{"id":3962013,"url":"https://alion.io/job/biorce-machine-learning-engineer-predictive-modeling-causal-inference-interpretability-sigma-team-austin","title":"Machine Learning Engineer - Predictive Modeling, Causal Inference & Interpretability - Sigma Team - Austin","company":{"id":1980480,"name":"Biorce","domain":"biorce.com","url":"https://alion.io/company/biorce","size_band":"51-200","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":null,"truth_index":null},"role":"AI/ML","role_family":"AI/ML","seniority":null,"employment_type":"full_time","work_mode":"hybrid","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Austin, United States"],"countries":["US"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":null,"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"AI Agents","optional":false},{"name":"GCP","optional":false},{"name":"Interpretability","optional":false},{"name":"Machine Learning","optional":false},{"name":"Python","optional":false},{"name":"PyTorch","optional":false},{"name":"TensorFlow","optional":false},{"name":"Vertex AI","optional":false}],"status":"live","first_seen_at":"2026-11-09T09:57:35Z","employer_posted_date":null,"last_verified_at":"2026-11-09T09:57:35Z","board_verified":false,"closed_at":null,"days_open":0,"trust":{"level":"not_scored","repost_count":null,"flags":[],"days_open":0},"description":"Biorce is a healthtech company using AI to support drug development and improve patient outcomes. The Machine Learning Engineer will lead causal inference and predictive modeling for an AI product that estimates clinical trial success, translates predictions into expected-value estimates, and explains the factors driving outcomes. The role spans research, prototyping, production deployment, monitoring, and collaboration with a cross-functional team.\n\nResponsibilities\nOwn the full lifecycle of projects leveraging deep learning and causal inference methods, from research to prototyping to final product and monitoring in production\nWork embedded with a Product Owner, a Designer, and the Scientific Lead from day one, taking causal and predictive models from an ambiguous problem to a demoable, benchmarked product in weeks rather than quarters\nPartner with the data team to source, assemble, and extract usable signals from unstructured and semi-structured data for downstream modeling\nDesign causal inference models, from classical to deep learning-based methods, to answer counterfactual questions and distinguish what actually drives outcomes from what merely correlates with them\nBuild models that predict the likelihood of event success under uncertain, noisy, or incomplete data, translating predictions into expected-value estimates to support decision-making\nUse simulation-based or uncertainty quantification methods to gauge confidence in predictions and support robustness checks\nDevelop and maintain high-quality, scalable, and robust code in Python\nApply model interpretability techniques to validate model behavior and build stakeholder trust in predictions\nConduct rigorous experimentation and model evaluation, including causal validation and A/B or quasi-experimental testing, to ensure performance and trustworthiness\nCollaborate with product and software engineering teams to integrate causal and predictive modeling solutions into our core products\nStay current with the latest advancements in causal inference, deep learning, and applied statistics research\n\nSkills\nYou're a Machine Learning or AI Research Engineer with a genuine grounding in causal inference, not just predictive modeling, you can articulate the difference between \"what predicts the outcome\" and \"what causes it,\" and you design accordingly\nYou're comfortable turning probabilistic, uncertainty-laden predictions into expected-value estimates that a non-technical stakeholder can act on, and you don't treat interpretability as an afterthought, you build it in because the people relying on your models will immediately distrust anything that feels like a black box\nYou're a relentless self-teacher who has picked up new tools, methods, and domains without waiting for a course or a manager to hand you one\nYou're comfortable partnering with data teams to wring usable signal out of messy, unstructured data, and with scientists and researchers as collaborators rather than stakeholders\nYou measure cycle time in weeks, communicate complex AI concepts to diverse audiences, and can lead a technical project autonomously from research question to production model\n5+ years of experience in Machine Learning, Deep Learning, or AI Research\nProven proficiency in Python and ML/Deep Learning frameworks (TensorFlow, PyTorch, etc.)\nSolid, hands-on experience with causal inference methods\nExperience modeling and predicting outcomes from uncertain, noisy, or incomplete data, and translating probabilistic predictions into expected-value or decision-oriented outputs\nExperience working with data teams to extract structured signals from unstructured or messy data sources for use in modeling pipelines\nWorking knowledge of model interpretability and explainability techniques\nSolid experience architecting, developing, and deploying machine learning models, with a strong foundation in data structures, algorithms, and software engineering principles\nAbility to work autonomously, lead technical projects, and communicate complex AI concepts to diverse audiences\nPractical experience with deep causal inference approaches, Bayesian modeling, probabilistic programming, or simulation-based methods for uncertainty-aware