{"id":3716006,"url":"https://alion.io/job/true-talent-solutions-partners-founding-ml-research-engineer-3","title":"Founding ML Research Engineer","company":{"id":4415660,"name":"True Talent Solutions Partners","domain":"truetalentsolutionspartners.com","url":"https://alion.io/company/true-talent-solutions-partners","size_band":"11-50","is_staffing_agency":true,"employer_type":"agency","is_intermediary":false,"listed_via":null,"ats_vendor":"Ashby","truth_index":null},"role":"AI/ML","role_family":"AI/ML","seniority":"junior","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":230000,"currency":"USD","period":"year","gross":null,"usd_annual":230000},"salary_estimate":null,"experience_years_min":2,"visa_sponsorship":true,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Computer Vision","optional":false},{"name":"Docker","optional":false},{"name":"Edge AI","optional":false},{"name":"Fine-tuning","optional":false},{"name":"Function Calling","optional":false},{"name":"Hugging Face","optional":false},{"name":"LLM Evaluation","optional":false},{"name":"Machine Learning","optional":false},{"name":"Multimodal AI","optional":false},{"name":"ONNX","optional":false},{"name":"Python","optional":false},{"name":"PyTorch","optional":false},{"name":"Quantization","optional":false},{"name":"Ray Serve","optional":false},{"name":"RLHF","optional":false},{"name":"SFT","optional":false},{"name":"TensorRT","optional":false},{"name":"Transformers","optional":false},{"name":"Triton","optional":false},{"name":"vLLM","optional":false},{"name":"Ray","optional":true}],"status":"closed","first_seen_at":"2026-11-04T00:15:30Z","employer_posted_date":"2026-11-04","last_verified_at":"2026-11-10T01:51:07Z","board_verified":false,"closed_at":"2026-11-10T01:51:07Z","days_open":6,"trust":{"level":"not_scored","repost_count":null,"flags":[],"days_open":6},"description":"About the Role\nJoin an early-stage industrial AI startup as a founding engineer building the multimodal AI system for wearable assistants used in field operations. You will own key parts of the production vision-language stack, from model orchestration and evaluation to deployment on real-world hardware.\nWhat You'll Do\nBuild production agentic vision-language pipelines that support multi-step visual reasoning and tool use in industrial workflows.\n\nDevelop evaluation frameworks, capture model failures, and create data and fine-tuning loops to improve model quality.\n\nAdapt model orchestration and edge inference to variable connectivity, latency, and deployment constraints.\n\nCreate real-time voice and video interfaces, including conversational and proactive assistance features.\n\nFine-tune and optimize open-source multimodal models for enterprise and on-premise deployments.\n\nWhat We're Looking For\nTwo to six years of experience shipping production vision-language or multimodal AI systems, with ownership of model and orchestration components.\n\nStrong Python and PyTorch skills, plus experience with tools such as vLLM, Triton, Ray Serve, ONNX, TensorRT, Hugging Face Transformers, Docker, and quantization methods.\n\nApplied experience with supervised fine-tuning, RLHF, evaluation design, model serving, and deployment; pretraining from scratch is not the focus.\n\nExperience building multi-step agents with real tool use, evaluation harnesses, and data improvement loops.\n\nA record of shipping AI products for end users and taking ownership of product outcomes and technical decisions.\n\nBackground in computer science, machine learning, or engineering, with strong fundamentals; startup or other fast-paced product experience is valuable.\n\nInterest or experience in computer vision, wearables, industrial AI, edge inference, or on-premise model deployment.\n\nCompensation & Benefits\nBase salary is $180,000 to $230,000 annually. 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