is preferrable
Strong proficiency in Python and its common data processing frameworks (e.g., for streaming, batch, and asynchronous data).
Strong foundational knowledge in classical machine learning, deep learning, and advanced mathematics.
Familiarity with MLOps principles and experience using MLOps tools for managing the ML model lifecycle.
Experience with backend systems and infrastructure, and a willingness to learn new technologies (like Golang) for integration purposes.
The ability to design solutions using common design patterns and various design tools.
Experience in experiment design and the ability to validate the credibility of information from various sources.
A solid understanding of the business value of delivered features.
Strong problem-solving skills, with an emphasis on using technical facts and reasoning.
Excellent communication and collaboration skills, including active listening and the ability to build consensus.
A strong drive for continuous self-development and a collaborative mindset for contributing to communities.
Ability to balance quality and speed depending on the case, and justify your decision
Ability to explain the background and consequences of your technical decisions to non-technical stakeholders, including product and business managers
English communication skills
Why join us
Help us challenge injustice by creating fair choices for millions of people across 47 countries.
Develop your professional skills with access to mentoring, career consulting, and learning programs.
Collaborate with teams around the world and gain international experience through our Global Talent Exchange Program.
Engage in company-wide challenges, awards, sports activities, employee-led social impact and volunteering projects.
Work alongside people who take initiative, speak openly, and challenge themselves to grow.
Improve your language skills through co-financed courses and internal speaking clubs.
Final benefits may vary depending on the location.
Location: Limassol, Cyprus
Workplace: hybrid
We are looking for an
Engineering Manager
to lead and develop a team of infrastructure engineers responsible for critical production systems. This role combines
people management with strong technical expertise
, requiring a solid infrastructure background and deep knowledge of
AWS, Kubernetes, distributed systems, networking, and infrastructure automation
.
Key Responsibilities
Lead, manage, and develop a team of infrastructure engineers responsible for critical production systems
Own team execution, delivery, and day-to-day prioritization in alignment with infrastructure strategy and architectural direction
Run the shared goals and collaborate with the Infrastructure Security, and other platform teams
Translate infrastructure priorities into clear team plans, milestones for both infrastructure and product teams
Work with Staff and Senior engineers to ensure technical solutions are delivered effectively and aligned with established architectural principles
Ensure the reliability, availability, and operational readiness of systems owned by the team
Own the team’s operational practices, incident response, postmortems, and completion of follow-up actions
Set clear expectations, run regular 1:1s, provide actionable feedback, and support engineers through performance reviews and Individual Development Plans
Skills, Knowledge and Expertise
7+ years of experience in software, infrastructure, SRE, or platform engineering, including 2+ years in an engineering management role within a Platform cluster
Proven experience leading and developing engineering teams responsible for production infrastructure or platform systems
Strong infrastructure engineering background, with sufficient technical depth to review architecture, challenge decisions, and work effectively with Staff and Senior engineers
Strong understanding of cloud infrastructure, especially AWS, Kubernetes, distributed systems, networking, and infrastructure automation
Experience operating highly available, business-critical systems, including incident management, on-call, postmortems, capacity planning, and operational excellence
Proven ability to set priorities, build execution plans, and deliver complex engineering initiatives while balancing reliability, technical debt, operational work, and long-term investments
Ability to delegate technical ownership and enable senior engineers to lead major technical initiatives
Strong cross-functional leadership skills, with the ability to align teams, manage dependencies, and resolve organizational, process, and technical bottlenecks
Professional working proficiency in English
Nice to Have
Experience operating infrastructure across multiple regions, data centers, or hybrid cloud environments
Experience managing teams with significant networking or distributed infrastructure responsibilities
Experience working in organizations where infrastructure teams provide internal platforms and services to a large number of engineering teams
Why join us
Help us challenge injustice by creating fair choices for millions of people across 47 countries.
