Machine Learning Engineer - Speech & Natural Language

You will own the models that make natural language work at the tactical edge: speech recognition, natural language understanding, and the full path from candidate approach to a trained, hardened model running reliably on constrained compute. You'll evaluate architectures, build data generation tooling, train and fine-tune models for noisy environments, optimize latency and memory, mature deployment into production runtime, close the loop on evaluation, and sharpen MLOps practices.

Responsibilities

• Evaluate and Decide: Assess candidate architectures and modeling approaches against the criteria that actually matter operationally: accuracy under degraded conditions, latency, and footprint. Make the call and defend it with data, not vibes.
• Build the Data Engine: Develop synthetic data generation tooling and augmentation pipelines, and curate the data we collect from the field. Our accuracy advantage comes from data we build ourselves, and you'll own that machinery.
• Train for the Domain: Train and fine-tune models that understand how operators actually talk, in noisy environments and with degraded audio, using language no general-purpose model has seen enough of.
• Optimize Ruthlessly: Every millisecond of latency and every megabyte of memory is a design decision with operational consequences. You'll find the wins and prove them with benchmarks.
• Mature the Deployment: Turn research-grade code into something we can ship: model weights served directly through an efficient production runtime, with the experimental scaffolding stripped out.
• Close the Loop: Own how we measure success. Build evaluation that reflects whether an operator's command actually worked, not just how a model scores on a test set, and turn field failures into test cases, training data, and fixes that stick.
• Sharpen Our MLOps: Improve how we train, track, evaluate, and release models. At minimum, be relentlessly vocal about what's broken.

Requirements

• Track Record: Substantial hands-on experience training, optimizing, and deploying ML models in production systems. We're flexible on years and title, biased toward senior, and inflexible on demonstrated results.
• Speech and Language Depth: Deep, hands-on experience across both speech recognition and natural language understanding, including adapting models to a specialized domain.
• Core Skills: Deep proficiency in Python and modern deep learning frameworks, including custom training loops, data pipelines, and evaluation harnesses.
• Optimization: Demonstrated experience making models meaningfully faster or smaller, with the benchmarks to back it up.
• Productionization: Hands-on work moving models out of framework and research code into an efficient, deployable inference path.
• Education: Degree in CS, EE, Computational Linguistics, or a related technical field, or equivalent proof you can do the work.

Nice to have

• Deploying models on embedded or resource-constrained GPU hardware
• Defense, robotics, or autonomy experience
• Model lifecycle tooling: experiment tracking, model registries, automated evaluation

Benefits

• Beyond a Competitive Salary, we provide a significant equity stake, aligning your growth with ours.
• 100% Remote: This role is fully remote, open to residents across the US. We believe that talent knows no borders (well, except for the US in this case!).
• Comprehensive Health Coverage: We provide comprehensive medical, dental, and vision plans so you and your family can stay healthy and worry free.
• Retirement Planning: Plan for tomorrow with our 401(k) package, complete with a generous 6% company match.
• Generous DTO: Our generous Discretionary Time off (DTO) policy is all about helping you take the breaks you deserve and live your healthiest, most fulfilling life.
• Elite Technology Package: Enjoy an elite technology package to make your work effortless and efficient.