Senior Machine Learning Engineer

About the role

Senior Machine Learning Engineer

Titan Advanced Energy Solutions, Inc., Salem, MA

Titan Advanced Energy Solutions, headquartered in Salem, MA, develops revolutionary, ultrasound-based battery cell inspection systems for gigafactories. Using non-destructive, high-resolution, high-speed ultrasound technology, Titan’s IonSight analyses cell morphology to detect critical manufacturing anomalies, directly addressing safety concerns and improving processes. The novel in-line technology integrates into existing cell manufacturing processes to decrease the cost of quality, accurately classify cell quality grades, and evaluate lifetime performance and safety characteristics.

Located in Salem, MA, Titan’s innovative strides have been recognized with numerous awards and funding from multi-national corporate investment groups, venture capital and top clean energy programs and institutions, including Greentown Labs, the Massachusetts Clean Energy Center (MassCEC) and the Department of Energy. Growing and poised to continue their positive momentum, this is an exciting time to join the Titan team!

We are looking for a Senior Machine Learning Engineer to join our innovative and dynamic team. Our mission is to propel the shift toward world electrification and decarbonization by scaling battery manufacturing technology more efficiently.

Summary

The Senior Machine Learning Engineer is a crucial decision-maker on the data science approach at the core of our ultrasound inspection product: a scalable, state-of-the-art method for detecting defective battery cells. You will design an anomaly-detection system that holds up across the environments we operate in, from gigafactory-scale production to inbound inspection to our internal lab, with the model, evaluation, annotation, and deployment built to scale and stay traceable. This is a hands-on role for a senior engineer who is as comfortable training models as owning the pipelines, containers, and services that put them into production. You will decide how the pieces fit together as data volumes and customer sites grow, and keep the results grounded in the physics of the cell.

What you’ll do

Design, build, and iterate the anomaly-detection approach that flags abnormal cell scans, and keep it current with the state of the art.

Think structurally about running it at production scale: throughput, traceability, model versioning, reproducible and standardized evaluation, and drift as new sites and datasets come online.

Own the ML pipeline end to end as production software: containerized training and inference, orchestration, CI/CD for models, experiment tracking and model registry (we use MLflow), and lineage across data, code, and artifacts.

Build out Titan’s Battery Quality Library, including a scalable annotation workflow and extraction of multi-modal data (electrical, teardown, CT) for training and validation.

Define quantifiable anomaly metrics fit for production use and grounded in the physics of the cell, such as its compositional structure and how ultrasound propagates through it.

Package models to run wherever the product runs, in the cloud and on-prem at the edge, and monitor them once they are out there.

Support new and existing pilots across Europe, Asia, and North America, informing implementation strategy with what works for gigafactory inbound inspection.

Document methods, key algorithms, and their evaluation clearly, and prepare the material that drives fast, evidence-based stakeholder decisions.

Set technical direction for the ML work, raise the bar through code review, and mentor engineers as the team grows.

Partner with Product, Battery Science, Firmware, Software, and Data Science teams to advance and improve existing software and develop dependable production capability. Software Engineering owns the platform, edge runtime, and application; you own the models and the pipelines that produce and serve them.

Required skills

Strong applied machine learning background designing and training deep learning models (PyTorch, Tensorflow, or JAX).

Built generative anomaly detection models, such as diffusion models, and familiar in detail with their training regime and tuning of the noise/reconstruction schedule.

Familiarity with a range of approaches to AD, such as prototype-based methods, localization, segmentation, and working with anomaly maps, and the judgment to select among them rather than defaulting to one.

Owned model evaluation end-to-end: dataset construction, imbalance-aware metrics, and calibrated operating points.

Owned reproducible ML pipelines with lineage across artifacts, best practices for reproducible experiments.

Strong software engineering fundamentals in Python: testing, packaging, code review, and building services other teams depend on.

Docker/container workflows and CI/CD ownership for training and inference.

Production MLOps: remote model deployments, drift/regression monitoring, and the tooling to catch problems before customers do. We run training and inference on AWS, including SageMaker.

Experiment tracking and a model registry used in earnest, such as MLflow: runs, metrics, artifacts, and a clear path for promoting a model to production.

Self-sufficient across a modern micro-service stack, with production AWS experience.

Senior enough to make the call on approach, defend it with evidence, and carry it through to something running in production (typically 7+ years of relevant experience).

Nice to have

ML in a manufacturing or inline-inspection setting, ideally at gigafactory or high-volume scale.

Working with multi-modal data (imaging, electrical, CT, teardown) and building annotation frameworks.

Optimizing models for constrained or on-prem hardware (quantization, distillation, ONNX/TensorRT).

Domain background in batteries, ultrasound, or non-destructive testing.

Startup experience, and comfort in a small team where scope is broad and priorities move.

Personal Values

Curious and driven to figure things out, especially when the data is messy and the answer is not obvious.

Bias to action, self-motivated and entrepreneurial spirit

Attention to detail, effective time management, and pride in work

Dependable, trustworthy, empathetic & full of integrity

Strong collaborative communication skills; able to build consensus internally and externally