{"id":1498308,"url":"https://alion.io/job/mayflower-lead-ml-engineer","title":"Lead ML Engineer","company":{"id":6026,"name":"Mayflower","domain":"mayflower.work","url":"https://alion.io/company/mayflower","size_band":null,"is_staffing_agency":true,"employer_type":"agency","is_intermediary":false,"listed_via":null,"ats_vendor":"Recruitee","truth_index":{"grade":"B","score":79,"open_postings":8,"ghost_share":0,"stale_share":0.625,"repost_share":0,"time_to_fill_p50_days":63,"computed_at":"2026-10-01T05:45:00Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":"lead","employment_type":null,"work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Limassol, Cyprus"],"countries":["CY"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":65000,"max_usd":155000,"period":"year","method":"global_role_cell_scaled_by_country","sample_n":773},"experience_years_min":5,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Docker","optional":false},{"name":"Machine Learning","optional":false},{"name":"Python","optional":false},{"name":"SQL","optional":false},{"name":"Anomaly Detection","optional":true},{"name":"Apache Kafka","optional":true},{"name":"CI/CD","optional":true},{"name":"Computer Vision","optional":true},{"name":"FastAPI","optional":true},{"name":"Fine-tuning","optional":true},{"name":"Kubernetes","optional":true},{"name":"LLM","optional":true},{"name":"MLFlow","optional":true},{"name":"NLP","optional":true},{"name":"Recommender Systems","optional":true}],"status":"live","first_seen_at":"2026-09-29T10:25:23Z","employer_posted_date":"2026-09-29","last_verified_at":"2026-10-01T10:53:46Z","board_verified":true,"closed_at":null,"days_open":2,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":2},"description":"Mayflower is a technology company building high-load products used by millions of people worldwide. Operating at the scale of one of the world's top-50 websites, we solve complex engineering challenges and create solutions that power real-time entertainment for a global audience.\nWe are looking for a Lead ML Engineer to own a new ML stream focused on fast delivery of applied machine learning solutions across different product and business domains.\nThe stream will work with a broad range of ML challenges. Some initiatives may be relatively small and delivered within a few weeks, while others may prove their value and grow into larger dedicated projects.\nThis role combines hands-on ML engineering with end-to-end technical delivery ownership. You will receive product problems and expected outcomes from Product or internal stakeholders, clarify the technical requirements and constraints, define the implementation approach, coordinate execution within the stream, and bring solutions to production readiness and launch.\nThis is a highly hands-on role. The stream will not have a dedicated software engineer for every initiative, so we expect Data Scientists and ML Engineers to be comfortable working beyond experimentation and contributing directly to production code.\nThe role is not tied to a single ML domain. We value strong ML fundamentals, engineering skills, pragmatism, and the ability to quickly understand new problem areas more than deep specialization in one particular class of models.\nJob Responsibilities\nTechnical Delivery Ownership\nTurn product and business problems into concrete ML implementation plans.\n\nClarify requirements, constraints, available data, integrations, and success criteria together with Product and relevant stakeholders.\n\nDefine technical scope, milestones, dependencies, risks, and delivery estimates.\n\nSelect appropriate ML approaches and determine the fastest reliable way to validate and implement them.\n\nDrive technical delivery through experimentation, implementation, integration, deployment, and launch readiness.\n\nKeep delivery on track, proactively identify blockers, and coordinate dependencies with other teams.\n\nProvide Product with clear technical options, trade-offs, estimates, risks, and experiment results required for product decisions.\n\nSupport production rollout and iteration based on observed results.\n\nHands-on ML & Engineering\nDesign, train, evaluate, and deploy ML models across different domains and problem types.\n\nWrite production-quality Python and contribute directly to implementation.\n\nBuild APIs, batch jobs, data-processing pipelines, and ML services required to deliver solutions where appropriate.\n\nWork with classical ML, deep learning, and foundation-model-based approaches depending on the problem.\n\nProcess and transform large production datasets using Python and SQL.\n\nIntegrate models into existing production systems.\n\nImplement appropriate testing, monitoring, logging, and observability for delivered ML solutions.