{"id":168904,"url":"https://alion.io/job/satsure-ml-researcher-foundation-models","title":"ML Researcher – Foundation Models","company":{"id":59505,"name":"SatSure","domain":"satsure.co","url":"https://alion.io/company/satsure","size_band":"51-200","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Keka","truth_index":{"grade":"C","score":58,"open_postings":27,"ghost_share":0.704,"stale_share":0,"repost_share":0,"time_to_fill_p50_days":null,"computed_at":"2026-10-31T05:58:55Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":"middle","employment_type":"full_time","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":24000,"max_usd":58000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":32},"experience_years_min":3,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Contrastive Learning","optional":false},{"name":"DeepSpeed","optional":false},{"name":"Fine-tuning","optional":false},{"name":"FSDP","optional":false},{"name":"Mamba","optional":false},{"name":"Multimodal AI","optional":false},{"name":"Segment Anything","optional":false},{"name":"Self-Supervised Learning","optional":false},{"name":"Time Series","optional":false},{"name":"CUDA","optional":true},{"name":"JAX","optional":true},{"name":"Mixture of Experts","optional":true},{"name":"PyTorch","optional":true}],"status":"live","first_seen_at":"2026-06-19T05:56:34Z","employer_posted_date":"2026-06-19","last_verified_at":"2026-10-31T10:27:46Z","board_verified":true,"closed_at":null,"days_open":134,"trust":{"level":"ghost","repost_count":0,"flags":["stale","company_stale"],"days_open":133},"description":"About SatSure\nSatSure is a deep tech, decision intelligence company working at the nexus of agriculture, infrastructure, and climate action - creating impact for the other millions, with a focus on the developing world. As part of this mission, we're building geospatial foundation models that learn directly from Earth observation data - optical, SAR, and elevation - at scale. This role sits at the heart of that effort: architecting and training large-scale models that can generalize across geographies, sensors, and time. You'll be shaping the core intelligence layer that powers insights for millions, not just fine-tuning someone else's model.\nRole\nYou will be the architect of the model’s latent space, designing foundation models for multi-spectral, multi-temporal, and multi-resolution geospatial data.\nThis is a hands-on role involving prototyping, experimentation, and large-scale training. You will work across representation learning, model scaling, and spatiotemporal modeling to build systems that generalize across sensors, geographies, and time.\nKey Responsibilities\nRepresentation Learning\nDesign and implement self-supervised learning (SSL) objectives (e.g., Masked Autoencoders, DINO-style methods, contrastive learning) tailored for geospatial data\nDevelop multi-modal representations spanning optical, SAR, elevation, and derived signals\nEnsure representations transfer effectively across tasks such as segmentation, classification, and change detection\nDesign evaluation strategies to measure generalization across geographies, sensors, and time\nModel Development & Scaling\nDesign and scale models based on Vision Transformers (ViT), hybrid architectures, or State Space Models (e.g., Mamba) to large parameter regimes\nApply modern training techniques such as RMSNorm, FlashAttention, mixed precision, and gradient checkpointing\nRun scaling experiments, ablations, and architecture explorations grounded in empirical rigor\nLeverage insights from scaling behavior to make compute-efficient decisions across model size, data, and training strategy\nTemporal Dynamics\nDevelop methods to model time-series satellite data, capturing:Seasonal patterns\nTemporal dependencies\nLong-term land-use changes\n\nExplore sequence modeling, memory mechanisms, and temporal tokenization strategies\nSystems-Level Thinking\nDesign ML systems as end-to-end pipelines (data ingestion → curation → training → evaluation → deployment → feedback)\nMake explicit trade-offs between model quality, latency, cost, and data freshness\nWork with platform teams to optimize:Distributed training (FSDP, DeepSpeed)\nGPU utilization\nData pipelines and experiment throughput\n\nBuild reusable components and abstractions, not one-off models\nPreferred Background\nExperience\n3-5 years of experience in ML research or applied research roles\nExperience in large-scale foundation model development (vision, multimodal, speech, or related domains)\nExperience training and/or fine-tuning billion-parameter models\nExperience working with sequence, video, or temporal data\nExposure to geospatial foundation models, such as:Prithvi\nClay\nSegment Anything Model (SAM) (nice to have)\n\nTechnical Skills\nExpert-level proficiency in PyTorch or JAX\nStrong experience with:Distributed training (FSDP / DeepSpeed)\nLarge-scale datasets and training pipelines\n\nFamiliarity with transformer architectures and training dynamics\nBonus: CUDA / performance optimization experience\nAdditional Strengths\nFamiliarity with efficient scaling techniques (e.g., Mixture of Experts) is a plus\nStrong experimental rigor and ability to design meaningful ablations\nTrack record of publishing or contributing to state-of-the-art research in representation learning or generative modeling\nBenefits:\nMedical Health Cover for you and your family including unlimited online doctor consultations\nAccess to mental health experts for you and your family\nDedicated allowances for learning and skill development\nComprehensive leave policy with casual leaves, paid leaves, marriage leaves, bereavement leaves\nInterview Process:\nIntro call\nAssessment\nPresentation\nInterview rounds (ideally up to 3-4 rounds)\nCulture Round / HR round","description_format":"text","description_chars":4164,"description_truncated":false,"requirements":{"experience_years_min":3,"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":["Geospatial Analytics","AgTech","Earth Observation","Geospatial AI"],"lifecycle":[{"event":"open","at":"2026-08-29T20:50:04Z"}],"visa":[],"liveness":{"score":6,"band":"cold","label":"Long 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