{"id":3359132,"url":"https://alion.io/job/42dot-machine-learning-engineer-driving-foundation-models","title":"Machine Learning Engineer, Driving Foundation Models","company":{"id":256,"name":"42dot","domain":"42dot.ai","url":"https://alion.io/company/42dot","size_band":"201-500","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Ashby","truth_index":{"grade":"B","score":83,"open_postings":16,"ghost_share":0,"stale_share":0.688,"repost_share":0,"time_to_fill_p50_days":43,"computed_at":"2026-10-29T05:49:05Z"}},"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":["Sunnyvale, United States"],"countries":["US"],"hiring_countries":[],"hiring_countries_total":0,"salary":{"min":175000,"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":"AWS","optional":false},{"name":"Azure","optional":false},{"name":"C++","optional":false},{"name":"DeepSpeed","optional":false},{"name":"Diffusion Models","optional":false},{"name":"Docker","optional":false},{"name":"DPO","optional":false},{"name":"GCP","optional":false},{"name":"Go","optional":false},{"name":"Hallucination","optional":false},{"name":"Hugging Face","optional":false},{"name":"JAX","optional":false},{"name":"Kubernetes","optional":false},{"name":"LLM Guardrails","optional":false},{"name":"Megatron-LM","optional":false},{"name":"Physical AI","optional":false},{"name":"Post-training","optional":false},{"name":"Python","optional":false},{"name":"PyTorch","optional":false},{"name":"Quantization","optional":false},{"name":"Reinforcement Learning","optional":false},{"name":"Reinforcement Learning","optional":false},{"name":"RLHF","optional":false},{"name":"Speculative Decoding","optional":false},{"name":"Synthetic Data","optional":false},{"name":"TensorFlow","optional":false},{"name":"TensorFlow C++","optional":false},{"name":"Tokenization","optional":false},{"name":"TPU","optional":false},{"name":"Transformers","optional":false},{"name":"Vision-Language-Action","optional":false},{"name":"vLLM","optional":false}],"status":"live","first_seen_at":"2026-10-27T17:25:12Z","employer_posted_date":"2026-10-27","last_verified_at":"2026-10-29T12:31:37Z","board_verified":true,"closed_at":null,"days_open":1,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":1},"description":"We are looking for the best\nAbout Us\n42dot is a mobility AI company committed to solving mobility challenges with software and AI. As the Global Software Center of Hyundai Motor Group, 42dot pioneers the future of mobility by advancing the development of software-defined vehicles.\nWe develop safety-first, user-centric software-defined vehicle technologies that deliver the latest performance through continuous updates like smartphones. By advancing software and AI technology, 42dot envisions a world where everything is connected and moves autonomously through a self-managing urban transportation operating system.\nAbout the Role\nDevelop driving and physical AI foundation models. Support Aria VLA model development.\nResponsibilities\nData Curation & Pretraining: Design and manage massive, multi-terabyte datasets, including data scraping, filtering, deduplication, tokenization, and synthetic data generation.\n\nDistributed Model Training: Scale training across massive GPU/TPU clusters using parallel processing, custom data loaders, and framework optimization (PyTorch, JAX, or TensorFlow).\n\nPost-Training & Alignment: Implement instruction tuning, Reinforcement Learning from Human Feedback (RLHF), or direct preference optimization to align models with safety and behavior goals.\n\nInference & Optimization: Compress and optimize models (quantization, pruning, speculative decoding) to run at low latency on cloud infrastructure or edge hardware like smartphones and robotics.\n\nEvaluation & Guardrails: Build benchmarking harnesses, hallucination checkers, and red-teaming pipelines to test model accuracy and safety.\n\nQualifications\nLanguages: Python, C++, and Golang (for high-throughput systems).\n\nFrameworks: PyTorch, JAX, Hugging Face ecosystem, vLLM.\n\nInfrastructure: Kubernetes, Docker, multi-node distributed training (Megatron-LM, DeepSpeed), and cloud platforms (AWS, Azure, Google Cloud).\n\nMath & Theory: Linear algebra, calculus, probability, statistics, and deep learning architectures (Transformers, diffusion models).\n\nInterview Process\nApplication Review - Coding Test - 1st interview - 2nd interview - Offer Negotiation - Hiring\n\nThe screening procedures may vary depending on the position, schedule, or other circumstances.\nYou will be individually notified of the screening schedule and results via the email address provided in your application.\n\nCompensation\n$175,000 - $250,000\n\nAdditional Information\nIn accordance with fair hiring practices, do not include any personal information unrelated to your job qualifications (e.g., Social Security Number, family relations, marital status, age, photo, physical condition, place of birth, etc.) in your resume.\n\nAll documents must be submitted in PDF format and under 30MB in size.\n\nIf you experience issues uploading your resume, please send it along with the job posting URL to .\n\nWe strongly encourage applications from U.S. veterans and candidates eligible for employment preference under applicable laws.\n\nQualified individuals with disabilities are encouraged to apply and will receive consideration under the Americans with Disabilities Act (ADA).\n\n42dot does not accept unsolicited resumes and will not pay fees for any such submissions. Equal Opportunity Statement\n\n42dot is an Equal Opportunity Employer. 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