\n\nWork within the shared ML infrastructure, architecture, and engineering practices used across the company.\n\nCollaborate with Data Science, Backend, Data Engineering, and MLOps specialists when deeper domain expertise or infrastructure changes are required.\n\nStream Execution\nBreak initiatives down into concrete technical tasks and coordinate execution within the stream.\n\nCoordinate the work of Data Scientists and ML Engineers contributing to stream initiatives.\n\nReview technical approaches, experiments, and implementation.\n\nKeep the team focused on the agreed scope, priorities, and delivery timeline.\n\nIdentify technical risks and dependencies early and drive them to resolution.\n\nEscalate architectural, infrastructure, or methodological questions when broader alignment is required.\n\nHelp prepare successful initiatives for scaling or transition into longer-term ownership if they grow beyond the scope of the stream.\n\nCross-functional Collaboration\nWork closely with Product throughout the delivery lifecycle.\n\nIndependently gather the technical details and constraints required to execute on a product request.\n\nCommunicate estimates, dependencies, technical trade-offs, and delivery status clearly.\n\nWork directly with Engineering and other internal teams to unblock implementation.\n\nChallenge technically unclear, contradictory, or infeasible requirements and propose practical alternatives.\n\nRequirements\nYou’ll thrive here if you have\n5+ years of commercial experience in Machine Learning, Data Science, or ML Engineering.\n\nStrong hands-on Python programming and software engineering skills.\n\nExperience taking ML solutions from a product requirement through experimentation, implementation, integration, and production.\n\nStrong understanding of machine learning methods, statistics, experimentation, and model evaluation.\n\nExperience writing maintainable production code rather than working exclusively in notebooks.\n\nExperience building APIs, services, batch processing, or data pipelines.\n\nStrong SQL skills and experience working with large production datasets.\n\nPractical experience with Docker and production deployment environments.\n\nAbility to turn partially defined problems into concrete technical plans.\n\nAbility to estimate work, identify dependencies and risks, and drive technical execution against a timeline.\n\nExperience owning technical delivery involving several contributors and coordinating work across dependencies.\n\nExperience reviewing code and technical approaches.\n\nAbility to work effectively within established engineering and ML practices while independently owning delivery within a stream.\n\nStrong communication skills and the ability to work directly with Product and technical stakeholders.\n\nAbility to balance speed and engineering quality: validate ideas quickly when uncertainty is high and build robust solutions when moving towards production.\n\nNice to Have\nPrevious experience as a Tech Lead, Stream Lead, or technical owner of ML initiatives.\n\nExperience with Kubernetes and CI/CD.\n\nExperience with Kafka or other streaming platforms.\n\nExperience with Airflow, MLflow, experiment tracking, model monitoring, or similar tooling.\n\nExperience building real-time or high-load ML services.\n\nExperience with FastAPI or similar Python service frameworks.\n\nExperience across several ML domains, such as recommendation systems, ranking, NLP/LLMs, Computer Vision, anomaly detection, forecasting, or classification.\n\nExperience with LLM inference, fine-tuning or other GenAI systems.\n\nExperience in teams where Data Scientists and ML Engineers own a substantial part of production implementation themselves.","description_format":"text","description_chars":6660,"description_truncated":false,"requirements":{"experience_years_min":5,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":null,"security_clearance":false,"languages":[]},"benefits":[],"hiring_locations":[],"hiring_excludes":[],"relocation_offered":false,"industries":[],"lifecycle":[{"event":"open","at":"2026-09-30T02:53:11Z"}],"liveness":{"score":54,"band":"ok","label":"Likely open","p_open":1,"p_active":0.542,"p_room":1,"age_days":1,"expected_fill_days":63,"reasons":["conf:5","agency","velocity","win:early"],"computed_at":"2026-10-01T05:45:00Z"},"pay":null,"html_url":"https://alion.io/job/mayflower-lead-ml-engineer","json_url":"https://alion.io/job/mayflower-lead-ml-engineer.json","meta":{"generated_at":"2026-10-01T12:21:47Z","cache_seconds":300,"methodology":"https://alion.io/methodology","terms":"https://alion.io/terms","contact":"https://alion.io/contact","api":"https://alion.io/developers","usage":{"tier":"crawler","counted_by":"address","units_charged":1,"used_today":3722,"day_limit":5000,"remaining_today":1278,"minute_limit":60,"resets_at":"2026-10-02T00:00:00Z"}}}