{"id":3070831,"url":"https://alion.io/job/bose-machine-learning-engineer","title":"Machine Learning Engineer","company":{"id":1761362,"name":"Bose","domain":"bose.com","url":"https://alion.io/company/bose-com","size_band":"501-1000","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Workday","truth_index":{"grade":"B","score":79,"open_postings":6,"ghost_share":0,"stale_share":0.833,"repost_share":0,"time_to_fill_p50_days":58,"computed_at":"2026-10-24T06:02:35Z"}},"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":["Framingham, United States"],"countries":["US"],"hiring_countries":[],"hiring_countries_total":0,"salary":{"min":141000,"max":193950,"currency":"USD","period":"year","gross":null,"usd_annual":193950},"salary_estimate":null,"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"C++","optional":false},{"name":"Edge AI","optional":false},{"name":"Knowledge Distillation","optional":false},{"name":"Machine Learning","optional":false},{"name":"Model Distillation","optional":false},{"name":"Python","optional":false},{"name":"PyTorch","optional":false},{"name":"Quantization","optional":false},{"name":"TensorFlow","optional":false},{"name":"TensorFlow C++","optional":false},{"name":"Apache TVM","optional":true},{"name":"Function Calling","optional":true},{"name":"LiteRT","optional":true},{"name":"LLM","optional":true},{"name":"MLIR","optional":true},{"name":"Multimodal AI","optional":true},{"name":"ONNX Runtime","optional":true},{"name":"Post-training","optional":true}],"status":"live","first_seen_at":"2026-10-21T00:00:00Z","employer_posted_date":"2026-10-21","last_verified_at":"2026-10-22T14:01:38Z","board_verified":true,"closed_at":null,"days_open":3,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":3},"description":"At Bose Corporation, we believe sound is the most powerful force on earth - and for over 60 years, we have been a company built on innovation, excellence, and independence. Privately owned, fiercely customer-focused, and driven by our values, we continue to lead industries and transform lives through sound.\nToday, Bose Corporation is entering an exciting new era. Across multiple global Business Units and Global Functions, we are shaping the future of audio technology, automotive, luxury, and premium experiences. We invite you to join us in this transformation.\nJob Description\nAbout the Role\nBose is seeking a Machine Learning Engineer with expertise in Efficient AI and Edge ML to join our team. In this role, you will design, train, optimize, and deploy machine learning models for next-generation audio products. You will work closely with embedded, DSP, compiler, hardware, and software engineers to bring AI capabilities to resource-constrained devices.\nThe ideal candidate has strong machine learning fundamentals, practical experience with efficient model development, and an understanding of Edge ML deployment constraints. Deep embedded systems expertise is not required. However, you should be able to understand deployment and integration feedback, identify model-related limitations, and quickly iterate on model architectures and training methods to improve accuracy, latency, memory usage, and power efficiency.\nResponsibilities\nDesign, train, evaluate, and optimize machine learning models for consumer audio products.\nApply Efficient ML techniques, including quantization, pruning, knowledge distillation, model compression, and efficient model architectures, to improve model efficiency while maintaining model quality.\nWork closely with embedded, DSP, compiler, and hardware engineers to deploy and integrate machine learning models on resource-constrained platforms, including DSPs, NPUs, microcontrollers, and ARM-based systems.\nAnalyze deployment and integration feedback, identify model-related issues, and rapidly iterate on model architectures and training methodologies.\nAdapt models to address platform constraints such as unsupported operators, data-type limitations, memory usage, latency, compute requirements, and power consumption.\nProfile and evaluate model performance across accuracy, latency, memory footprint, model size, compute efficiency, and power usage.\nDevelop and maintain repeatable workflows for model training, conversion, compilation, simulation, validation, and performance analysis.\nCollaborate across Machine Learning, embedded software, DSP, hardware, compiler, and product engineering teams to deliver production-ready AI solutions.\nEvaluate emerging ML technologies and AI-assisted engineering tools that can improve model development, deployment, debugging, and optimization workflows.\nDocument experiments, technical findings, deployment learnings, and reusable engineering practices.\nMentor interns and co-op engineers.\nMinimum Qualifications\nM.S. or Ph.D. in Computer Science, Electrical Engineering, Computer Engineering, Machine Learning, or a related field.\nStrong foundation in machine learning and deep learning, including model architecture design, training, evaluation, and optimization.\nWorking knowledge of Efficient ML techniques such as quantization, pruning, knowledge distillation, model compression, and efficient model architectures.\nUnderstanding of how model optimization techniques affect model accuracy, latency, memory footprint, compute requirements, and deployment compatibility.\nProficiency with deep learning frameworks such as PyTorch, TensorFlow, or similar.\nStrong Python programming, debugging, and problem-solving skills.\nWorking knowledge of C/C++ and the ability to collaborate effectively with engineers working on embedded and production systems.\nUnderstanding of Edge ML concepts and common resource constraints, including computation, memory, latency, and power.\nAbility to analyze model behavior and troubleshoot issues across training, conversion, deployment, and integration workflows.\nStrong communication and cross-functional collaboration skills.\nPreferred Qualifications\nHands-on experience with quantization-aware training, post-training quantization, mixed-precision inference, structured or unstructured pruning, operator replacement, or hardware-aware model optimization.\nExperience adapting model architectures or training workflows in response to deployment and integration constraints.\nExperience deploying machine learning models on edge devices, DSPs, NPUs, mobile platforms, wearable devices, or other resource-constrained systems.\nFamiliarity with deployment frameworks and toolchains such as TensorFlow Lite, TensorFlow Lite Micro, ONNX Runtime, TVM, MLIR, or vendor-specific SDKs.\nUnderstanding of deployment and performance metrics such as latency, memory footprint, model size, MACs, memory bandwidth, and power consumption.\nExperience with audio ML, speech processing, acoustic sensing, or multimodal models involving audio.\nFamiliarity with LLM- or agent-based engineering workflows, including knowledge retrieval, tool calling, technical documentation analysis, structured data extraction, failure diagnosis, benchmark analysis, or optimization recommendations.\nExperience building or evaluating AI-assisted tools that improve ML development, deployment, debugging, or engineering productivity.\nCuriosity about the interaction between machine learning models, software toolchains, and hardware platforms.\nAt Bose, you're inspired to be and do your best and are rewarded for your unique talents! Our compensation is thoughtfully tailored to your skills, experience, education, and location, and goes beyond base salary. The hiring range for this position in the primary work location of Framingham, Massachusetts is: $141,000-$193,950.The hiring range for other Bose work locations may vary.\n\nIn addition to competitive base pay we offer rewards including bonus programs, comprehensive health and welfare benefits, a 401(k) plan, plus exclusive perks designed to support your wellbeing, and a generous employee discount where you can immerse yourself in our products and experiences. We are a proudly independent company-driven by purpose, guided by our values, and united by a belief in the power of sound. As the world leader in audio experiences, we’re creating what’s next-pushing boundaries and delivering transformative sound experiences for people everywhere. Join us and make your next career move a mic-drop. 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