{"id":2205676,"url":"https://alion.io/job/altos-labs-senior-scientist-machine-learning","title":"Senior Scientist, Machine Learning","company":{"id":449654,"name":"Altos Labs","domain":"altoslabs.com","url":"https://alion.io/company/altos-labs","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":85,"open_postings":5,"ghost_share":0,"stale_share":0.4,"repost_share":0,"time_to_fill_p50_days":73,"computed_at":"2026-10-13T05:45:00Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":"senior","employment_type":null,"work_mode":"hybrid","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["San Francisco, United States","San Diego, United States","Redwood City, United States"],"countries":["US"],"hiring_countries":[],"hiring_countries_total":0,"salary":{"min":200900,"max":257500,"currency":"USD","period":"year","gross":null,"usd_annual":257500},"salary_estimate":null,"experience_years_min":5,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Accelerate","optional":false},{"name":"DeepSpeed","optional":false},{"name":"Diffusion Models","optional":false},{"name":"FSDP","optional":false},{"name":"GraphRAG","optional":false},{"name":"Hugging Face","optional":false},{"name":"JAX","optional":false},{"name":"Knowledge Graph","optional":false},{"name":"Machine Learning","optional":false},{"name":"Megatron-LM","optional":false},{"name":"Mixture of Experts","optional":false},{"name":"Multimodal AI","optional":false},{"name":"Python","optional":false},{"name":"PyTorch","optional":false},{"name":"Ray","optional":false},{"name":"Transformers","optional":false},{"name":"In-Context Learning","optional":true},{"name":"Knowledge Distillation","optional":true},{"name":"Model Distillation","optional":true},{"name":"Quantization","optional":true}],"status":"live","first_seen_at":"2026-02-17T19:31:20Z","employer_posted_date":"2026-10-09","last_verified_at":"2026-10-13T16:38:36Z","board_verified":true,"closed_at":null,"days_open":237,"trust":{"level":"stale","repost_count":0,"flags":["stale"],"days_open":237},"description":"Our Mission\nOur mission is to restore cell health and resilience through cell rejuvenation to reverse disease, injury, and the disabilities that can occur throughout life.\nFor more information, see our website at altoslabs.com.\nOur Value\nOur Single Altos Value: Everyone Owns Achieving Our Inspiring Mission.\nDiversity at Altos\nAltos Labs has been named one of the Top 3 Biotech Companies and ranked for the second year on the Forbes 2026 Best Startups in America list. At Altos, exceptional scientists and industry leaders from around the world work together to advance a shared mission. Our intentional focus is on Belonging, so that all employees know that they are valued for their unique perspectives. We are all accountable for sustaining a diverse and inclusive environment.\nWhat You Will Contribute To Altos\nAs part of our team, you will help to accelerate and optimize our progress in developing unified, multi-modal generative foundation models for multiscale biology. You will be an integral part of our multidisciplinary teams building the computational platforms that will enable Altos to achieve its mission.\nIn this role, you will partner and collaborate with other multidisciplinary Scientists and Engineers across the Institute of Computation to design, build, and scale state-of-the-art foundation models that tackle biological questions and aid in the discovery of novel interventions for aging and disease. You will focus on the synthesis of unstructured multimodal signals with the structured relational data and knowledge graphs that represent biological reality.\nThe successful candidate will thrive in a fast-paced environment that stresses teamwork, transparency, scientific excellence, originality, and integrity.\nResponsibilities \n\nAs a Staff Machine Learning Scientist, you will use your experience to focus on designing, developing, and evaluating state-of-the-art foundation models, at scale, to benefit the research.\nPre-train and fine-tune large-scale machine learning systems using multimodal biological data, natural language, and structured relational inputs. \nArchitect and implement novel hybrid models that integrate Large Language Models (LLMs) with Graph Neural Networks (GNNs) for multi-hop reasoning over biological knowledge graphs .\nDevelop Relational Foundation Models (RFMs) that enable zero-shot predictive tasks over heterogeneous, multi-table biological datasets. \nLead the design of efficient data loading strategies and distributed training recipes (e.g., FSDP, DeepSpeed) to train models across multiple GPU nodes. \nGain insights into model performance based on theory, deep research, and the mathematical underpinnings of set-invariant and graph-structured architectures .