{"id":168896,"url":"https://alion.io/job/satsure-machine-learning-engineer-2","title":"Machine Learning Engineer - 2","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":64,"open_postings":28,"ghost_share":0.607,"stale_share":0,"repost_share":0,"time_to_fill_p50_days":null,"computed_at":"2026-10-16T05:45:00Z"}},"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":[],"countries":[],"hiring_countries":[],"hiring_countries_total":0,"salary":{"min":2000000,"max":2500000,"currency":"INR","period":"year","gross":null,"usd_annual":25808},"salary_estimate":null,"experience_years_min":4,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Amazon EC2","optional":false},{"name":"Amazon S3","optional":false},{"name":"AWS","optional":false},{"name":"Docker","optional":false},{"name":"Fine-tuning","optional":false},{"name":"GDAL","optional":false},{"name":"Hypothesis","optional":false},{"name":"Knowledge Distillation","optional":false},{"name":"KServe","optional":false},{"name":"Kubernetes","optional":false},{"name":"Machine Learning","optional":false},{"name":"MLFlow","optional":false},{"name":"Model Distillation","optional":false},{"name":"ONNX","optional":false},{"name":"Python","optional":false},{"name":"PyTorch","optional":false},{"name":"Quantization","optional":false},{"name":"TensorRT","optional":false},{"name":"Triton","optional":false},{"name":"CI/CD","optional":true},{"name":"CUDA","optional":true},{"name":"CUDA Toolkit","optional":true},{"name":"Time Series Forecasting","optional":true}],"status":"live","first_seen_at":"2026-07-24T09:11:04Z","employer_posted_date":"2026-07-24","last_verified_at":"2026-10-16T11:08:47Z","board_verified":true,"closed_at":null,"days_open":84,"trust":{"level":"ok","repost_count":0,"flags":["company_stale"],"days_open":83},"description":"SatSure is a global Earth intelligence company headquartered in India. Founded in 2017, SatSure owns the full Earth observation data value chain - from the upstream payload infrastructure to the foundational deep-tech and AI layers, to the downstream decision intelligence solutions. We work with private companies and government bodies across Agri-tech, Agri-banking, forestry, critical infrastructure, and aviation. Through our subsidiary KaleidEO, SatSure is building multispectral payloads for high-definition space imagery with edge computing capabilities, powering both sovereign and commercial applications.\nRole:\nWe are looking for a Machine Learning Engineer II to own a model workstream end-to-end - not a single model, but the family of models behind a product line, and the decisions that keep them accurate, fast, and affordable in production. You will be handed goals and ideas, not finished solutions: you decide how to run the experiments to test them, own the optimization and serving strategy, and defend your choices with benchmarks and clear findings. This is a hands-on role. You will still write the training loop and read the Triton logs.\nResponsibilities:\nExperiment Execution & Reporting: Own how a workstream's ideas get tested. Take the team's modelling proposals, turn them into well-run, reproducible training/fine-tuning experiments, monitor them, and report rigorous findings that decide what ships. You make pragmatic implementation choices; the research direction comes from Data Science.\n Model Optimization: Own the accuracy/latency/cost tradeoff for your models in production. Drive quantization (INT8/FP16), pruning, distillation, and ONNX/TensorRT export; profile GPU utilization and eliminate the bottleneck rather than guessing at it.\nPipelines at Scale: Design ML pipelines that survive real data - petabyte-scale satellite archives, missing tiles, sensor drift, inconsistent projections. Make them reproducible and cheap to re-run.\nProductionization: Prepare models for production and support their deployment on KServe / Triton alongside the Platform team. You should understand how modern serving frameworks work and what they demand of a model - batch sizing, concurrency, autoscaling behaviour, and failure modes under load - and hand over artifacts that account for them.\nEvaluation & Benchmarking: Define what \"good\" means before training starts - offline metrics, production SLOs, and the acceptance criteria a model must meet to ship.\nMonitoring & Drift: Own post-deployment model health. Instrument for drift, set thresholds, and drive the retraining decision rather than waiting to be told.\nTechnical Mentorship: Review code, models, and experiment design from MLEs/Data Scientists. Raise the floor of the team's engineering practice.\nQualification:\n4-7 years of relevant experience as a Machine Learning Engineer or in an applied research/engineering role.\nMandatory: Deep hands-on PyTorch expertise - custom datasets, distributed/multi-GPU training, mixed precision, and inference optimization. You can read a PyTorch profiler trace and act on it.\nMandatory: Multiple models shipped to production and kept in production, with evidence of measured latency/throughput improvements.