{"id":955702,"url":"https://alion.io/job/neander-lead-ml-scientist","title":"Lead ML Scientist","company":{"id":714439,"name":"Neander","domain":"neander.ai","url":"https://alion.io/company/neander-2","size_band":"11-50","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":null,"truth_index":null},"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":["Bengaluru, India"],"countries":["IN"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":28000,"max_usd":75000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":47},"experience_years_min":8,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Machine Learning","optional":false},{"name":"Reinforcement Learning","optional":false},{"name":"AI Agents","optional":true},{"name":"arXiv","optional":true}],"status":"live","first_seen_at":"2026-09-16T05:34:39Z","employer_posted_date":null,"last_verified_at":"2026-09-16T05:34:39Z","board_verified":false,"closed_at":null,"days_open":49,"trust":{"level":"not_scored","repost_count":null,"flags":[],"days_open":49},"description":"We are looking for a Lead ML Scientist who can take ownership of building and scaling our machine learning function from the ground up. The ideal candidate should have strong hands-on expertise in machine learning, reinforcement learning (RL), feedback loops, ML infrastructure, and modern AI/ML pipelines. You will work across the full ML lifecycle, from core model development and data collection to experimentation, deployment, feedback loops, and building the ML team and infrastructure. This is a 01 leadership role for someone who has experience evolving ML practices from traditional/classical ML approaches toward modern AI and ML pipelines.\nResponsibilities:\nOwn the 01 setup and evolution of the ML practice, including processes, infrastructure, tooling, and team.\nDesign and implement reinforcement learning (RL), feedback loops, and learning systems for continuous model improvement.\nWork hands-on with core ML problems, including model development, experimentation, evaluation, optimization, and deployment.\nBuild and scale ML infrastructure for data collection, processing, training, experimentation, evaluation, and model usage.\nEstablish effective data collection and feedback mechanisms to continuously improve ML models.\nWork closely with ML engineers, data scientists, platform engineers, and product/engineering teams to build production-grade ML systems.\nDrive the transition from classical/traditional ML approaches to modern AI/ML pipelines.\nDefine ML architecture, best practices, experimentation frameworks, and model evaluation methodologies.\nHelp build and mentor a high-performing ML team, including hiring and defining team structure.\nIdentify new ML/AI techniques and translate relevant research into practical product capabilities.\nEstablish processes for model monitoring, feedback, experimentation, and continuous improvement.\nCollaborate with leadership to define the long-term ML roadmap and technical strategy.\nRequirements:\n8+ years of experience in machine learning / AI/data science, with significant hands-on ML experience.\nStrong understanding and practical experience with reinforcement learning (RL), feedback loops, or learning-from-feedback systems.\nStrong full-stack ML expertise from core ML/model development to ML platforms, infrastructure, deployment, and monitoring.\nExperience working on or leading a 01 ML function/practice within a company.\nExperience setting up or scaling ML infrastructure and data pipelines for collecting, processing, and utilizing data for model improvement.\nStrong understanding of ML system design and productionization.\nExperience transitioning or working across classical ML and modern AI/ML approaches.\nAbility to operate as a hands-on technical leader, rather than purely as a people manager.\nExperience collaborating with or leading teams across ML Engineering, ML Platform, Data Engineering, and Product/Engineering.\nStrong problem-solving and research-oriented mindset.\nStrong Signals / Preferred Qualifications:\nExperience at a strong AI/ML-focused company or a highly recognized technology organization.\nStrong academic background, preferably from top-tier institutes such as IITs, with strong academic performance.\nResearch publications/papers on arXiv or other credible ML/AI research contributions.\nExperience building ML systems involving RL, human feedback, behavioral feedback, recommendation/optimization loops, or continuous learning.\nExperience building ML 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