{"id":3307715,"url":"https://alion.io/job/techchain-talent-machine-learning-engineer","title":"Machine Learning Engineer","company":{"id":4476923,"name":"TechChain Talent","domain":"techchaintalent.xyz","url":"https://alion.io/company/tech-chain-talent","size_band":"11-50","is_staffing_agency":true,"employer_type":"agency","is_intermediary":false,"listed_via":null,"ats_vendor":"Schema","truth_index":{"grade":"B","score":75,"open_postings":10,"ghost_share":0,"stale_share":1,"repost_share":0,"time_to_fill_p50_days":null,"computed_at":"2026-10-31T05:58:55Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":null,"employment_type":"full_time","work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"explicit","locations":["New York, United States"],"countries":["US"],"hiring_countries":[],"hiring_countries_total":0,"salary":{"min":400000,"max":600000,"currency":"USD","period":"year","gross":null,"usd_annual":600000},"salary_estimate":null,"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"A/B Testing","optional":false},{"name":"Machine Learning","optional":false},{"name":"Python","optional":false},{"name":"PyTorch","optional":false},{"name":"Recommender Systems","optional":false},{"name":"Reinforcement Learning","optional":false},{"name":"TensorFlow","optional":false}],"status":"live","first_seen_at":"2026-10-07T15:53:01Z","employer_posted_date":"2026-10-07","last_verified_at":"2026-10-31T04:43:42Z","board_verified":true,"closed_at":null,"days_open":23,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":23},"description":"Machine Learning Engineer\nLocation: New York, US\nSalary: $400K - $600K\nAs a Machine Learning Engineer, you’ll work alongside engineering and product teams to build algorithms that power personalisation, recommendations, search and real-time decision-making. Your primary focus will be recommendation systems, with opportunities to apply machine learning to fraud detection and content moderation.\nYou’ll take projects from initial problem definition through to deployment and iteration, owning the models, data pipelines and supporting infrastructure needed to deliver results. Design, develop and deploy recommendation models that improve discovery, personalisation, engagement and retention. Build scalable data pipelines and use large datasets to develop features, train models and inform product development.\nDevelop and optimise machine learning infrastructure, ensuring training and inference systems are scalable, efficient and reliable in production. Work closely with product teams to integrate models into the platform, supporting low-latency inference and real-time data processing. Design experiments and combine offline evaluation with A/B testing to measure impact and guide improvements.\nExperiment with techniques across deep learning, natural language processing and reinforcement learning where they can solve meaningful product problems. Monitor, optimise and iterate on deployed models as user behaviour, data and product needs evolve. Develop models for fraud detection, abuse prevention and content moderation to improve platform quality and user experience.\nCollaborate with engineers and data scientists to solve challenges involving high-volume user and transactional data. Identify useful developments in ML research and turn them into practical improvements. Who You Are An experienced Machine Learning Engineer with a strong foundation in algorithms, data structures and statistical modelling.\nExperienced in building and deploying recommendation systems in production, with measurable improvements to user experience or business outcomes. This is our highest-priority area of experience. Familiar with retrieval, ranking and personalisation techniques, including embeddings, candidate generation and challenges such as cold starts and sparse data.\nProficient in Python and frameworks such as TensorFlow or PyTorch, with strong software engineering skills and hands-on experience deploying models. Skilled in building and maintaining ML infrastructure, including data pipelines, model deployment, monitoring and systems that scale to large datasets. Comfortable working across the entire ML lifecycle, from research and experimentation to deployment and maintenance.\nExperienced working in startups, delivering at a high pace through ambiguity, shifting priorities and limited resources. Hands-on and resourceful, willing to build the models, pipelines and infrastructure yourself to get a solution into production. High-agency and ownership-driven, capable of identifying problems, making decisions and leading projects end to end with minimal guidance.\nHighly collaborative, with the ability to communicate technical concepts clearly to engineers, product teams and non-technical stakeholders. Experience with production fraud detection, abuse prevention or content moderation systems will be highly advantageous. 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