Machine Learning Engineer Hybrid Engineering Full time White Plains, New York, United States Description Who We Are and Why Join Us OnMed our purpose is simple but powerful...to improve the quality of life and sense of well-being in our communities by bringing access to healthcare to everyone, everywhere. Our path to everywhere has already begun, with our innovative OnMed CareStation™, a small but mighty, Clinic-in-a-Box, bringing #healthcareaccess anywhere with an outlet to plug it in. Poised to become a key component in America’s public health infrastructure, the CareStation is the only tech-enabled, human-led, hybrid care solution that combines the comprehensive experience, trust and outcomes of a clinic, with the rapid scalability of virtual care. At OnMed, every role, every day, is directly impacting the communities we serve. You’ll join a high-performing purpose-driven team, innovating to break down the barriers that keep people from the care they need. This is not just a job...it's a movement to bring access to healthcare where and when people need it most. It’s healthcare that shows up Who You Are You are a Data Scientist / ML Engineer who bridges the gap between rigorous statistical science and production-grade engineering. You design and validate models, run controlled experiments, and ship reliable, maintainable ML systems—working side by side with a software development team in a shared toolchain. You leverage AI-aided development practices to responsibly speed development, while applying your expertise in understanding the sensitivity of specific models and the need for clinical accuracy. You are skilled in proper validation techniques and identifying potential biases or weaknesses in underlying data, particularly as models are developed to support clinical diagnoses. You take full ownership of your work—from hypothesis through deployment. This is not a role where you hand off notebooks to an engineering team; you see your work through end-to-end. Requirements Role’s Responsibilities - Design and execute experiments with proper controls, including variable isolation, hypothesis testing, and statistical power analysis. - Build, validate, and monitor machine learning models using sound. statistical methodology (cross-validation, confidence intervals, residual analysis, distributional checks, etc.) - Write production-quality Python code, structured for maintainability, testability, and peer review. - Develop and run ML workloads in distributed compute environments (Spark / PySpark), including feature engineering, large-scale data processing, and model training pipelines. - Comfortable explaining the tradeoffs of AI-assisted development in the data science / ML/AI development space, and comfortable leveraging AI to speed model development. - Collaborate within a standard SDLC: branching strategies, pull requests, code review, and CI/CD pipeline participation. - Partner closely with data engineers, software engineers, and analysts — contributing to shared codebases and following team conventions. - Document work clearly: experiment design docs, model cards, and pipeline documentation. Knowledge, Skills & Abilities - Strong proficiency in Python for ML and data workflows. - Experience with a deep learning or ML framework — PyTorch, TensorFlow, or similar. - Demonstrated ability to design rigorous experiments and statistically validate model results (not just optimize a metric). - Hands-on experience with Apache Spark / PySpark for large-scale data processing. - Comfortable working in Git — branching, PRs, conflict resolution, and code review as standard practice. - Familiarity with CI/CD concepts and participation in automated build/test/deploy pipelines. - Ability to communicate technical findings clearly to both technical and non-technical stakeholders. - Nice to haves: - Exposure to containerization (Docker) and/or orchestration basics. - Experience ina regulated or data-sensitive industry (healthcare, finance, etc.) - Background in feature stores, data versioning, or ML platform tooling. Education & Experience - Bachelor's or Master’s degree in Computer Science, Data Science, Statistics, Mathematics, or a related technical field. - 5–7 years of hands-on experience in a data science or ML engineering role. - Experience working in a cloud-based data platform (Databricks, AWS SageMaker, Azure ML, or similar). Benefits The base salary range for this role is $170,000 commensurate with the candidate's experience. OnMed is a proud equal opportunity employer. All qualified applicants will be considered without regard to race, color, creed, religion, gender, gender identity or expression, sexual orientation, national origin, genetic information, disability, age, marital status, veteran status, or any other category protected by law.

