Geomotiv Career

ABOUT THE PRODUCT:

ElementalTV, founded in 2020 in Pasadena, CA, empowers publishers to maximize advertising inventory value with advanced audience data and AI curation for direct programmatic transactions. ElementalTV’s 1Audience solution provides a comprehensive view of audiences using deterministic and probabilistic data.

1Audience Alliance (1AA) unites CTV publishers to address common challenges, enhance buyer engagement, and optimize revenue. The product offers a video advertising technology stack, including ad server, SSP, DSP, DMP, and more, facilitating access to direct, programmatic, and third-party inventory buyers/sellers. The tech stack utilizes Java 21, Jetty/Netty, Druid, Kafka, Aerospike, PostgreSQL, and runs on Proxmox provisioned by Ansible with Jenkins for deployment. Client-side is Angular 19-based SPA.

ElementalTV’s team spans the USA, Germany, Portugal, Ukraine, Poland, Armenia, Belarus, Pakistan and India. The regular day-to-day process management is done using Jira, Confluence, Slack, and audio/video calls.

WHY THIS ROLE EXISTS:

Elemental runs a fully vertically integrated CTV advertising stack — ad server, bidder, planner, scheduler, SSAI, decisioning, reporting — on our own hybrid cloud. It is a soft real-time system inside an ecosystem of supply and demand partners, each with their own protocol extensions and failure modes.

Quality control has not scaled with it. Verification is largely manual, automation coverage is partial and unversioned, regression scope is not fully documented, and some behaviour can only be validated in production because we cannot reproduce it anywhere else. The role is to build quality control as an engineering discipline, own it, and separate it from development.

WHAT YOU’LL BE UP TO:

The build described below is conventionally a fifteen-to-twenty-engineer undertaking. We intend to do it with a small team.

Our intent is that verification is built, authored, and maintained primarily by coding agents, with engineers designing the systems those agents operate inside. We are not there — today the function works manually. What we have is one demonstration: a staging harness with traffic generation and stubbed demand partners, stood up through agents by a single engineer and re-establishing itself on restart. Getting from that to a function that works this way is the substance of the role.

• Correctness against specification. Our specification is being reconstructed into a machine-readable source of truth. Quality control consumes it, and verifies automatically that documented behaviour matches actual behaviour.
• Performance within budget. In a soft real-time system, correct is not enough. Budgets need to be defined per component and enforced automatically; today they are neither.
• Ecosystem integrity. A change correct in isolation can still break a DSP’s OpenRTB extension or a platform’s stream requirements. This has to be verifiable before release.
• Behaviour under failure. Breaking components on demand and confirming the rest of the system responds as specified — degrade a node, verify the external symptoms of internal throttling against the spec.

All four require infrastructure we do not have: a repository your team owns, simulators for the external systems we depend on, environments where components can be instructed to fail, permanent synthetic traffic on staging, and tests versioned against the code and specification they verify. Today the suite drifts out of sync and is refitted to match reality; we want reality held to the tests.

The core of the build is infrastructure agents can operate. Machine-readable specification as generation context, so agents author from the normative source rather than from the code they are checking. Rules and repository structure that constrain what they produce. Environments an agent provisions, exercises, and tears down unattended. Feedback loops tight enough that an agent can tell whether what it built works. People sit at the end of the chain — what can only be judged by eye, and the escalation point where the system cannot decide. Where that boundary sits is yours to design.

THE PROBLEM:

If agents author the verification, what verifies the agents?

The failure mode already exists manually: when a run goes red, the path of least resistance is to adjust the test until it goes green. At agent scale it industrialises — coverage counts rise while coverage means less. Our current thinking is generation from specification rather than implementation, agent output evaluated rather than trusted, drift detection between what tests assert and what the spec requires, and explicit boundaries where determinism is mandatory and generated code is not permitted. That is a sketch. Closing it is part of the job.

