{"id":1261060,"url":"https://alion.io/job/manifold-bio-aiml-research-engineer","title":"AI/ML Research Engineer","company":{"id":1883364,"name":"Manifold Bio","domain":"manifold.bio","url":"https://alion.io/company/manifold-bio","size_band":"11-50","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Greenhouse","truth_index":{"grade":"B","score":75,"open_postings":5,"ghost_share":0,"stale_share":1,"repost_share":0,"time_to_fill_p50_days":null,"computed_at":"2026-10-02T05:45:00Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":"junior","employment_type":null,"work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Boston, United States"],"countries":["US"],"hiring_countries":[],"hiring_countries_total":0,"salary":{"min":140000,"max":225000,"currency":"USD","period":"year","gross":null,"usd_annual":225000},"salary_estimate":null,"experience_years_min":2,"visa_sponsorship":false,"relocation_package":false,"has_equity":true,"technologies":[{"name":"Git","optional":false},{"name":"JAX","optional":false},{"name":"Machine Learning","optional":false},{"name":"NumPy","optional":false},{"name":"Pandas","optional":false},{"name":"Python","optional":false},{"name":"PyTorch","optional":false},{"name":"Scikit-learn","optional":false},{"name":"AWS","optional":true},{"name":"Docker","optional":true},{"name":"ETL/ELT","optional":true},{"name":"GCP","optional":true},{"name":"Kubernetes","optional":true}],"status":"live","first_seen_at":"2026-04-13T15:11:37Z","employer_posted_date":"2026-04-30","last_verified_at":"2026-10-02T12:30:05Z","board_verified":true,"closed_at":null,"days_open":172,"trust":{"level":"stale","repost_count":0,"flags":["stale"],"days_open":171},"description":"Manifold Bio is a platform biotechnology company pioneering AI-guided protein design and massively multiplexed in vivo screening to unlock tissue-targeted medicines and organism-scale models of living systems. Using proprietary molecular barcoding technology, we screen hundreds of thousands of protein designs simultaneously in living systems, producing in vivo-validated datasets at a scale no one else can match. The datasets power our computational models, which leads to better drug designs, creating a flywheel that gets stronger with every campaign. Our team of protein engineers, biologists, and computational scientists works across this full stack to pursue programs both internally and with leading pharma companies.\nPosition\nManifold Bio is seeking a talented Machine Learning Research Engineer to join our growing AI team. You will work closely with our research scientists to implement, scale, and optimize machine learning systems that power our de novo antibody design platform and advance our protein design capabilities. Your efforts will contribute to building production-ready ML infrastructure that enables breakthrough discoveries in protein therapeutics. You will be expected to take ownership of engineering challenges in our ML pipeline, from data processing and model training to deployment and monitoring, while collaborating closely with our research team to translate cutting-edge ideas into robust, scalable systems.\nThis is an on-site role and can be based in either Boston, Massachusetts or San Francisco, California. Please only apply if you reside in these cities or are open to relocate. \nResponsibilities\nImplement and optimize machine learning models for protein design\nBuild and maintain scalable data processing pipelines for large-scale protein and molecular datasets\nDevelop and deploy ML infrastructure for distributed training and inference across GPU clusters\nCollaborate with research scientists to translate experimental ML approaches into production-ready code\nDesign and execute ML experiments with clear hypotheses and rigorous analysis\nOptimize model performance and computational efficiency for large-scale protein design tasks\nBuild tools and utilities to support rapid prototyping and experimentation by the research team\nRequired Qualifications\nBachelor's or Master's degree in Computer Science, Machine Learning, Computational Biology, or related field\n2+ years of hands-on experience with PyTorch and/or JAX for deep learning applications\nStrong proficiency in Python scientific computing stack (NumPy, Pandas, scikit-learn)\nExperience with distributed computing and GPU optimization techniques\nFamiliarity with protein structure analysis, computational biology, or analogous problems in natural sciences\nUnderstanding of modern deep learning architectures and optimization techniques\nExperience implementing research papers or translating ML approaches to production systems\nProficiency with version control (Git), testing frameworks, and software engineering best practices\nStrong problem-solving skills and ability to work independently on technical challenges\nExcellent written and verbal communication skills for cross-functional collaboration\nPreferred Qualifications\nExperience training LLMs or diffusion generative models\nKnowledge of cloud computing platforms (AWS, GCP) and containerization (Docker, Kubernetes)\nBackground in computational biology, bioinformatics, or structural biology\nExperience with large-scale data engineering and ETL pipelines\nFamiliarity with MLOps practices and model deployment frameworks\nThis Role Might Be Perfect For You If\nYou are passionate about leveraging state of the art machine learning approaches to solve challenging disease areas\nYou enjoy translating research ideas into high impact, productionized, scalable code\nYou have rich AI/ML experience and are looking to pivot into biotech\nIf you're excited to build scalable ML systems that revolutionize protein therapeutic discovery, please reach out to .\nBase Salary Range: $140,000-225,000\nThis reflects the typical offer range for this role, based on experience, role scope, and internal equity. Final compensation decisions are made using a consistent leveling framework and consider the candidate’s experience, interview performance, and expected impact.\nThis role is eligible for:\nAnnual performance-based target bonus\nStock options\nComprehensive medical, dental, and vision coverage\n401(k) plan\nFlexible paid time off and holidays\nPerks including on-site gym, onsite lunch, and commuter support\nOur compensation ranges are reviewed annually to ensure alignment with market trends and internal equity.\nWe value different experiences and ways of thinking and believe the most talented teams are built by bringing together people of diverse cultures, genders, and backgrounds.","description_format":"text","description_chars":4852,"description_truncated":false,"requirements":{"experience_years_min":2,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":{"level":"bachelor","optional":false},"security_clearance":false,"languages":[]},"benefits":["Equity","Stock options"],"hiring_locations":[],"hiring_excludes":[],"relocation_offered":true,"industries":["Artificial Intelligence","Biologics & Biosimilars","Recommendation Systems"],"lifecycle":[{"event":"open","at":"2026-09-25T20:34:59Z"}],"liveness":{"score":3,"band":"cold","label":"Long shot","p_open":1,"p_active":0.115,"p_room":0.28,"age_days":171,"expected_fill_days":19,"reasons":["conf:17","win:tail","crowd:junior"],"computed_at":"2026-10-02T05:45:00Z"},"pay":{"stated_usd_annual":225000,"is_top_pay":false},"html_url":"https://alion.io/job/manifold-bio-aiml-research-engineer","json_url":"https://alion.io/job/manifold-bio-aiml-research-engineer.json","meta":{"generated_at":"2026-10-03T00:51:17Z","cache_seconds":300,"methodology":"https://alion.io/methodology","terms":"https://alion.io/terms","contact":"https://alion.io/contact","api":"https://alion.io/developers","usage":{"tier":"crawler","counted_by":"address","units_charged":1,"used_today":582,"day_limit":5000,"remaining_today":4418,"minute_limit":60,"resets_at":"2026-10-04T00:00:00Z"}}}