\nApply strong coding experience to model development and deployment, ensuring research prototypes transition into reliable, scalable production systems.\nStay up-to-date on the latest developments in deep learning-including native early-fusion and Mixture-of-Experts (MoE) architectures-and apply this knowledge to Altos' research .\nMentor junior staff while maintaining a high individual technical contribution to the core research ecosystem and peer-reviewed publications.\nWho You Are \nWe are looking for someone who is:\n\nExcited about the Altos mission of restoring cell health and resilience to reverse disease, injury, and age-related disabilities.\nHighly collaborative in mindset and ways of working across research and engineering boundaries.\nSelf-motivated to drive and deliver on long-term technical projects and scientific goals.\nDemonstrates the desire to grow professionally and expand their skillset in biology, machine learning, and/or drug development.\nAble to communicate and explain the design, results, and impact of complex AI architectures to both scientific and non-scientific staff.\nKeen to contribute to seminars and scientific initiatives within Altos and the broader AI research community.\nMinimum Qualifications\nPhD in Computer Science, Machine Learning, or a similar quantitative field with 5+ years of relevant work experience in academic or industry settings.\nPrior experience in developing and implementing novel generative AI models, specifically in multimodal integration, GraphRAG, or relational deep learning .\nDeep understanding of Machine Learning principles and how they apply to diverse architectures like Transformers, GNNs, and diffusion models .\nVery strong programming skills in Python and deep learning libraries (e.g., PyTorch, JAX, Hugging Face Transformers/Accelerate). \nProven experience with multi-GPU and distributed training at scale (e.g., DDP, FSDP, DeepSpeed, Megatron, or Ray). \nStrong track record of published, peer-reviewed innovative AI/ML research at top-tier conferences (NeurIPS, ICML, ICLR, CVPR).\nPreferred Qualifications \n\nFamiliarity with tabular foundation models (e.g., TabPFN) and in-context learning strategies for structured data .\nSpecific experience in native multimodal modeling (early-fusion) or the synthesis of LLMs and Knowledge Graphs .\nTrack record of ML applied to biological data, such as NGS data (RNA-seq, ATAC-seq), biological imaging (microscopy, IF), or spatial transcriptomics.\nExperience in optimizing large-scale inference via quantization, distillation, or memory-efficient attention mechanisms.\nThe salary range for Redwood City, CA:\nScientist I, Machine Learning: $200,900 - $257,500\nScientist II, Machine Learning: $226,200 - $290,000\nSenior Scientist I, Machine Learning: $257,400 - $330,000\nThe salary range for San Diego, CA:\nScientist I, Machine Learning: $179,400 - $230,000\nScientist II, Machine Learning: $212,900 - $273,000\nSenior Scientist I, Machine Learning: $239,500 - $307,000\nExact compensation may vary based on skills, experience, and location.\nFor UK applicants, before submitting your application:\n- Please click here to read the Altos Labs EU and UK Applicant Privacy Notice (bit.ly/eu_uk_privacy_notice)\n- This Privacy Notice is not a contract, express or implied and it does not set terms or conditions of employment.\nEqual Opportunity Employment\nWe value collaboration and scientific excellence.\nWe believe that a culture of belonging are foundational to scientific innovation and inquiry. At Altos Labs, exceptional scientists and industry leaders from around the world work together to advance a shared mission. Our intentional focus is on Belonging, so that all employees know that they are valued for their unique perspectives. We are all accountable for sustaining an inclusive environment.\nAltos Labs provides equal employment opportunities to all employees and applicants for employment, without regard to race, color, religion, age, sex, national origin, disability status, genetics, protected veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by federal, state or local laws. Altos prohibits unlawful discrimination and harassment. This policy applies to all terms and conditions of employment, including recruiting, hiring, placement, promotion, termination, layoff, recall, transfer, leaves of absence, compensation and training.\nThank you for your interest in Altos Labs where we strive for a culture of scientific excellence, learning, and belonging.\nNote: Altos Labs will not ask you to download a messaging app for an interview or outlay your own money to get started as an employee. If this sounds like your interaction with people claiming to be with Altos, it is not legitimate and has nothing to do with Altos. 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