Machine Learning Engineer

About the Role

Takt Engineering is recruiting a Machine Learning Engineer for a client in a large-scale, project-based manufacturing. This role you will work with teams to turn design data and historical builds into usable planning outputs and product part suggestions, then own the evaluation loop that makes those outputs trustworthy enough to route for approval. The modeling work extends beyond large language models.

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

• Partner directly with planners and manufacturing engineers to understand how build artifacts are produced today, and define what a correct output looks like before building.
• Design and ship models and pipelines that produce planning outputs from specifications and comparable prior builds.
• Build the evaluation harnesses, error taxonomies, and regression checks that measure output quality against expert judgment, and drive accuracy up through systematic error analysis.
• Structure and normalize engineering and production data across PLM, ERP, and design systems, including part resolution and comparability across prior builds.
• Deliver outputs into human review and approval workflows with traceability, so that they can be verified and corrected before use.
• 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 systems in production. Software engineering background is valued, but the ML stack is a different one and we are looking for hands-on modeling experience.
• Strong Python, along with C++ and SQL, and regular hands-on use of Pandas, Scikit-learn, XGBoost, and LightGBM.
• Proficiency with at least one deep learning framework (PyTorch, TensorFlow, or Keras), including models you have trained and tuned yourself, paired with a strong understanding of ensemble methods such as bagging, boosting, stacking, and blending.
• 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.
• Rigorous evaluation practice, with a track record of building evaluation harnesses and using structured error analysis to improve system accuracy over successive iterations.
• 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.
• Outstanding communication skills, a bias for action, and the ability to deliver results independently in ambiguous environments with direct stakeholder contact.

Preferred Qualifications

• Computer vision experience such as object detection, segmentation, OCR, or document layout work.
• Applied LLM work alongside classical modeling: retrieval, embeddings and similarity search, structured generation, and fine-tuning.
• Full-stack or front-end experience, for example FastAPI with React or Next.js.
• Experience deploying open-weight models in self-hosted, on-premise, or air-gapped environments, including inference serving and capacity planning. Working knowledge of common MLOps tooling (MLflow, BentoML, Triton, ONNX Runtime, Airflow, or comparable) is useful.
• Familiarity with enterprise engineering systems such as PLM, ERP, or MES platforms and the data structures they hold. The client runs Teamcenter and Oracle.
• Exposure to shipbuilding, aerospace, or other large-scale project-based manufacturing.
• Experience with optimization or scheduling methods (constraint programming, discrete-event simulation) applied to production planning problems.
• 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: $120.00 - $170.00 per hour

Work Location: Remote