TEAM AND AUTHORITY:

You inherit a small existing quality team and the authority to grow, develop, and restructure it. This is a VP-level role: you own hiring and staffing decisions for your function, not only its workload.

At our scale we need playing coaches rather than pure managers. You are not expected to be the most productive engineer in the room. You are expected to be technical enough to design the approach, to implement meaningful parts of it yourself, and to judge which of your engineers can operate at the level it requires. The transformation runs in parallel with active delivery — we are not stopping releases while it happens.

OUT OF SCOPE:

• Specification archaeology. Reconstructing our source-of-truth documentation is a separate track with separate owners. You consume that specification and hold the system to it.
• Model evaluation. We run AI products of our own; assessing model behaviour is a separate track. Your remit is the platform and the agentic systems that verify it.

WHAT SUCCESS LOOKS LIKE (FIRST 12 MONTHS):

You will produce your own version of this in your first weeks, and we expect to be argued with. As a starting position:

• A green regression run is evidence rather than a formality, and release is no longer gated on manual verification for most changes.
• Partner-facing and failure-path behaviour is verifiable pre-production.
• New coverage is produced predominantly by agents working inside infrastructure your team owns, with a defensible basis for trusting what they produce.
• The existing team has been assessed honestly, developed where possible, and shaped into an engineering function.

WHAT YOU’LL NEED TO HAVE:

• 10+ years in IT or software engineering, with at least 5–7 years in a senior leadership role (Director or VP level) managing QA/testing organizations.
• Systems Engineering Background: You have hands-on experience building — not just testing — distributed, high-throughput, or real-time systems, but your true passion lies in driving system quality and reliability.
• Applied AI Infrastructure: You have deployed coding agents as core infrastructure (e.g., context and specification pipelines, automated constraints, unattended environments) rather than just using them as personal productivity tools. You hold a pragmatic, considered view on the trust boundaries of AI output.
• First-Principles Engineering: You possess a strong grasp of system composition, failure modes, and verifiability. While you may not be a pure feature developer, you write pragmatic code whenever the problem demands it.
• Advanced Reliability & Testing: You have deep, practical expertise in fault injection, dependency simulation, performance validation, and designing robust test environments.
• Empathetic Leadership: You have the sound judgment to objectively evaluate an existing team’s capabilities and the temperament to coach and elevate them, rather than defaulting to replacement.
• Demonstrated ability to lead and set priorities for a small engineering team while staying hands-on in the systems and architecture. Highly autonomous and comfortable owning a function without a large supporting organization.
• Fluent in Russian, Ukrainian or Belarusian (working languages of the engineering team).
• English – B2+.

WOULD BE A PLUS:

• Your career trajectory aligns closely with an SDET or Platform Engineering profile (a background of pure manual QA transitioning into basic automation is not a fit for this role).
• Strong Plus: Experience with Connected TV (CTV) or programmatic advertising ecosystems (OpenRTB, VAST, HLS, SSAI, high-scale ad serving), as well as managing Kubernetes across hybrid infrastructure.

INTERVIEW STEPS:

• HR interview.
• Introductory conversation.
• Problem brief and written assignment. We give you the problem as it stands; you come back with how you would solve it.
• Defence to the CTO and engineering leadership. We will push on how you bound agent authority, what you would never let a generated test decide, and how you would know when agent-produced coverage has degraded. No algorithm round.
• If your approach convinces us, it becomes your mandate — and you implement a meaningful part of it yourself.

FAQ:

• Remote? — Yes.
• AI tools? — We use them heavily.
• Work-life? — No overtime or work at weekends. Rest well – unless prod is seriously broken.
• Team? — You’ll ship with experienced people from around the world.
• English? — B2+ – comfortable in meetings, docs, PR reviews.

SUMMARY:

We are looking for an experienced Solution Architect/Tech Lead to provide technical expertise in the AdTech domain, design high-load systems, and contribute to pre-sales activities.

