{"id":1062412,"url":"https://alion.io/job/constellation-research-engineer","title":"Research Engineer","company":{"id":456068,"name":"Constellation Systems","domain":"constellationlab.io","url":"https://alion.io/company/constellation-4","size_band":null,"is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Ashby","truth_index":null},"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":"structured","locations":["San Francisco, United States"],"countries":["US"],"hiring_countries":[],"hiring_countries_total":0,"salary":{"min":180000,"max":250000,"currency":"USD","period":"year","gross":null,"usd_annual":250000},"salary_estimate":null,"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"ClearML","optional":false},{"name":"FSDP","optional":false},{"name":"LLM Evaluation","optional":false},{"name":"MLFlow","optional":false},{"name":"Multimodal AI","optional":false},{"name":"Python","optional":false},{"name":"PyTorch","optional":false},{"name":"PyTorch C++","optional":false},{"name":"Time Series Forecasting","optional":false},{"name":"Weights & Biases","optional":false},{"name":"C++","optional":true},{"name":"CUDA","optional":true},{"name":"CUDA Toolkit","optional":true},{"name":"Edge AI","optional":true},{"name":"Knowledge Distillation","optional":true},{"name":"Model Distillation","optional":true},{"name":"ONNX","optional":true},{"name":"Quantization","optional":true},{"name":"Rust","optional":true},{"name":"TensorRT","optional":true},{"name":"Triton","optional":true}],"status":"live","first_seen_at":"2026-09-18T22:35:25Z","employer_posted_date":"2026-09-18","last_verified_at":"2026-10-20T06:04:19Z","board_verified":true,"closed_at":null,"days_open":31,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":31},"description":"About Us\nConstellation is creating the AI-human translation layer that ensures humanity evolves alongside our technology. Our mission is to leverage AI towards addressing deep and meaningful problems at the core of the human experience: empowering people towards their goals, augmenting our cognition and emotional wellness, and understanding ourselves and each other. Our path forward is to move away from AI that captures human knowledge towards AI that truly understands what it is to be human. We are generating the richest multimodal dataset ever collected to build a new class of foundation models and we're seeking the team of researchers that will build them.\nThe Role\nWe're looking for a Research Engineer to sit between our data and our models and make the whole loop faster. You will orchestrate and optimize training runs on long-horizon multimodal sequences, build the pipelines that turn a messy, daily-growing corpus into something our models can learn from, and write the research code (libraries, dataloaders, evaluation harnesses) that lets the rest of the team try ideas quickly and trust what they see. You'll work closely with both our engineering and research teams.\nResponsibilities\nOrchestrate and optimize training: distributed configuration (DDP/FSDP), mixed precision, checkpointing and recovery on multi-day runs, and profiling to find which of kernel, dataloader, communication or pipeline is actually the bottleneck before touching anything.\n\nBuild high-throughput data loading over TB-scale multimodal data: sharding, prefetching, caching and format choices that keep GPUs saturated rather than waiting on I/O.\n\nWrangle complex data: align and synchronize multi-stream time series, handle changing collection protocols, and own dataset versioning and lineage as the corpus grows.\n\nBuild pipelines that derive rich features from raw recordings so every derived dataset is versioned, reproducible from its inputs, and cheap to recompute when upstream data changes.\n\nWrite research code that makes research better: clean, well-tested, well-documented libraries for datasets, models, transforms and metrics that the team builds on rather than around.\n\nMake every run traceable: code version, config, dataset version and environment recoverable from any result.\n\nBuild evaluation harnesses that run automatically on new checkpoints and surface regressions before anyone goes looking.\n\nTurn research prototypes into repeatable pipelines without flattening the flexibility researchers need to keep exploring.\n\nQualifications\n3+ years building ML systems or research infrastructure, including distributed training runs on 100+ GPUs.\n\nExpert-level Python and deep knowledge of PyTorch internals: DDP and FSDP, mixed precision, gradient accumulation, and the profiling tools to tell a slow model from a starved one.\n\nA track record of building or maintaining research libraries others depend on. Contributions to packages like torch_geometric, torchaudio, torcheeg, torch_brain, neuralsets, or comparable internal tooling are exactly what we're looking for.\n\nExperience with data pipelines over large unstructured and multimodal datasets, and familiarity with columnar and streaming formats (Zarr, Parquet, Arrow, Lance, WebDataset, Vortex) and the tradeoffs between random access and sequential throughput.\n\nHands-on experience with experiment tracking and dataset versioning tooling (ClearML, Weights & Biases, MLflow, or similar).\n\nEnough research fluency to read a paper, reimplement a component, and tell whether a loss curve is broken.\n\nDebugging range that spans everything from a corrupted shard to a silently wrong collate function to a training run that quietly diverged on day two.\n\nYou care about the well-being of the people this technology touches, and you thrive in a high-bandwidth, collaborative environment.\n\nNice to Have\nCustom kernel work in CUDA or Triton, or compiler-level optimization (torch.compile, TensorRT, ONNX).\n\nExperience with time-series or multimodal data where synchronization across streams is a prerequisite for analysis and learning.\n\nLow-latency or edge inference, quantization, or distillation.\n\nComfort dropping into Rust or C++ when Python is the wrong tool.\n\nInterest in neurotech, mental health, or human-AI interaction.\n\nBenefits\nComprehensive, high-quality health, dental, and vision insurance with premiums fully covered.\n\nA renovated, light-filled office with a quirky layout in the heart of San Francisco's Mission District, surrounded by world-class food and coffee.\n\nRelocation assistance for those joining us in SF and workspace setup stipend.\n\nMonthly team dinners and outings, plus twice-yearly off-sites and retreats.\n\nFully stocked kitchen (snacks!)\n\nSponsorship for travel to relevant conferences and regular professional development activities to help you level up.\n\nA minimum of 12 weeks of fully paid parental leave with a “soft-landing” transition back.\n\nPaid time off (PTO)","description_format":"text","description_chars":4956,"description_truncated":false,"requirements":{"experience_years_min":null,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":null,"security_clearance":false,"languages":[]},"benefits":["Parental leave","Professional development","Relocation assistance","Vision insurance"],"hiring_locations":[],"hiring_excludes":[],"relocation_offered":true,"industries":["IT Consulting & Digital Transformation","Custom Software 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