{"id":188451,"url":"https://alion.io/job/hark-on-device-ai-inference-engineer","title":"Embedded AI Engineer","company":{"id":764,"name":"Hark","domain":"hark.com","url":"https://alion.io/company/hark","size_band":"1001-5000","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Greenhouse","truth_index":{"grade":"A","score":95,"open_postings":45,"ghost_share":0,"stale_share":0,"repost_share":0,"time_to_fill_p50_days":113,"computed_at":"2026-10-17T05:46:00Z"}},"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 Jose, United States"],"countries":["US"],"hiring_countries":[],"hiring_countries_total":0,"salary":{"min":200000,"max":450000,"currency":"USD","period":"year","gross":null,"usd_annual":450000},"salary_estimate":null,"experience_years_min":2,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"AI Agents","optional":false},{"name":"C++","optional":false},{"name":"CNN","optional":false},{"name":"Edge AI","optional":false},{"name":"KV Cache","optional":false},{"name":"LiteRT","optional":false},{"name":"llama.cpp","optional":false},{"name":"LLM","optional":false},{"name":"Machine Learning","optional":false},{"name":"Multimodal AI","optional":false},{"name":"Rust","optional":false},{"name":"TFLite","optional":false},{"name":"TensorFlow","optional":true}],"status":"live","first_seen_at":"2026-09-02T00:51:45Z","employer_posted_date":"2026-09-25","last_verified_at":"2026-10-17T11:41:27Z","board_verified":true,"closed_at":null,"days_open":45,"trust":{"level":"ok","repost_count":0,"flags":["company_stale"],"days_open":45},"description":"About Hark\nHark is an artificial intelligence company building advanced, personalized intelligence. One that is proactive, multimodal, and capable of interacting with the world through speech, text, vision, and persistent memory.\nWe're pairing that intelligence with next-generation hardware to create a universal interface between humans and machines. While today's AI largely operates through chat boxes and decade-old devices, Hark is focused on what comes next: agentic systems that interact naturally with people and the real world.\nTo get there, we're developing multimodal models and next-generation AI hardware together - designed from the ground up as a single, unified interface for a new era of intelligent systems.\nAbout the Role\nAs an Embedded AI Engineer, you will work closely with the AI research team to bring AI to Hark’s next-gen hardware. You will be responsible for the full AI stack on the device, including data ingestion, model development, optimization, and deployment on embedded devices. You should have deep understanding of the constraints of an embedded system (compute, memory, power etc) and leverage your expertise in both embedded system software development and AI model deployment to deliver production-ready ML solutions on hardware\nResponsibilities\nBuild data collection and ingestion pipelines for an embedded system including various sensors, at scale\nWork closely with model teams to co-design model architectures that meets the required latency, memory, power, and bandwidth\nWork with platform vendors to bring up toolchains, SDKs and new accelerator to ensure efficient model deployment and optimization\nIntegrate ML inference into embedded firmware written in C, C++, or Rust\nProfile and optimize memory usage, power consumption, and real-time performance\nEvaluate and select silicon platforms (GPUs, NPUs etc.) for Hark’s next gen on-device and edge deployment of a wide range of models\nRequirements\n5 years of experience in machine learning engineering, with at least 2 years focused on embedded or edge ML\nFamiliarity with embedded systems, and CPU/DSP/NPU HW architectures\nHands-on experience with IMUs and other sensor types including accelerometers, gyroscopes, and microphones\nExperience building sensor data collection pipelines\nFamiliarity and experience with embedded ML run times (e.g. TFLite, llamacpp, QNN)\nExperience optimizing models for deployment on microcontrollers and edge processors such as ARM Cortex-M/A, RISC-V, and DSPs\nExperience deploying workloads on NPUs or specialized accelerators for embedded systems\nBonus Qualifications\nExperience with Audio/Voice/Vision models\nExperience with light weight LLM models\nUnderstand the performance characteristics of edge AI models, including CNN, RNN, transformers, KV-cache behavior, and their memory bandwidth requirements.\nExperience designing hybrid edge-LLM pipelines or integrating small language models on device\nPrior work on products in wearables, robotics, industrial sensing, or IoT\nCompensation\nThe US base salary range for this full-time position is between $200,000 - $450,000 annually.\nThe pay offered for this position may vary based on several individual factors, including job-related knowledge, skills, and experience. The total compensation package may also include additional components/benefits depending on the specific role. 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