You will play a key role in shaping technical solutions for our clients, building reusable AdTech architectures, and leading the company’s internal knowledge development to grow the company’s competence.

WHAT YOU’LL BE UP TO:

Pre-Sales & Client Engagement

• Participate in discovery sessions and client meetings.
• Analyze business requirements and translate them into technical solutions.
• Prepare architectural diagrams, high-level designs, and effort estimates.
• Support the sales team in RFP responses, tenders, and pitches.

Solution Architecture

• Design architecture for AdTech systems (RTB, DSP, SSP, CTV, data pipelines, attribution, privacy compliance).
• Select an appropriate technology stack and justify architectural decisions.
• Define non-functional requirements (scalability, latency, cost efficiency, compliance).

Knowledge Management

• Build and maintain the AdTech knowledge base: reusable architectures, RFP boilerplates, glossaries, and case studies.
• Conduct internal workshops and mentor engineers.
• Develop and manage the company’s “AdTech Competence Center.”

Delivery Oversight

• Participate in early project phases after deal closure: architecture validation, team onboarding, and reviewing key decisions.

WHAT YOU’LL NEED TO HAVE:

Experience:

• 5+ years in designing and building high-load distributed systems;
• 2+ years of experience in AdTech (DSP, SSP, RTB, DMP/CDP, attribution, programmatic buying, CTV) is a strong plus;
• Proven track record of participating in pre-sales/discovery sessions and RFPs;
• Previous role as an architect or tech lead with experience guiding teams and making architectural decisions.
  

Technical Skills:

• Backend: Java / Scala / Go / Python (at least one strong stack);
• Big Data: Kafka, Spark/Flink, ClickHouse, Druid, Presto/Trino;
• Cloud: AWS / GCP (Pub/Sub, BigQuery, Kinesis, Redshift);
• Databases: SQL/NoSQL (Postgres, Cassandra, DynamoDB);
• API design (REST/gRPC, GraphQL);
• Architectural patterns: microservices, event-driven, streaming pipelines;
• Strong understanding of OpenRTB, bid request/response flows, and low-latency systems;
• Knowledge of privacy regulations (GDPR/CCPA) and consent management solutions.
  

Soft Skills:

• Excellent client-facing communication skills (from C-level executives to engineers);
• Ability to “sell” architectural decisions by demonstrating their business and technical value;
• Leadership and mentoring skills to grow internal expertise;
• English: Advanced (required for client meetings).

INTERVIEW STEPS:

• HR interview;
• Technical interview.

ABOUT THE PROJECT:

The project is a cutting-edge healthcare technology provider dedicated to transforming patient care through real-time data insights. By leveraging advanced machine learning and programmatic automation, it empowers healthcare organizations to deeply understand patient data, ultimately accelerating and improving the accuracy of clinical diagnoses and solutions. The core mission is to elevate the predictive capabilities of modern healthcare through the comprehensive suite of analytics solutions.

Working hours: 9 a.m. – 6 p.m. ET US hours

WHAT YOU’LL BE UP TO:

• SRE & Platform Engineering (60–70%): Build and maintain the foundational infrastructure platform. Lead automation, establish rigorous observability, ensure system reliability, handle incident response, and manage Infrastructure as Code (IaC);
• Data Infrastructure Operations (20–30%): Architect, manage, and optimize the Kafka ecosystem, Ceph storage, and Hadoop clusters (including topic/partition design, governance, and capacity forecasting);
• Reduce operational toil through extensive automation, faster incident mitigation, and the deployment of preventive monitoring systems;
• Maintain fundamental cluster health, ensure high availability, and own backup/recovery and Disaster Recovery pipelines;
• Develop self-service platforms and observability stacks to empower the Data and ML engineering teams without directly managing ETL pipelines or writing data transformations (0-10% classic Big Data Engineering).