Develop your professional skills with access to mentoring, career consulting, and learning programs
Collaborate with teams around the world and gain international experience through our Global Talent Exchange Program
Engage in company-wide challenges, awards, sports activities, employee-led social impact and volunteering projects
Work alongside people who take initiative, speak openly, and challenge themselves to grow
Improve your language skills through co-financed courses and internal speaking clubs
Annual salary from 90 700 EUR gross
Final benefits may vary depending on the location.
Location: Abu Dhabi, United Arab Emirates
Workplace: hybrid
About the role
We are preparing the data foundation for our autonomous-driving work, and we are hiring the engineer who will start it. Today we collect street-level imagery and GPS tracks from vehicle cameras, including our own camera units with NVIDIA edge devices on board, and use them to update maps. Next comes a dedicated survey vehicle with a full sensor set: multi-camera, IMU, LiDAR. You will start with the imagery and map data pipelines we run today, and as the sensor platform arrives, you will build the pipelines that turn its data into training datasets for autonomous-driving models.
Key Responsibilities
Build the pipelines that move multi-sensor data (multi-camera video, IMU, LiDAR, GPS) from vehicles into cloud storage, synchronize and geo-reference the streams
Own the storage layout and lifecycle for sensor data: partitioning, hot and archival tiers, cost control as volume grows
Build validation and monitoring on the ingest path: incomplete uploads, missing or unsynchronized streams, vehicles that stopped producing usable data
Prepare training datasets for autonomous-driving models: select data by location, route, and conditions, version datasets, and deliver them in the formats ML teams need
Until then: take over and develop the current imagery and GPS-track pipelines from vehicle cameras, and contribute to geodata pipelines (ingestion from company data, OpenStreetMap, Overture Maps and vendors, conflation, validation, publication)
Work with company data in BigQuery and keep spatial queries fast on large PostGIS and GeoParquet tables
Skills, Knowledge & Expertise
4+ years in data engineering
Experience with multi-sensor data from vehicles or robots: cameras, IMU or LiDAR, and keeping the streams in sync
Experience preparing training datasets for computer vision or autonomous-driving models.
Large volumes of binary or media data in object storage (S3 or GCS); distributed processing (Spark or equivalent)
Hands-on experience with spatial data: PostGIS, GeoPandas/Shapely, coordinate reference systems, GPS tracks
Strong Python and SQL; AWS or GCP, Docker, Kubernetes
Nice to have
Sensor data formats such as ROS2 bag, MCAP or point clouds
Parquet, Delta or Iceberg; Databricks
Argo Workflows or Airflow
OpenStreetMap and Overture Map
QGIS
BigQuery
Why join us
Help us challenge injustice by creating fair choices for millions of people across 47 countries.
Develop your professional skills with access to mentoring, career consulting, and learning programs
Collaborate with teams around the world and gain international experience through our Global Talent Exchange Program
Engage in company-wide challenges, awards, sports activities, employee-led social impact and volunteering projects
Work alongside people who take initiative, speak openly, and challenge themselves to grow
Improve your language skills through co-financed courses and internal speaking clubs
Final benefits may vary depending on the location.
Location: Cairo, Egypt
Workplace: hybrid
We are looking for a Senior Data Engineer to join one of the Data Platform teams that works with the Marketing, Growth, partner, and financial data domains.
You will be working with cutting edge cloud technologies (GCP, AWS, BigQuery, Databricks, K8s) and building a large scale data infrastructure for analytics, machine learning, and streaming/CDC data delivery.