Build, Deploy, and Maintain AI for an Unpredictable WorldStriveworks helps organizations harness the power of artificial intelligence to solve real-world national security and business challenges by serving as the command center between data, models, and business outcomes. Founded by data scientists and engineers, Striveworks set out to make the journey from deployment to ongoing optimization simple and effective. With Striveworks, organizations aren’t just deploying AI—they’re building systems that remain reliable, adaptable, and ready to scale in an unpredictable world. Mission-critical operations require models that perform where they’re deployed, scale as workloads grow, and adapt rapidly as AI capabilities advance. Striveworks meets these demands—increasing reliability and performance while lowering costs and enabling confident, data-driven decision-making in dynamic environments. The RoleAs a Senior Machine Learning Engineer at Striveworks, you’ll be challenged—and trusted—on day one to be a core contributor to both the customer-driven projects and the enduring products of the company. You will be a key Striveworks representative and technology builder on projects and solutions that leverage Chariot, our proprietary AIOps platform, and you will inform and contribute to future capabilities of that platform. You will inform, envision, and help extend Striveworks’ core software products. You will work alongside data scientists, software engineers, and DevOps engineers to transform machine learning models into operational capabilities. You’re right for this opportunity if you value and possess technical expertise and enjoy pushing the boundaries of your own capabilities. You’re outcome-driven and are passionate about applying both software engineering and data science to solve real-world problems. You know that building customer-centric solutions, communicating clearly, and capturing repeatable value into productized capabilities are all important. Your day-to-day will include: - Working with customers, engineers, and other stakeholders to define clear requirements that solve customers’ problems and leverage the capabilities of our AI operations platform - Developing machine learning models and custom analytics that are applied to image, video, text, geospatial, time series, and structured data - Orchestrating and automating complex data engineering and analytic pipelines - Envisioning, specifying, and, at times, designing and implementing core product functionality - Opportunities to conduct mission-critical field work This position offers a fully remote work environment, or you can work hybrid/on site at our office in northwest Austin, TX, with up to 25% travel. The Right FitIn addition to the specific skills and expertise detailed below, we are looking for individuals who share our values. Sharing a set of values allows us to move at the speed of trust. We value a high-trust work environment where people respect each other and use candor kindly and constructively. We value work that intersects passion and perseverance, we geek out about the potential of our contributions, and we find joy in working hard on things that matter. Finally, we value taking ownership, having agency, and feeling individual responsibility for collective results. Here’s what we’re looking for: - BS degree in computer science, machine learning, or a related discipline and 6+ years relevant experience - Demonstrated experience delivering data-centric systems (e.g., data engineering, data cleaning, ETL pipelines, machine learning, and other production analytics) - Proficiency in programming languages and libraries common to machine learning; excellence in Python is essential, as is knowledge of libraries like TensorFlow, PyTorch, and/or scikit-learn - Proficiency in software engineering fundamentals to include algorithms, data structures, design patterns, and at least one systems programming language (e.g., Go, Rust, C++, Java, Scala, etc.) - Proficiency with modern software engineering tools and processes (Agile, version control, issue tracking, CI/CD, debugging, etc.) - Eligibility and willingness to obtain and maintain a Secret (or above) US security clearance - Due to the nature of this role, candidates must have US citizenship The Wish ListWe’re very interested in candidates who possess the above qualifications, and we appreciate and consider the addition of: - Advanced degree in data science, machine learning, computer science, or a related discipline - Knowledge of relevant architectures and design patterns for client-server systems (e.g., asynchronous programming, REST, GraphQL, React, Vue, Angular) - Experience implementing and deploying software into containerized or cloud environments (e.g., Docker, Kubernetes [k8s], infrastructure as code, major cloud architectures) - Experience with a variety of unstructured data types (e.g., imagery, full motion video, text, acoustic, sonar, RF, telemetry signals) - Experience building agentic systems, agentic workflows, or AI agents - Experience defining, scoping, planning, and delivering complex technical solutions - Experience leading a small team - Experience delivering technology solutions in secure government environments - Active Secret (or above) US security clearance The anticipated base pay range for this position is $160,000–$200,000/year. Striveworks’ total compensation package includes a competitive base salary, equity grants, and cash bonuses. The Benefits - Medical/dental/vision insurance - Voluntary life, long-term disability, accident, and hospital indemnity insurance - HSA and FSA (including dependent care FSA) plans - 401(k) plan - Unlimited PTO - Paid parental leave Check us out on Built In! Striveworks is an Equal Opportunity Employer and does not discriminate in employment on the basis of race, color, religion, belief, sex (including pregnancy and gender identity or expression), national origin, social or ethnic origin, political affiliation, sexual orientation, marital status, disability, genetic information, age, membership in an employee organization, retaliation, parental status, military service, or other non-merit factors. Striveworks will not tolerate discrimination or harassment of any kind. If you require assistance or a reasonable accommodation in the application process, please contact Operations at hr@striveworks.us. In compliance with federal law, all persons hired will be required to verify identity and eligibility to work in the United States and to complete an employment eligibility verification form upon hire. Striveworks is a participating employer in the E-Verify program.