WHAT YOU’LL NEED TO HAVE:

• 5+ years of hands-on experience in SRE, Platform Engineering, or DevOps, specifically operating, tuning, and scaling distributed systems in high-traffic production environments;
• Deep, under-the-hood operational expertise in Kafka, Ceph, Hadoop, or comparable distributed storage and streaming infrastructure;
• Strong automation and scripting skills (Python, Go, or Bash) focused on infrastructure management, deployments, and tooling (rather than heavy data engineering/ETL);
• Extensive experience with Infrastructure as Code (Terraform, Ansible), CI/CD pipelines, and container orchestration (Kubernetes);
• Solid background in implementing observability, monitoring, and alerting stacks (e.g., Prometheus, Grafana, ELK/EFK, Datadog);
• Experience administrating, scaling, and monitoring databases (e.g., PostgreSQL) and analytical data warehouses (e.g., ClickHouse, Snowflake);
• A proven track record of designing architectures that prioritize scale, high availability, and fault tolerance;
• English B2+.

NICE TO HAVE:

• Familiarity with the operational and deployment aspects of batch/stream processing frameworks (Spark, Flink) and workflow orchestration (Airflow, Dagster);
• Hands-on experience designing multi-region data replication architectures and robust Disaster Recovery (DR) pipelines;
• A proven track record of driving cost optimization across large-scale infrastructure without sacrificing system performance;
• Practical experience operating and seamlessly bridging complex hybrid environments (pairing on-premise/bare-metal hardware with public cloud elasticity).

INTERVIEW STEPS:

• HR interview + English check;
• Technical interview (2 Rounds);
• Additional interview with headquarters.

FAQ:

• Remote? — Yes.
• AI tools? — We use them heavily.
• Work-life? — No overtime or work at weekends. Rest well – unless prod is seriously broken.
• Team? — You’ll ship with experienced people from around the world.
• English? — B1+/B2 – comfortable in meetings, docs, PR reviews.
• Benefits? — Yes:

1. Comprehensive medical plans, affordable medical, dental and vision options, 100%-paid life & disability insurance;
2. Free virtual fitness classes, Better Yourself Wellness program;
3. Generous annual tuition reimbursement, ongoing team trainings;
4. Paid vacation, sick time, and company holidays (including a floating holiday);
5. Team-building events, happy hours, holiday celebrations, and more.

ABOUT THE PROJECT:

The project is a cutting-edge healthcare technology provider dedicated to transforming patient care through real-time data insights. By leveraging advanced machine learning and programmatic automation, it empowers healthcare organizations to deeply understand patient data, ultimately accelerating and improving the accuracy of clinical diagnoses and solutions. The core mission is to elevate the predictive capabilities of modern healthcare through the comprehensive suite of analytics solutions.

Working hours: necessary overlap until 1 or 2 pm EST. 

WHAT YOU’LL BE UP TO:

• Architect, scale, and maintain existing Kubernetes-based platform to ensure long-term stability, performance, and adaptability;
• Gather and analyze requirements from development, data management, and SRE teams to continuously evolve the platform to meet business needs;
• Develop and implement missing functionalities within the open-source tools that serve as our core K8s platform components;
• Engage with open-source community maintainers to integrate our customizations upstream. For custom forks, manage and merge necessary bug fixes from upstream projects;
• Build and manage CI/CD pipelines to run regression tests and securely release new versions of component images;
• Rigorously test new platform component versions and orchestrate seamless, zero-downtime upgrades for production clusters;
• Proactively identify system limitations, evaluate alternative platform components, and stage proof-of-concept migrations to validate performance benefits;
• Partner with the SRE team to design and automate cluster diagnostics and rapid issue recovery workflows;
• Guide and assist development teams in configuring their own CI/CD pipelines and successfully implementing GitOps practices for their specific workloads.