Key Responsibilities
Build and operate batch and streaming ingestion into a layered BigQuery DWH (raw → ODS → data marts) using Airflow, Debezium CDC over Kafka with protobuf, Pub/Sub, and Dataflow
Integrate external data sources end-to-end — marketing platforms (GA4, AppsFlyer, TikTok/Meta/Google Ads), payment providers, S3 buckets, and third-party APIs — including schema contracts, backfills, and reconciliation
Engineer the data platform itself in Python: custom Airflow operators and connectors in a shared ETL framework, Kafka Connect on Strimzi (K8s), Cloud Functions, and API integrations with external providers
Build CI/CD and change-management tooling for BigQuery: GitHub-based test-and-approval flows, SQL migration engines (Liquibase/Flyway/Bytebase), sandbox validation, backup and rollback
Own reliability and correctness of pipelines: idempotency, deduplication, late-data handling, backfill and replay, freshness monitoring and alerting; write integration and unit tests
Drive data governance and compliance: ITGC-compliant change management for BigQuery, IAM and least-privilege access, PII policy tags and DLP, Unity Catalog on Databricks, column-level lineage (OpenMetadata/Dataplex), and disaster-recovery planning
Build internal data tools and platform services for agentic workflows with data — Streamlit apps, Slack bots, LLM-based agents and MCP servers that help teams find and use data
Support analysts and business teams with data requests, fostering data-driven decision-making across the company
Contribute to system design and architecture with the development team
Skills, Knowledge and Expertise
Strong practical Python: clean, well-structured, and tested code for services, tooling, and data pipelines
Solid software design skills (OOP, modularity, design patterns) — we build platform tools for agentic workflows with data and plan to develop data-related backend services, so well-designed code is highly valued
Experience building and operating services in a cloud environment (GCP, AWS or similar): CI/CD, containerization, monitoring and alerting
Familiarity with Kubernetes and Terraform — our infrastructure runs on GCP/K8s
Hands-on experience with DWH-related tasks (BigQuery or another cloud warehouse) and confident working SQL
Clear communication with non-engineering stakeholders — a meaningful share of the work is data requests from analysts and business teams
Demonstrated ability to take ownership of technologies or services and proactively contribute ideas to the team
Nice to have
Advanced SQL: complex queries, window functions, partitioning, clustering, and cost optimization
Experience building reliable pipelines around CDC (e.g., Debezium): idempotency, schema evolution, backfills, and reconciliation
Analytical data modeling skills: table grain, facts vs dimensions, slowly changing dimensions, and metric definitions
Experience with stream processing frameworks such as Flink or Apache Beam/Dataflow
Exposure to data governance and audit compliance (ITGC/SOX), Databricks Unity Catalog, or lineage/catalog tooling (OpenMetadata, Dataplex)
Interest in building LLM-based agents and AI tooling for data
Why join us
Help us challenge injustice by creating fair choices for millions of people across 47 countries.
Develop your professional skills with access to mentoring, career consulting, and learning programs.
Collaborate with teams around the world and gain international experience through our Global Talent Exchange Program.
Engage in company-wide challenges, awards, sports activities, employee-led social impact and volunteering projects.
Work alongside people who take initiative, speak openly, and challenge themselves to grow.
Improve your language skills through co-financed courses and internal speaking clubs.
Final benefits may vary depending on the location.
Location: Limassol, Cyprus
Workplace: hybrid
We’re looking for an
Engineering Manager
to lead our
Grocery team
. In this role, you'll play a key role in building high-performance, scalable systems while leading and mentoring a strong engineering team.
Key Responsibilities
Lead and grow engineering teams, supporting new developers' adaptation and career development
Manage project planning, execution, and resource allocation to ensure high-quality and on-time software delivery
Align engineering efforts with business goals through close collaboration with stakeholders
Develop and optimize engineering processes, tools, and technologies in collaboration with the team
Build and nurture a developer community within the company and participate in external conferences
Oversee the use of core technologies, including Golang, Python, MySQL, Kubernetes to ensure efficient development practices
Skills, Knowledge and Expertise
Demonstrated experience in engineering management with a strong background in software development
Solid understanding of high-volume projects, server and client architecture, and development limitations
Excellent leadership and communication abilities, with a talent for explaining complex technical concepts by sharing your technical expertize
Ability to manage multiple projects simultaneously, ensuring timely and quality delivery
Enthusiasm for new technologies and a commitment to ongoing skill development and teamwork
BS/MS in Engineering, Computer Science, or a related field
Experience working on Grocery, Marketplace, or other e-commerce products, with a strong understanding of their business and technical challenges
Why join us
Help us challenge injustice by creating fair choices for millions of people across 47 countries
Develop your professional skills with access to mentoring, career consulting, and learning programs
Collaborate with teams around the world and gain international experience through our Global Talent Exchange Program
Engage in company-wide challenges, awards, sports activities, employee-led social impact and volunteering projects
Work alongside people who take initiative, speak openly, and challenge themselves to grow
Improve your language skills through co-financed courses and internal speaking clubs
Annual salary from 90 700 EUR gross
Final benefits may vary depending on the location.