Role Description We are sharing a specialised part-time consulting opportunity for experienced Coaches and Scouts with strong professional expertise in athletic instruction, player development, performance evaluation, talent identification, and real-world educational services sector workflows. This role supports a research project for one of the world's top AI companies and focuses on using real-world occupational expertise to design high-quality questions related to the day-to-day work of Coaches and Scouts. Selected professionals will apply their domain knowledge to help shape structured project content grounded in real professional practice, contributing occupation-specific expertise that supports advanced AI training and evaluation work. Key Responsibilities - Occupational Expertise & Research Input - Use professional experience as a Coach or Scout to support a research project for a leading AI company - Contribute domain knowledge grounded in real-world full-time work experience - Help ensure project outputs reflect authentic occupational practice and professional standards - Question Design & Content Development - Design questions related to the occupation of Coaches and Scouts - Translate day-to-day professional knowledge into structured, high-quality research content - Contribute occupation-specific material that supports AI training and evaluation initiatives - Project Participation & Quality Contribution - Engage consistently in a structured remote project environment - Contribute professional judgment shaped by coaching, scouting, and athletic development experience - Support high-quality output through reliable participation and strong subject-matter expertise Qualifications - 4+ years of full-time work experience as a Coach or Scout - Strong professional background in coaching, scouting, athletic development, talent evaluation, or related educational services work - Ability to contribute real-world occupational insight to structured research work - Strong written communication and analytical thinking skills - Reliable availability for consistent project participation - Current location in the US, UK, or Canada Preferred Qualifications - Experience translating professional knowledge into structured content or research inputs - Strong understanding of coaching workflows, scouting evaluation, athlete development, and performance assessment - Ability to contribute precise, profession-specific material in a consistent and high-quality way Benefits - Contribute specialised coaching and scouting expertise to a high-impact AI research project - Help shape AI systems using authentic occupational knowledge from real professional practice - Participate in a focused remote project with meaningful domain contribution - Flexible remote work with competitive compensation based on background and location Contract Details - Independent contractor role - Fully remote with flexible scheduling - Hourly compensation listed at $50–$90 per hour for U.S.-based applicants - Compensation will vary depending on professional background and geographic location - Minimum commitment of 15 hours per week - Estimated project duration of approximately 3–4 weeks after project start - The work is fully asynchronous and can be completed around your schedule - A desktop or laptop computer is required for this project - Weekly payments are made via Stripe or Wise based on services rendered - Candidates will be classified as at-will contractors to Mercor - Projects may be extended, shortened, or concluded early depending on project needs and performance - Work will not involve access to confidential or proprietary information from any employer, client, or institution - Please note: We are unable to support H1-B or STEM OPT candidates at this time - Start date: Immediate About the Platform This opportunity is available through a leading AI-driven work platform that connects domain experts with frontier AI research projects. Experts contribute to improving advanced AI systems by providing specialised expertise across real-world workflows, structured evaluation, model training support, and domain-specific professional knowledge.

• Design, develop, and deploy machine learning models and solutions, leveraging tools such as LangGraph and MLflow for orchestration and lifecycle management. • Collaborate on building and maintaining scalable data and feature pipeline infrastructure for real time and batch processing using tools like BigQuery, BigTable, Dataflow, Composer(Airflow), PubSub, and Cloud Run to support ML model training and inference. • Develop and implement robust strategies for model monitoring and observability to detect model drift, bias, and performance degradation, leveraging tools like Vertex AI Model Monitoring and custom dashboards. • Optimize ML model inference performance to improve latency and cost-efficiency of AI applications. • Ensure the overall reliability, performance, and scalability of the ML models and data infrastructure platform, including proactive identification and resolution of issues related to model performance and data quality. • Troubleshoot and resolve complex issues impacting ML models, data pipelines, and production AI system. • Ensure AI/ML models and workflows meet data governance, security, and compliance requirements, specifically for real-money gaming.