WHAT YOU’LL NEED TO HAVE:

• 5+ years of experience running, bootstrapping, and automating highly available, secure Kubernetes clusters in production environments;
• Deep knowledge of Kubernetes concepts, APIs, and inner workings (both control plane and kubelet);
• Proven ability to port legacy applications into a Kubernetes cluster;
• Experience managing persistent state in a cluster using reliable storage tools;
• Bare metal/on-premise environment experience
• 2+ years of Golang development, specifically with async programming and TDD;
• Proficiency with Golang’s Kubernetes client library and the ability to customize open-source Golang tools;
• Strong grasp of GitOps principles and hands-on experience with tools like Argo CD or Flux;
• Ability to automate infrastructure tasks and build CI/CD pipelines;
• Strong networking knowledge, specifically involving LVS, BGP protocols, and Linux firewalls;
• Required experience setting up external ingress into K8s clusters using Project Contour, Envoy, HAProxy, or Nginx reverse proxies;
• Ability to configure monitoring/alerting using Prometheus, including writing custom metric exporters for legacy apps;
• Experience running centralized log aggregation using Elasticsearch stacks or Loki;
• Proven ability to implement issue detection and recovery mechanisms using K8s;
• Ability to troubleshoot and resolve production issues under pressure;
• Strong cross-functional communication skills to collect requirements and deliver solutions for SRE, network engineering, and application development teams;
• English B2+.

NICE TO HAVE:

• Ability to proactively detect or automatically resolve production issues before they impact revenue;
• Active contributions to Golang-based Kubernetes open-source projects;
• Experience running workloads in GCP or AWS;
• Familiarity with Kube-router, Kubeadm, and Puppet;
• Knowledge of specific storage solutions like Rook and Ceph;
• Experience packaging applications as Helm charts;
• Familiarity with Java and Kubernetes-native Java frameworks (e.g., Quarkus).

INTERVIEW STEPS:

• HR interview + English check;
• Technical interview;
• Additional interview with headquarters.

ABOUT THE COMPANY:

Geomotiv is an international software development company with a reputation in the US and European media industry. Since 2010, we’ve been providing comprehensive AdTech solutions and have accumulated solid tech experience in different spheres. Join our team of over 150 engineers and work on top-tier projects with world-class organizations! Become part of our crew, grow with us, and develop your skills – we will put you front and center!

Geomotiv is part of a larger family of companies, including an advertising technology company, Adoppler, and its sister, Elemental TV.

SUMMARY:

We are looking for a proactive and results-driven Business Development Representative with solid experience in the ADtech domain. In this role, you will focus on generating qualified opportunities, engaging key decision-makers, and supporting the sales pipeline from first contact to scheduled demos.

The ideal candidate understands the digital advertising ecosystem (SSP, DSP, RTB, etc.), has hands-on SaaS sales experience, and is motivated to grow in a fast-paced, performance-driven environment.

WHAT YOU’LL BE UP TO:

• Identify and qualify potential clients within the ADtech ecosystem (publishers, advertisers, agencies, ad networks).
• Execute outbound lead generation campaigns (email, LinkedIn, cold outreach and etc.).
• Support pre-sales activities and collaborate closely with the Sales and Marketing teams.
• Optimize lead generation strategies and maintain accurate CRM records.

WHAT YOU’LL NEED TO HAVE:

• 3+ years of experience in B2B SaaS sales as a BDR, SDR, or Lead Generator.
• Proven experience in the ADtech domain is a must (SSP, DSP, RTB, etc.).
• Strong communication and customer engagement skills.
• Experience participating in pre-sales activities and product demos.
• Hands-on experience with CRM systems and sales automation tools.
• Ability to analyze market trends and identify potential clients.
• Self-motivated, proactive, and eager to grow in sales.
• Strong attention to detail and consistency.
• English level – C1 or higher.

WOULD BE A PLUS:

• Willingness to travel and represent the company at industry meetups, conferences, exhibitions, and networking events.

INTERVIEW STEPS:

• HR interview.
• Interview with Head of Sales (+ CEO – optional).