Location: Almaty, Kazakhstan
Workplace: hybrid
The QA Load team is responsible for the performance and scalability of services across their entire lifecycle — from Go/No-Go decisions before launch to working with real production traffic. We design and execute load tests, model real-world usage scenarios, identify and eliminate bottlenecks in backend systems, databases, messaging, and infrastructure, and work closely with SRE, DevOps, and product engineering teams.
The role focuses on systematic performance improvement: building load-testing infrastructure, automation and CI/CD quality gates, performance analysis and experimentation, and translating technical metrics (latency, RPS, p95/p99) into clear business impact. Our goal is to make performance a core aspect of product quality and ensure the stable operation of high-load, geo-distributed systems.
Key Responsibilities
Performance Audits & Integration Support:
Go/No-Go decisions with our stakeholder about launches services before integrating them into our ecosystem and integrations support (audit APIs, web, databases, caches, message brokers)
Design&Execute Load Tests global strategy:
Modeling of load testing scenarios (load, stress, soak, peak, find max tests), methodology and formation of non-functional requirements for system using k6, JMeter (DSL framework)
Simulate real-world scenarios and traffic patterns to validate systems under production-like conditions (including high RPS, concurrency levels, data skew, and geo-distributed load)
Load Modeling:
Calculate and validate detailed load models – e.g. expected RPS, concurrent users, transaction volumes, and data distribution.
Adjust these models to mirror peak and edge-case scenarios, ensuring our capacity planning is grounded in reality.
Bottleneck Analysis & Fixes:
Find, analyze, and fix performance bottlenecks. This includes:
backend issues (CPU hotspots, memory leaks, GC pauses, thread pool exhaustion)
DB inefficiencies (Aurora MySQL/RDS indexing, query locks, IOPS limits)
messaging/queue back-pressure (Kafka partitioning, consumer lag, throughput limits)
network constraints (latency, load balancer settings, timeouts)
Work hands-on with SRE, DevOps, and Backend teams to implement optimizations and verify the improvements.
Prepare for Production Traffic:
Ensure services are production-ready. Drive capacity planning exercises and stress tests to determine scaling lag/limits and failure modes. Verify that services can gracefully handle traffic spikes or component failures without violating SLAs or SLOs. Help partners configure autoscaling policies for sustained performance
Loadtesting Infrastructure Setup&Tuning:
Set up, audit, and operate performance testing infrastructure for our services load testing automation and geo-distributed load generator to simulate production traffic
Performance Analysis & Experimentation:
Approach performance issues with an performance analyst/architect mindset – form hypotheses, design experiments, run tests, and draw conclusions to configuration infrastructure and fix backend code.
For example, if latency is high, hypothesize the cause (e.g. database index missing), test the hypothesis, and then make a the fix. Repeat this iterative process to methodically improve system performance.
Business Impact Translation:
Translate technical performance insights into business risks and opportunities. For instance, demonstrate how reducing latency or improving p95 response time can increase conversion rates or GMV. Communicate these insights to both engineers and non-technical stakeholders to underline the business value of performance work.
Observability & Monitoring:
Utilize observability tools to monitor performance during tests. Set up dashboards and alerts in Grafana/Prometheus and other systems (e.g. New Relic, Dynatrace, Zabbix, ELK Stack, Coroot), analyze traces and logs. Use few methods to interpret results and guide troubleshooting.
Performance Tooling:
Build internal performance tooling in Golang: Design and maintain internal tools for automation performance testing and analysis Load as a Service for product teams: traffic generators, load managers, mocks, test stubs, data feeders, and helper services used across partner integrations. Building load test scenarios to Jmeter (DSL framework) and k6 in JavaScript. Innovate on tooling to make load tests easier to create, run, and analyze for the whole team.