\nMandatory: Hands-on experience training, fine-tuning, debugging, optimizing, and productionizing modern deep-learning architectures, including CNNs, vision transformers, and vision foundation models such as DINO- and SAM-style models. You are comfortable reading unfamiliar model implementations, adapting them to new use cases, and improving their training efficiency, inference performance, and production reliability.\nMandatory: Experience running experiments to a defined hypothesis with minimal supervision - and reporting findings others could act on.\nBachelor's degree in Computer Science, IT, Statistics, or a related field; non-IT degrees with strong relevant experience are acceptable.\nMust-have skills:\nML Engineering Depth: You have debugged the training instability, found the data leak, and traced the production regression back to a preprocessing change. You know where models break in the real world.\nTraining at Scale: Confident running distributed / multi-GPU training and fine-tuning jobs efficiently and reproducibly, and instrumenting them so the findings are trustworthy.\nOptimization Fluency: Quantization, distillation, ONNX/TensorRT, and batching/concurrency tuning at the serving layer. You can quantify what each technique bought you.\nFeature & Data Pipelines: Scalable pipelines over raster/vector geospatial formats; comfortable with Rasterio/GDAL, tiling strategies, and the failure modes of remote sensing data.\nPython & Engineering Practice: Clean, tested, maintainable code. You write ML code other engineers can pick up.\n Containerization & Cloud: Confident with Docker and AWS (S3, EC2, ECR); you can reason about GPU instance selection and the cost of your own training runs.\nServing Infrastructure: Familiarity with KServe, Triton, or an equivalent model-serving stack - enough to understand how your model will be served and to work effectively with the team that runs it.\nExperimentation & Versioning : MLflow (or equivalent) used properly - reproducible experiments, a model registry others can trust, and lineage from data to deployed artifact.\n Kubernetes (User Level): Submitting GPU jobs, reading pod logs, reasoning about resource requests and limits.\nGood-to-have:\nBackground in geospatial or remote sensing ML (satellite imagery, SAR, multispectral, time-series of Earth observation data).\nKernel-level bottleneck analysis and the ability to quantify what it bought you.\nCI/CD for ML - automated training triggers, evaluation gates, and progressive rollout of models.\nCUDA-level debugging and custom kernel awareness.\nExperience with distributed training frameworks and large-scale data loading optimization.\nExposure to cost optimization for GPU workloads (spot strategy, right-sizing, inference cost per prediction).\nCompetencies:\nOwnership: You own the outcome, not the artifact. If the model is slow in production, you drive the diagnosis and the fix with whoever owns the infrastructure it runs on.\nJudgement: You know how to run an idea cheaply enough to get a signal fast, and when a negative result is conclusive. You do not over-engineer.\nScientific Rigor: Hypothesis-driven experimentation with results others can reproduce from your tracking and write-ups alone.\nCollaboration & Influence: You can explain a tradeoff to a Data Scientist, a Platform engineer, and a domain expert - and get all three to agree on a path.\nMentorship: You make the engineers around you better through review, pairing, and clear technical writing.\nInterview Process:\nIntro call\nTake-home assessment (focus on model training, optimization & deployment)\nInterview rounds (ideally up to 3 rounds, including a deep dive on a model you have shipped and an ML system design discussion)\nCulture round / HR round","description_format":"text","description_chars":6974,"description_truncated":false,"requirements":{"experience_years_min":4,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":{"level":"bachelor","optional":false},"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":10,"band":"cold","label":"Long shot","p_open":1,"p_active":0.351,"p_room":0.28,"age_days":83,"expected_fill_days":32,"reasons":["conf:3","stale_co","velocity","win:tail","crowd:"],"computed_at":"2026-10-16T05:45:00Z"},"pay":{"stated_usd_annual":25808,"is_top_pay":false},"html_url":"https://alion.io/job/satsure-machine-learning-engineer-2","json_url":"https://alion.io/job/satsure-machine-learning-engineer-2.json","meta":{"generated_at":"2026-10-16T12:51:00Z","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":12110,"day_limit":null,"remaining_today":null,"minute_limit":300,"resets_at":"2026-10-17T00:00:00Z"}},"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":59505},"rest":"https://alion.io/mcp/rest/get_company?id=59505"},"human":"https://alion.io/catalog?offer=company.slices&for=job%2Fsatsure-machine-learning-engineer-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%2Fsatsure-machine-learning-engineer-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%2Fsatsure-machine-learning-engineer-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/satsure-machine-learning-engineer-2\", \"note\": \"what you need it for\"}"},"human":"https://alion.io/catalog?offer=company.verify&for=job%2Fsatsure-machine-learning-engineer-2"}]}