Machine Learning Engineer

About the Role Takt Engineering is recruiting a Machine Learning Engineer for a client in large-scale, project-based manufacturing. In this role, you will work directly with planners and manufacturing engineers to turn design data and historical builds into proposed bills of material, work instructions, and work packages, then own the evaluation loop that makes those outputs trustworthy enough to route for approval. The domain is complex and the data is sparse, so this is a strong fit for an engineer who is systematic about error analysis and comfortable learning a technical domain deeply from the people who run it. Job Responsibilities * Engage and scope: Partner directly with the teams to understand how build artifacts are produced today, and define what a correct output looks like before building. * Build generation systems: Design and ship models and pipelines that propose bills of material, work instructions, and work packages from specifications and comparable prior builds. * Own evaluation: Build the evaluation harnesses, error taxonomies, and regression checks that measure output quality against expert judgment, and drive accuracy up through systematic error analysis. * Design for approval: Deliver outputs into human review and approval workflows with traceability, so that generated artifacts can be verified, corrected, and controlled rather than trusted blindly. * Deploy and operate: Stand up models and services in a restricted enterprise environment, with lightweight CI/CD, monitoring, and repeatable deployment. Required Qualifications * 5+ years of software engineering experience, including at least 3 years building and shipping applied machine learning or LLM-based systems in production. * Strong Python and hands-on depth across modern ML and LLM tooling, including retrieval, embeddings and similarity search, structured generation, and model fine-tuning or adaptation. * Demonstrated experience applying ML in a low-data, expert-labeled domain such as defense, aerospace, medical, legal, or industrial engineering, where correctness is defined by subject matter experts rather than by an abundant labeled dataset. * Experience working with complex structured and semi-structured enterprise data, including entity resolution, hierarchical or graph-shaped records, and messy master data. * Sound judgment about data handling and deployment constraints in restricted environments, including what can and cannot cross a system boundary. * Prototype rapidly: Use modern AI-assisted development tooling and agentic workflows to move from concept to working prototype in days rather than months. * Outstanding communication skills, a bias for action, and the ability to deliver results independently in ambiguous environments with direct stakeholder contact. Preferred Qualifications * Experience deploying open-weight models in self-hosted, on-premise, or air-gapped environments, including inference serving and capacity planning. * Familiarity with enterprise engineering systems such as PLM, ERP, or MES platforms, and the bills of material, routings, and work package structures they support. * Exposure to aerospace, shipbuilding, or other large-scale project-based manufacturing, including work breakdown structures and zone or stage based planning. * Experience working in FedRAMP, CUI, ITAR, or DoD/military-regulated environments with security-first development practices. Active or prior clearance is a plus. Job Details *Type: *Contract (1099) *Duration: *6 months *Location: *Costa Mesa, CA preferred. Remote considered for the right candidate (U.S. only). *Work Style: *High autonomy, high output Pay: $130.00 - $170.00 per hour Work Location: Remote