CI/CD Integration:
Integrate performance tests into our CI/CD pipeline as QG. Automate load tests run to every new integration or code change is evaluated for performance regressions. Ensure that build pipelines can catch and flag performance drops (e.g. increased response times, higher resource usage) before changes reach production.
Reporting&Communication:
Prepare executive-ready performance reports for engineering teams and C-level stakeholders. Highlight key metrics (p95/p99 latency, throughput, error rates, saturation signals) and identify primary scalability bottlenecks across services, databases, caches, and messaging systems, and compare results against SLOs and capacity targets. Provide clear root-cause explanations and actionable recommendations, including performance vs cost trade-offs, to support production readiness and Go/No Go decisions.
Performance Culture:
Champion a culture where performance = product quality.
Educate and influence teams to treat performance testing as an integral, continuous part of the development lifecycle rather than a one-time task.
Share best practices and success stories, ensuring that performance considerations are baked into design, coding, and testing processes from the start.
Conducting production load and traffic validation experiments with full ownership of risk assessment, blast radius control, observability, and rollback procedures.
Work with geo-distributed, high-load systems (200k+ RPS) and latency-sensitive mobile flows
Set up production-like performance environments in AWS
Conducting and modeling load tests in production
Performance analytics → hypotheses → experiments → fixes → measurable business impact (latency, conversion, GMV)
Skills, Knowledge and Expertise
5+ years in Performance/SRE/Load Testing
Strong programming skills in Golang/Kotlin/Java
Strong k6 or JMeter DSL - Performance Testing Mastery: Hands-on expertise with modern load testing tools, especially k6 and/or JMeter (and their scripting DSL). Deep understanding of performance best practice – you know when to apply load testing vs stress testing vs soak testing, and how to interpret the results of each.
Hands-on experience with AWS infrastructure on for high-load systems: EKS, EC2, Auto Scaling Groups, ALB/NLB, VPC, subnets, routing, security groups
Designing and tuning autoscaling strategies for compute (KEDA/Karpenter/Cluster Autoscaler, EC2 scaling policies)
Understanding network performance characteristics: latency, cross-AZ traffic, NAT/egress limits, load balancer behavior under high RPS
Profiling Golang, Kafka, Redis, DB tuning experience
Automation loadtesting and make QG (JVM languages and Go)
SRE mindset (RED/USE, SLO, error budgets)
Why join us
Help us challenge injustice by creating fair choices for millions of people across 47 countries.
Develop your professional skills with access to mentoring, career consulting, and learning programs.
Collaborate with teams around the world and gain international experience through our Global Talent Exchange Program.
Engage in company-wide challenges, awards, sports activities, employee-led social impact and volunteering projects.
Work alongside people who take initiative, speak openly, and challenge themselves to grow.
Improve your language skills through co-financed courses and internal speaking clubs.
Final benefits may vary depending on the location.
ОфисseniorCanada, Vancouver, BC
УдалённоseniorHybrid/Remote - Europe (incl. UK); Estonia (remote); Switzerland (remote); Bulgaria (remote); Serbia (remote); Luxembourg (remote); France (remote); Paris (on-site); Norway (remote); England (remote); Croatia (remote); Sweden (remote); Scotland (remote); Slovenia (remote); Czechia (remote); Florence (on-site); Milan (on-site); Spain (remote); Romania (remote); Netherlands (hybrid); Ukraine (remote); Austria (remote); Denmark (remote); Ireland (remote); Slovakia (remote); EU (remote); Leuven (on-site); Belgium (remote); Northern Ireland (remote); Athens (on-site); Amsterdam (hybrid); Lithuania (remote); UK (remote); Hungary (remote); Finland (remote); Poland (remote); Wales (remote); Germany (remote); Eindhoven (hybrid); Moldova (remote); Albania (remote); Greece (remote); Zurich (on-site); Portugal (remote); Bristol (on-site); Italy (remote); Latvia (remote); We welcome applicants from all backgrounds to join us in shaping the future of AI
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