{"id":1142627,"url":"https://alion.io/job/kpler-ml-engineer-power-2","title":"ML Engineer - Power","company":{"id":4764,"name":"Kpler","domain":"kpler.com","url":"https://alion.io/company/kpler","size_band":"501-1000","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Lever","truth_index":{"grade":"B","score":79,"open_postings":6,"ghost_share":0,"stale_share":0.833,"repost_share":0,"time_to_fill_p50_days":57,"computed_at":"2026-10-16T05:45:00Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":"junior","employment_type":"full_time","work_mode":"hybrid","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Paris, France"],"countries":["FR"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":null,"experience_years_min":2,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Agile","optional":false},{"name":"CI/CD","optional":false},{"name":"Git","optional":false},{"name":"Machine Learning","optional":false},{"name":"PostgreSQL","optional":false},{"name":"Python","optional":false},{"name":"Time Series Forecasting","optional":false},{"name":"Airflow","optional":true},{"name":"Apache Kafka","optional":true},{"name":"AWS","optional":true},{"name":"Docker","optional":true},{"name":"GCP","optional":true},{"name":"Kubeflow","optional":true},{"name":"Kubernetes","optional":true},{"name":"MLFlow","optional":true}],"status":"live","first_seen_at":"2026-09-23T12:07:21Z","employer_posted_date":"2026-09-23","last_verified_at":"2026-10-17T01:32:07Z","board_verified":true,"closed_at":null,"days_open":23,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":23},"description":"About the Role\nAs a Machine Learning Engineer at Kpler, you will play a key role in developing and deploying predictive models that power our global commodity, energy, and maritime intelligence platforms. Working closely with Data Scientists, Data Engineers, and Product teams, you will bridge the gap between machine learning experimentation and production-grade software delivery. Your work will directly transform complex data flows into real-time, actionable insights that help world-leading trading firms, industrial leaders, and analysts make critical decisions.\nKey Responsibilities\nArchitect and deploy ML pipelines: Design, build, and maintain production-grade machine learning workflows and microservices for power market forecasting and electricity grid modeling.\n\nBridge research and engineering: Transition statistical and machine learning prototypes from initial experimentation into scalable, production-ready Python applications.\n\nManage time-series and event data systems: Design and optimize database schemas in PostgreSQL to handle high-throughput time-series data, event streams, and normalization routines.\n\nImplement robust MLOps practices: Establish automated model training, backtesting, evaluation, tuning, and feature/model versioning standards across deployments.\n\nDrive data engineering quality: Construct clean ingestion and transformation pipelines, ensuring high integrity, validation, and low-latency access across analytical models.\n\nChampion software excellence: Write modular, well-tested Python code, actively participating in peer code reviews, CI/CD automation, and Agile delivery processes.\n\nExperience & Background\nWhat you'll need (Must-haves)\nSoftware engineering foundation: Approximately two to five years of experience as a data-focused software engineer.\n\nPython mastery: Significant experience working with large production Python codebases, rather than working exclusively in notebooks.\n\nDomain knowledge: Deep understanding of electricity-grid fundamentals, including generation, transmission, and electricity markets.\n\nData engineering & databases: Experience in data engineering, including working with PostgreSQL or similar databases, database design, data normalisation, and managing time-series and event data.\n\nDS & ML research rigor: Proven experience in data science and machine learning research, encompassing statistics, hypothesis testing, model training, evaluation, backtesting, tuning, and model selection.\n\nMLOps & versioning: Practical experience in machine learning engineering, specifically including model and feature versioning.\n\nEngineering practices & communication: Confidence working with Git, code reviews, and Agile methodologies, supported by strong written and spoken English.\n\nNice-to-haves\nCloud platforms: Experience deploying ML workloads on AWS or GCP using Docker and Kubernetes.\n\nWorkflow orchestration: Familiarity with orchestration tools such as Apache Airflow, Kubeflow, or MLflow.\n\nStreaming technologies: Exposure to real-time streaming architectures (e.g., Apache Kafka).","description_format":"text","description_chars":3062,"description_truncated":false,"requirements":{"experience_years_min":2,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":null,"security_clearance":false,"languages":[{"language":"English","level":"All levels","optional":false}]},"benefits":[],"hiring_locations":[{"name":"France","iso":"FR","kind":"country"}],"hiring_excludes":[],"relocation_offered":false,"industries":["Data Providers & Datasets","Supply Chain & Logistics Software","Financial Data & Market Intelligence"],"lifecycle":[{"event":"open","at":"2026-09-23T13:00:17Z"}],"visa":[],"liveness":{"score":78,"band":"hot","label":"Hiring now","p_open":1,"p_active":0.865,"p_room":0.9,"age_days":22,"expected_fill_days":57,"reasons":["conf:1","velocity","win:mid","comp:junior"],"computed_at":"2026-10-16T05:45:00Z"},"pay":null,"html_url":"https://alion.io/job/kpler-ml-engineer-power-2","json_url":"https://alion.io/job/kpler-ml-engineer-power-2.json","meta":{"generated_at":"2026-10-17T03:02:37Z","cache_seconds":300,"methodology":"https://alion.io/methodology","terms":"https://alion.io/terms","contact":"https://alion.io/contact","api":"https://alion.io/developers","about":"Alion is a live layer of people, companies and AI agents: who they are, whether they are real and active right now, what they do and how to work with them, readable by people and by agents and paid per call.","catalog":"https://alion.io/catalog.json","usage":{"tier":"crawler_verified","counted_by":"address","units_charged":1,"used_today":4065,"day_limit":null,"remaining_today":null,"minute_limit":300,"resets_at":"2026-10-18T00:00:00Z"}},"offers":[{"id":"company.slices","title":"One company in depth, by slice","status":"live","price":{"credits":0.02,"usd":0.002,"plus_per_slice":{"credits":0.05,"usd":0.005}},"unit":"per company, plus each slice with data","note":"the employer in depth","call":{"mcp_tool":"get_company","arguments":{"id":4764},"rest":"https://alion.io/mcp/rest/get_company?id=4764"},"human":"https://alion.io/catalog?offer=company.slices&for=job%2Fkpler-ml-engineer-power-2"},{"id":"market.stats","title":"A market slice: pay, demand and time to fill","status":"live","price":{"credits":1,"usd":0.1},"unit":"per slice","note":"pay, demand and time to fill for this role and place","call":{"mcp_tool":"market_stats"},"human":"https://alion.io/catalog?offer=market.stats&for=job%2Fkpler-ml-engineer-power-2"},{"id":"job.search","title":"Open jobs by role, technology, place, pay and visa","status":"live","price":{"credits":0.02,"usd":0.002},"unit":"per posting in a list","note":"similar open postings","call":{"mcp_tool":"search_jobs"},"human":"https://alion.io/catalog?offer=job.search&for=job%2Fkpler-ml-engineer-power-2"},{"id":"company.verify","title":"Is this company real and active right now","status":"pilot","price":null,"unit":"per company","request":{"url":"https://alion.io/catalog/request","method":"POST","body":"{\"offer\": \"company.verify\", \"for\": \"job/kpler-ml-engineer-power-2\", \"note\": \"what you need it for\"}"},"human":"https://alion.io/catalog?offer=company.verify&for=job%2Fkpler-ml-engineer-power-2"}]}