Principal ML Engineer

Principal Machine Learning Engineer

The work people build is worth protecting and its harder to protect than it used to be. AI agents now move at machine speed with human-level access and a small slip-up can become a very public one. We disrupt cybercriminal activity before that happens. We think fast go big and always demand more of ourselves. We work hard deliver and repeat. We grow with real determination and put success well within reach. We push each other to be better and expect to be pushed back in a community built on respect where everyone is counted.
Work Protected.

As a Principal Machine Learning Engineer you will set technical direction for Mimecasts ML capabilities including our GCI products or advanced email threat-detection products. This is a senior individual-contributor role: you will own the hardest modeling and systems problems end to end define the architecture other engineers build on and be a technical authority for product and engineering leadership when the direction is not obvious.

Our threat ML runs on Mimecasts shared AI enrichment platform the detection and extraction infrastructure serving models across the product suite. You will work at the intersection of applied ML production model serving and cross-team integration making architectural decisions that hold up under production load and customer commitments.

Mimecast is an AI-first engineering organization. You will use AI development tools including Claude Code Cursor and MCP integrations in your daily work and establish the workflows patterns and quality bar for the team. You will also design product systems using LLM and agent-based patterns.

Employees are expected to work from the office at least two days per week. This fosters collaboration communication performance and learning; drives innovation and creativity within and between teams; introduces employees to priorities beyond their immediate realm; and supports important interpersonal relationships and connections.

• Develop and own ML systems end to end from data sourcing cleaning and labeling strategy through feature engineering model development deployment and monitoring.

• Set the ML architecture across model design serving and surrounding systems optimizing accuracy latency and throughput for highly imbalanced threat-detection data.

• Set technical direction for production model serving using AWS SageMaker NVIDIA Triton Inference Server ensemble/KServe patterns hardened container images and integration with enrichment and gateway layers.

• Benchmark and prototype alternatives to de-risk major decisions then give leadership defensible technical recommendations.

• Establish reproducible ML standards including versioned datasets region-partitioned data and shared experimentation workflows.

• Make model observability and efficacy measurement first-class concerns through distributed tracing threshold-independent metrics raw-payload capture and monitoring for real regressions.

• Own capacity planning and rollout strategy including throughput per core or GPU utilization headroom peak-load provisioning and phased regional canary or shadow deployments.

• Diagnose production incidents close the structural gaps they expose and act as a primary reviewer and mentor across the ML codebase.

• Partner with Product platform engineering and adjacent teams to shape the roadmap and communicate technical complexity and business implications through engineering and product leadership.

• Breadth across transformer architectures RNNs CNNs generalized linear models and gradient-boosted trees with the judgment to select the right approach for the problem rather than defaulting to the largest model.

• Deep Python proficiency and strong command of PyTorch Hugging Face transformers and NLP tooling plus working knowledge of ONNX Runtime quantization such as FP16 and inference acceleration.

• Experience with dense and lexical retrieval including embeddings vector indexes and approximate nearest-neighbor search BM25 TF-IDF and hybrid approaches.

• Experience working with datasets exceeding two million examples and highly imbalanced data using rigorous evaluation methods for precision and recall trade-offs threshold selection and test-set leakage prevention.

• A track record of owning production ML systems on AWS including SageMaker S3 Athena Lambda Glue Kinesis and Bedrock with Terraform IAM containers and Kubernetes-based deployment.

• Working knowledge of model-serving frameworks such as TorchServe FastAPI and NVIDIA Triton Inference Server/KServe and the trade-offs among throughput GPU efficiency flexibility and speed of iteration.

• Hands-on experience running CUDA workloads in production including driver toolkit and runtime alignment; GPU passthrough in containers; debugging GPU failures; and improving GPU utilization.

• Fluency with AI-native development tools and modern LLM application patterns including OpenAI-style chat-completion and structured tool/function-calling APIs MCP and agent frameworks.

• Demonstrated technical leadership as an individual contributor including setting direction mentoring engineers across seniority levels and communicating technical decisions and their business implications to technical and executive audiences.

• An understanding of handling sensitive data in accordance with Master Service Agreements and compliance requirements.

• A Ph.D. or Masters degree in a quantitative discipline such as computer science statistics or mathematics with substantial experience applying advanced ML to production problems; or equivalent depth demonstrated through a Bachelors degree and a longer track record. We value demonstrated technical authority over a specific year count.

The base salary range for this position is $172000 - $258000 USD plus benefits. This range represents the minimum and maximum new hire compensation for this role. The position may also be eligible for incentive plans and additional benefits in accordance with company policy and local regulations. Our salary ranges are determined by role level and location with individual compensation also dependent on factors such as qualifications experience and skills. Final offers will reflect these considerations and may vary accordingly.

Belonging at Mimecast

Cybersecurity is a community effort. Thats why were committed to building an inclusive diverse community that celebrates and welcomes everyone unless theyre a cybercriminal of course.

Were proud to be an Equal Opportunity and Affirmative Action Employer and wed encourage you to join us whatever your background. We particularly welcome applicants from traditionally underrepresented groups.

We consider everyone equally: your race age religion sexual orientation gender identity ability marital status nationality or any other protected characteristic wont affect your application.

If you require any adjustments or accommodations due to a disability or any other reason that may help you in your interview process please let us know by emailing

Due to certain obligations to our customers an offer of employment will be subject to your successful completion of applicable background checks conducted in accordance with local law.

It is unlawful in Massachusetts to require or administer a lie detector test as a condition of employment or continued employment.