prediction\nFamiliarity with advanced or mechanistic interpretability methods, beyond standard attribution techniques\nExposure to large language models and agentic AI systems, particularly for extracting structured signals from unstructured data or supporting causal/predictive workflows\nMaster's or Ph.D. in Computer Science, Statistics, or a related technical field with a focus on causal inference or probabilistic modeling\nContributions to research (publications in top-tier conferences) or open-source projects in causal inference, probabilistic modeling, or interpretability\nExperience with Google Cloud Platform and Vertex AI, or open-source causal ML and interpretability libraries\nExposure to clinical research, life sciences, or another regulated domain\nAdditional languages, we're an international team\n\nBenefits\nComprehensive private health coverage to ensure your physical and mental well-being.\nHybrid work model offering flexibility to balance your professional and personal life.\nCompany events to celebrate achievements and enjoy time together.\nGet equipped with a MacBook to enhance your productivity and work experience.\nOur office is pet-friendly!\n\nCompany Overview\nBiorce is a medtech startup that develops a clinical AI assistant to enhance clinical trials for both speed and cost efficiency. It was founded in 2024, and is headquartered in Barcelona, Catalonia, ESP, with a workforce of 201-500 employees. Its website is https://www.biorce.com.","description_format":"text","description_chars":5676,"description_truncated":false,"requirements":{"experience_years_min":null,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":null,"security_clearance":false,"languages":[]},"benefits":["Apple Macbook","Company events","Hybrid work"],"hiring_locations":[],"hiring_excludes":[],"relocation_offered":false,"industries":["Clinical Trial Software"],"lifecycle":[{"event":"open","at":"2026-11-09T12:06:53Z"}],"visa":[],"liveness":{"score":86,"band":"hot","label":"Hiring now","p_open":1,"p_active":0.86,"p_room":1,"age_days":0,"expected_fill_days":30,"reasons":["seen:0","win:early"],"computed_at":"2026-11-09T14:08:37Z"},"pay":null,"html_url":"https://alion.io/job/biorce-machine-learning-engineer-predictive-modeling-causal-inference-interpretability-sigma-team-austin","json_url":"https://alion.io/job/biorce-machine-learning-engineer-predictive-modeling-causal-inference-interpretability-sigma-team-austin.json","meta":{"generated_at":"2026-11-09T14:08:37Z","cache_seconds":300,"methodology":"https://alion.io/methodology","terms":"https://alion.io/terms","contact":"https://alion.io/contact","api":"https://alion.io/developers","about":"Alion is a live layer of people, companies and AI agents: who they are, whether they are real and active right now, what they do and how to work with them, readable by people and by agents and paid per call.","catalog":"https://alion.io/catalog.json","usage":{"tier":"crawler_verified","counted_by":"address","units_charged":1,"used_today":19073,"day_limit":null,"remaining_today":null,"minute_limit":300,"resets_at":"2026-11-10T00:00:00Z"}},"offers":[{"id":"company.slices","title":"One company in depth, by slice","status":"live","price":{"credits":0.1,"usd":0.01,"plus_per_slice":{"credits":0.05,"usd":0.005}},"unit":"per company, plus each slice with data","note":"the employer in depth","call":{"mcp_tool":"get_company","arguments":{"id":1980480},"rest":"https://alion.io/mcp/rest/get_company?id=1980480"},"human":"https://alion.io/catalog?offer=company.slices&for=job%2Fbiorce-machine-learning-engineer-predictive-modeling-causal-inference-interpretability-sigma-team-austin"},{"id":"market.stats","title":"A market slice: pay, demand and time to fill","status":"live","price":{"credits":2,"usd":0.2},"unit":"per slice","note":"pay, demand and time to fill for this role and place","call":{"mcp_tool":"market_stats"},"human":"https://alion.io/catalog?offer=market.stats&for=job%2Fbiorce-machine-learning-engineer-predictive-modeling-causal-inference-interpretability-sigma-team-austin"},{"id":"job.search","title":"Open jobs by role, technology, place, pay and visa","status":"live","price":{"credits":0.1,"usd":0.01},"unit":"per posting in a list","note":"similar open postings","call":{"mcp_tool":"search_jobs"},"human":"https://alion.io/catalog?offer=job.search&for=job%2Fbiorce-machine-learning-engineer-predictive-modeling-causal-inference-interpretability-sigma-team-austin"},{"id":"company.verify","title":"Is this company real and active right now","status":"pilot","price":null,"unit":"per company","request":{"url":"https://alion.io/catalog/request","method":"POST","body":"{\"offer\": \"company.verify\", \"for\": \"job/biorce-machine-learning-engineer-predictive-modeling-causal-inference-interpretability-sigma-team-austin\", \"note\": \"what you need it for\"}"},"human":"https://alion.io/catalog?offer=company.verify&for=job%2Fbiorce-machine-learning-engineer-predictive-modeling-causal-inference-interpretability-sigma-team-austin"}],"agent":{"mcp":"https://alion.io/mcp/jobs","mcp_open":"https://alion.io/mcp/open","next":[{"tool":"get_job","arguments":{"id":3962013}},{"tool":"search_jobs","arguments":{"company":"biorce.com","detail":"compact"}},{"tool":"match_people","arguments":{"job_id":3962013},"note":"people open to offers who fit this vacancy"}],"note":"The same data through MCP tools: https://alion.io/mcp/open needs no sign-in (compact searches, postings, people); https://alion.io/mcp with an Alion account has everything. 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